SMA-EPUandOilPrices.ipynb

{
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    "## Trading Strategy Development and Data Analysis Exercise\n",
    "\n",
    "### Objective\n",
    "You are tasked with developing a trading strategy that leverages traditional market data along with economic datasets. We're interested in how commodity price movements, particularly in the oil market, correlate with external economic factors and how we might use this relationship to inform our trading decisions.\n",
    "\n",
    "### Resources Provided\n",
    "Data Ingestion: Access to pre-sourced market and economic datasets.\n",
    "\n",
    "Data Preprocessing and Integration: Datasets are already preprocessed and integrated.\n",
    "\n",
    "Exploratory Data Analysis (EDA): Initial insights and trends from foundational EDA are provided. You're encouraged to further explore correlations and patterns, e.g. using your understanding of global events to interpret significant price movements.\n",
    "\n",
    "### Main Task: Strategy Development\n",
    "\n",
    "- Develop a trading strategy that utilizes insights from both market data and economic indicators.\n",
    "- Use the backtesting framework from [Backtesting.py Documentation](https://kernc.github.io/backtesting.py/doc/backtesting/) for strategy implementation.\n",
    "\n",
    "### Strategy Testing\n",
    "- Test your strategy's effectiveness using the provided backtesting framework, which computes various statistics e.g. cumulative returns and the Sharpe ratio.\n",
    "- Analyse the risk-adjusted performance and robustness of your strategy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required packages using a Virtual Environment\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_palette(\"deep\")\n",
    "import yfinance as yf\n",
    "\n",
    "from backtesting import Backtest, Strategy\n",
    "from backtesting.lib import crossover\n",
    "from backtesting.test import SMA\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Ingestion\n",
    "For this task, we have provided two key datasets:\n",
    "- Equity market data for tickers CL=F and BZ=F sourced from `yfinance` for a 2-year period at daily intervals.\n",
    "- Economic Policy Uncertainty (EPU) Index data from the same period, sourced from https://fred.stlouisfed.org/series/USEPUINDXD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "#### 1.1 Background research\n",
    "\n",
    "##### 1.1.1 Crude Oil\n",
    "Crude oil is a raw, unrefined petroleum product extracted from the ground. It is a key commodity and one of the most actively traded commodities in the world.\n",
    "\n",
    "\"CL\" is the commodity code or ticker symbol used to represent West Texas Intermediate (WTI) crude oil in financial markets. West Texas Intermediate (WTI) is a grade of crude oil that serves as a major benchmark in oil pricing.\n",
    "\n",
    "\"BZ\" is the commodity code or ticker symbol used to represent Brent Crude Oil in financial markets. Brent Crude is a major trading classification of crude oil that serves as a major benchmark price for purchases worldwide.\n",
    "\n",
    "The \"F\" at the end of \"CL=F\" and \"BZ=F\" signifies that this is a futures contract. Futures contracts are standardized agreements to buy or sell a specific amount of an underlying asset (in this case, crude oil) at a predetermined price on a specified future date.\n",
    "\n",
    "<br>\n",
    "\n",
    "West Texas Intermediate (WTI) and Brent Crude are two major classifications of sweet light crude oil. \"Sweet\" crude oil refers to oil that has a low sulfur content. Low sulfur levels make the oil easier to refine, resulting in fewer impurities and less pollution during the refining process. \"Light\" crude oil refers to oil that has a low density or low specific gravity. Light crude oils have a higher ratio of hydrocarbons to impurities, making them easier to process in refineries.\n",
    "* WTI is extracted primarily from wells in the United States, with a significant portion coming from the Permian Basin in West Texas. It's known for its relatively high quality, low sulfur content, and light density.\n",
    "* Brent Crude is sourced from the North Sea and consists of a blend of several crude oils from different oil fields. Brent Crude is also considered a high-quality crude oil with low sulfur content and a relatively light density.\n",
    "\n",
    "<br>\n",
    "\n",
    "##### 1.1.2 Economic Policy Uncertainty (EPU)\n",
    "\n",
    "The Economic Policy Uncertainty (EPU) is an index that attempts to quantify the degree of uncertainty surrounding economic policy decisions and their potential impact on economic conditions.\n",
    "\n",
    "The Economic Policy Uncertainty Index is often based on the following components:\n",
    "* News Coverage: The frequency of news articles and media coverage related to economic policy. An increase in coverage may indicate a higher level of uncertainty.\n",
    "* Tax Code Provisions: Changes or uncertainty in tax policies, including potential revisions to tax codes and regulations.\n",
    "* Government Spending: Uncertainty related to government spending and budget decisions, including debates on fiscal policies.\n",
    "* Trade and Monetary Policy: The uncertainty associated with trade policy like international trade policies, tariffs, and trade agreements; or monetary policy like central bank decisions, interest rate changes, and monetary policy actions.\n",
    "* Regulatory Environment: Changes or uncertainties in regulations affecting businesses and industries.\n",
    "* Political Climate: The overall political environment, including elections and political instability.\n",
    "\n",
    "</div>"
   ]
  },
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     "text": [
      "[*********************100%%**********************]  2 of 2 completed\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">High</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>BZ=F</th>\n",
       "      <th>CL=F</th>\n",
       "      <th>BZ=F</th>\n",
       "      <th>CL=F</th>\n",
       "      <th>BZ=F</th>\n",
       "      <th>CL=F</th>\n",
       "      <th>BZ=F</th>\n",
       "      <th>CL=F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>77.940002</td>\n",
       "      <td>75.690002</td>\n",
       "      <td>79.279999</td>\n",
       "      <td>76.459999</td>\n",
       "      <td>77.040001</td>\n",
       "      <td>74.269997</td>\n",
       "      <td>78.980003</td>\n",
       "      <td>76.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>78.949997</td>\n",
       "      <td>76.019997</td>\n",
       "      <td>80.550003</td>\n",
       "      <td>77.639999</td>\n",
       "      <td>78.599998</td>\n",
       "      <td>75.699997</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>76.989998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>80.080002</td>\n",
       "      <td>77.180000</td>\n",
       "      <td>81.489998</td>\n",
       "      <td>78.580002</td>\n",
       "      <td>79.550003</td>\n",
       "      <td>76.510002</td>\n",
       "      <td>80.800003</td>\n",
       "      <td>77.849998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>80.150002</td>\n",
       "      <td>77.239998</td>\n",
       "      <td>82.839996</td>\n",
       "      <td>80.239998</td>\n",
       "      <td>79.629997</td>\n",
       "      <td>76.730003</td>\n",
       "      <td>81.989998</td>\n",
       "      <td>79.459999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>82.059998</td>\n",
       "      <td>79.620003</td>\n",
       "      <td>82.989998</td>\n",
       "      <td>80.470001</td>\n",
       "      <td>81.440002</td>\n",
       "      <td>78.519997</td>\n",
       "      <td>81.750000</td>\n",
       "      <td>78.900002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price            Open                  High                   Low             \\\n",
       "Ticker           BZ=F       CL=F       BZ=F       CL=F       BZ=F       CL=F   \n",
       "Date                                                                           \n",
       "2022-01-03  77.940002  75.690002  79.279999  76.459999  77.040001  74.269997   \n",
       "2022-01-04  78.949997  76.019997  80.550003  77.639999  78.599998  75.699997   \n",
       "2022-01-05  80.080002  77.180000  81.489998  78.580002  79.550003  76.510002   \n",
       "2022-01-06  80.150002  77.239998  82.839996  80.239998  79.629997  76.730003   \n",
       "2022-01-07  82.059998  79.620003  82.989998  80.470001  81.440002  78.519997   \n",
       "\n",
       "Price           Close             \n",
       "Ticker           BZ=F       CL=F  \n",
       "Date                              \n",
       "2022-01-03  78.980003  76.080002  \n",
       "2022-01-04  80.000000  76.989998  \n",
       "2022-01-05  80.800003  77.849998  \n",
       "2022-01-06  81.989998  79.459999  \n",
       "2022-01-07  81.750000  78.900002  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Equity market data for Crude Oil and Brent Crude Oil\n",
    "tickers = ['CL=F', 'BZ=F']\n",
    "\n",
    "# Downloading 'Open', 'High', 'Low', and 'Close' for each ticker\n",
    "market_data = yf.download(tickers, start=datetime(2022, 1, 1), end=datetime(2024, 1, 1))[[\"Open\", \"High\", \"Low\", \"Close\"]]\n",
    "market_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USEPUINDXD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>169.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>157.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>146.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>192.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>111.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            USEPUINDXD\n",
       "DATE                  \n",
       "2022-01-01      169.59\n",
       "2022-01-02      157.01\n",
       "2022-01-03      146.60\n",
       "2022-01-04      192.08\n",
       "2022-01-05      111.08"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EPU index data\n",
    "epu_data = pd.read_csv('datasets/EPU_index.csv', parse_dates=['DATE'], index_col='DATE')\n",
    "epu_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 2. Data Preprocessing and Integration [Provided]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>USEPUINDXD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>75.690002</td>\n",
       "      <td>76.459999</td>\n",
       "      <td>74.269997</td>\n",
       "      <td>76.080002</td>\n",
       "      <td>146.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>76.019997</td>\n",
       "      <td>77.639999</td>\n",
       "      <td>75.699997</td>\n",
       "      <td>76.989998</td>\n",
       "      <td>192.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>77.180000</td>\n",
       "      <td>78.580002</td>\n",
       "      <td>76.510002</td>\n",
       "      <td>77.849998</td>\n",
       "      <td>111.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>77.239998</td>\n",
       "      <td>80.239998</td>\n",
       "      <td>76.730003</td>\n",
       "      <td>79.459999</td>\n",
       "      <td>235.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>79.620003</td>\n",
       "      <td>80.470001</td>\n",
       "      <td>78.519997</td>\n",
       "      <td>78.900002</td>\n",
       "      <td>57.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  USEPUINDXD\n",
       "Date                                                              \n",
       "2022-01-03  75.690002  76.459999  74.269997  76.080002      146.60\n",
       "2022-01-04  76.019997  77.639999  75.699997  76.989998      192.08\n",
       "2022-01-05  77.180000  78.580002  76.510002  77.849998      111.08\n",
       "2022-01-06  77.239998  80.239998  76.730003  79.459999      235.10\n",
       "2022-01-07  79.620003  80.470001  78.519997  78.900002       57.41"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>USEPUINDXD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>77.940002</td>\n",
       "      <td>79.279999</td>\n",
       "      <td>77.040001</td>\n",
       "      <td>78.980003</td>\n",
       "      <td>146.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>78.949997</td>\n",
       "      <td>80.550003</td>\n",
       "      <td>78.599998</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>192.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>80.080002</td>\n",
       "      <td>81.489998</td>\n",
       "      <td>79.550003</td>\n",
       "      <td>80.800003</td>\n",
       "      <td>111.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>80.150002</td>\n",
       "      <td>82.839996</td>\n",
       "      <td>79.629997</td>\n",
       "      <td>81.989998</td>\n",
       "      <td>235.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>82.059998</td>\n",
       "      <td>82.989998</td>\n",
       "      <td>81.440002</td>\n",
       "      <td>81.750000</td>\n",
       "      <td>57.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  USEPUINDXD\n",
       "Date                                                              \n",
       "2022-01-03  77.940002  79.279999  77.040001  78.980003      146.60\n",
       "2022-01-04  78.949997  80.550003  78.599998  80.000000      192.08\n",
       "2022-01-05  80.080002  81.489998  79.550003  80.800003      111.08\n",
       "2022-01-06  80.150002  82.839996  79.629997  81.989998      235.10\n",
       "2022-01-07  82.059998  82.989998  81.440002  81.750000       57.41"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split market data into two separate datasets and join each with EPU index data\n",
    "crude_epu_data = market_data.xs('CL=F', level=1, axis=1).join(epu_data)\n",
    "brent_epu_data = market_data.xs('BZ=F', level=1, axis=1).join(epu_data)\n",
    "crude_epu_data.head()\n",
    "brent_epu_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Exploratory Data Analysis\n",
    "Identify trends or correlations between the equity price movements and the EPU index using visualisations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 Stock Close Price and EPU Index Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crude Oil Close</th>\n",
       "      <th>Brent Oil Close</th>\n",
       "      <th>EPU Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>76.080002</td>\n",
       "      <td>78.980003</td>\n",
       "      <td>146.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>76.989998</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>192.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>77.849998</td>\n",
       "      <td>80.800003</td>\n",
       "      <td>111.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>79.459999</td>\n",
       "      <td>81.989998</td>\n",
       "      <td>235.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>78.900002</td>\n",
       "      <td>81.750000</td>\n",
       "      <td>57.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Crude Oil Close  Brent Oil Close  EPU Index\n",
       "Date                                                   \n",
       "2022-01-03        76.080002        78.980003     146.60\n",
       "2022-01-04        76.989998        80.000000     192.08\n",
       "2022-01-05        77.849998        80.800003     111.08\n",
       "2022-01-06        79.459999        81.989998     235.10\n",
       "2022-01-07        78.900002        81.750000      57.41"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a DataFrame with useful data for legibility\n",
    "data = pd.DataFrame({\n",
    "    'Crude Oil Close': crude_epu_data['Close'],\n",
    "    'Brent Oil Close': brent_epu_data['Close'],\n",
    "    'EPU Index': crude_epu_data['USEPUINDXD']\n",
    "})\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.1.1 Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig, ax1 = plt.subplots(figsize=(12, 4))\n",
    "ax1.plot(data.index, data['Crude Oil Close'], label='Crude Oil Close', color='blue')\n",
    "ax1.plot(data.index, data['Brent Oil Close'], label='Brent Oil Close', color='green')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Price (USD)')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Creating a second y-axis for EPU index\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(data.index, data['EPU Index'], label='EPU Index', color='purple')\n",
    "ax2.set_ylabel('EPU Index', color='purple')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "plt.title('Crude and Brent Oil Close Price Trends and EPU Index Over Time')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 Rolling Average (30d and 90d) Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate 20-day rolling averages\n",
    "data_r30 = data.rolling(window=30).mean()\n",
    "\n",
    "# Plotting\n",
    "fig, ax1 = plt.subplots(figsize=(12, 4))\n",
    "ax1.plot(data_r30.index, data_r30['Crude Oil Close'], label='Crude Oil Close Rolling Avg (30d)', color='blue')\n",
    "ax1.plot(brent_epu_data.index, data_r30['Brent Oil Close'], label='Brent Oil Close Rolling Avg (30d)', color='green')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Price (USD)')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Creating a second y-axis for EPU index\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(data_r30.index, data_r30['EPU Index'], label='EPU Index Rolling Avg (30d)', color='purple')\n",
    "ax2.set_ylabel('EPU Index', color='purple')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "plt.title('Crude and Brent Oil Close Price Trends and EPU Index with 30-day Rolling Averages')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate 20-day rolling averages\n",
    "data_r90 = data.rolling(window=90).mean()\n",
    "\n",
    "# Plotting\n",
    "fig, ax1 = plt.subplots(figsize=(12, 4))\n",
    "ax1.plot(data_r90.index, data_r90['Crude Oil Close'], label='Crude Oil Close Rolling Avg (90d)', color='blue')\n",
    "ax1.plot(brent_epu_data.index, data_r90['Brent Oil Close'], label='Brent Oil Close Rolling Avg (90d)', color='green')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Price (USD)')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Creating a second y-axis for EPU index\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(data_r90.index, data_r90['EPU Index'], label='EPU Index Rolling Avg (90d)', color='purple')\n",
    "ax2.set_ylabel('EPU Index', color='purple')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "plt.title('Crude and Brent Oil Close Price Trends and EPU Index with 90-day Rolling Averages')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.3 Volatility Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Stock Price and EPU Index Volatility Analysis\n",
       "Standard Deviation and Mean of Percentage Daily Returns"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Std Deviation</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Crude Oil Close</th>\n",
       "      <td>0.026209</td>\n",
       "      <td>0.000225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brent Oil Close</th>\n",
       "      <td>0.025205</td>\n",
       "      <td>0.000269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPU Index</th>\n",
       "      <td>0.971477</td>\n",
       "      <td>0.222722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Std Deviation      Mean\n",
       "Crude Oil Close       0.026209  0.000225\n",
       "Brent Oil Close       0.025205  0.000269\n",
       "EPU Index             0.971477  0.222722"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate additional volatility measures\n",
    "data_std = data.pct_change().std()\n",
    "data_mean = data.pct_change().mean()\n",
    "\n",
    "# Create a DataFrame for volatility measures\n",
    "volatility_table = pd.DataFrame({\n",
    "    'Std Deviation': data_std,\n",
    "    'Mean': data_mean,\n",
    "})\n",
    "\n",
    "title = \"#### Stock Price and EPU Index Volatility Analysis\\nStandard Deviation and Mean of Percentage Daily Returns\"\n",
    "display(Markdown(title))\n",
    "display(volatility_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.4 Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot scatter plots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Scatter plot for CL vs EPU\n",
    "ax1.scatter(data['Crude Oil Close'], data['EPU Index'], color='blue', alpha=0.7)\n",
    "ax1.set_xlabel('Crude Oil Close Price')\n",
    "ax1.set_ylabel('EPU Index')\n",
    "ax1.set_title(f'Crude Oil Close vs EPU Index')\n",
    "\n",
    "# Scatter plot for BZ vs EPU\n",
    "ax2.scatter(data['Brent Oil Close'], data['EPU Index'], color='green', alpha=0.7)\n",
    "ax2.set_xlabel('Brent Oil Close Price')\n",
    "ax2.set_ylabel('EPU Index')\n",
    "ax2.set_title(f'Brent Oil Close vs EPU Index')\n",
    "\n",
    "plt.suptitle(\"Stock Price and EPU Index Scatter Plots\")\n",
    "plt.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Stock Price and EPU Index Value Correlation Heatmap"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_2ef22_row0_col0, #T_2ef22_row1_col1, #T_2ef22_row2_col2 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ef22_row0_col1, #T_2ef22_row1_col0 {\n",
       "  background-color: #b70d28;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ef22_row0_col2, #T_2ef22_row2_col0 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2ef22_row1_col2, #T_2ef22_row2_col1 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2ef22\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2ef22_level0_col0\" class=\"col_heading level0 col0\" >Crude Oil Close</th>\n",
       "      <th id=\"T_2ef22_level0_col1\" class=\"col_heading level0 col1\" >Brent Oil Close</th>\n",
       "      <th id=\"T_2ef22_level0_col2\" class=\"col_heading level0 col2\" >EPU Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2ef22_level0_row0\" class=\"row_heading level0 row0\" >Crude Oil Close</th>\n",
       "      <td id=\"T_2ef22_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_2ef22_row0_col1\" class=\"data row0 col1\" >0.99</td>\n",
       "      <td id=\"T_2ef22_row0_col2\" class=\"data row0 col2\" >0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ef22_level0_row1\" class=\"row_heading level0 row1\" >Brent Oil Close</th>\n",
       "      <td id=\"T_2ef22_row1_col0\" class=\"data row1 col0\" >0.99</td>\n",
       "      <td id=\"T_2ef22_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_2ef22_row1_col2\" class=\"data row1 col2\" >0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ef22_level0_row2\" class=\"row_heading level0 row2\" >EPU Index</th>\n",
       "      <td id=\"T_2ef22_row2_col0\" class=\"data row2 col0\" >0.02</td>\n",
       "      <td id=\"T_2ef22_row2_col1\" class=\"data row2 col1\" >0.02</td>\n",
       "      <td id=\"T_2ef22_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14f446390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = data.corr()\n",
    "\n",
    "# Display the correlation matrix as a table\n",
    "styled_table = correlation_matrix.style.background_gradient(cmap='coolwarm', axis=None).format(\"{:.2f}\")\n",
    "title = \"#### Stock Price and EPU Index Value Correlation Heatmap\"\n",
    "display(Markdown(title))\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "#### Over the past two years, the stock prices of CL=F and BZ=F at close have been highly correlated, with a **99%** value. That is to be expected since they are both oil prices, though different types.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crude oil prices and the EPU Index are measured on different scales and units. Comparing their raw values directly isn't meaningful because a specific numerical change in one doesn't have the same significance in the other.\n",
    "\n",
    "When you look at percentage changes, you're analyzing relative movements rather than absolute levels. This allows you to see if there's a relationship in the way they move—do they tend to go up and down together (positive correlation) or in opposite directions (negative correlation)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Stock Price Pct Daily Returns and EPU Index Pct Change Correlation Heatmap"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d4a50_row0_col0, #T_d4a50_row1_col1, #T_d4a50_row2_col2 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d4a50_row0_col1, #T_d4a50_row1_col0 {\n",
       "  background-color: #cb3e38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d4a50_row0_col2, #T_d4a50_row2_col0 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d4a50_row1_col2, #T_d4a50_row2_col1 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d4a50\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d4a50_level0_col0\" class=\"col_heading level0 col0\" >Crude Oil Close</th>\n",
       "      <th id=\"T_d4a50_level0_col1\" class=\"col_heading level0 col1\" >Brent Oil Close</th>\n",
       "      <th id=\"T_d4a50_level0_col2\" class=\"col_heading level0 col2\" >EPU Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d4a50_level0_row0\" class=\"row_heading level0 row0\" >Crude Oil Close</th>\n",
       "      <td id=\"T_d4a50_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_d4a50_row0_col1\" class=\"data row0 col1\" >0.94</td>\n",
       "      <td id=\"T_d4a50_row0_col2\" class=\"data row0 col2\" >-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d4a50_level0_row1\" class=\"row_heading level0 row1\" >Brent Oil Close</th>\n",
       "      <td id=\"T_d4a50_row1_col0\" class=\"data row1 col0\" >0.94</td>\n",
       "      <td id=\"T_d4a50_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_d4a50_row1_col2\" class=\"data row1 col2\" >-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d4a50_level0_row2\" class=\"row_heading level0 row2\" >EPU Index</th>\n",
       "      <td id=\"T_d4a50_row2_col0\" class=\"data row2 col0\" >-0.02</td>\n",
       "      <td id=\"T_d4a50_row2_col1\" class=\"data row2 col1\" >-0.01</td>\n",
       "      <td id=\"T_d4a50_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14f877c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = data.pct_change().corr()\n",
    "\n",
    "# Display the correlation matrix as a table\n",
    "styled_table = correlation_matrix.style.background_gradient(cmap='coolwarm', axis=None).format(\"{:.2f}\")\n",
    "title = \"#### Stock Price Pct Daily Returns and EPU Index Pct Change Correlation Heatmap\"\n",
    "display(Markdown(title))\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### (With Lags) Stock Price and EPU Index Correlation Heatmap\n",
       "Stock Price is intended as daily percentage returns, EPU Index is intended as daily percentage change in EPU Index."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row6_col12, #T_0e138_row12_col6 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row7_col10, #T_0e138_row10_col7 {\n",
       "  background-color: #4f69d9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row7_col17, #T_0e138_row17_col7 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_0e138_row7_col20, #T_0e138_row9_col11, #T_0e138_row10_col15, #T_0e138_row11_col9, #T_0e138_row15_col10, #T_0e138_row20_col7 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row9_col20, #T_0e138_row20_col9 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row10_col20, #T_0e138_row20_col10 {\n",
       "  background-color: #3e51c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row11_col12, #T_0e138_row11_col13, #T_0e138_row12_col11, #T_0e138_row13_col11 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row11_col19, #T_0e138_row19_col11 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row12_col13, #T_0e138_row13_col12 {\n",
       "  background-color: #cb3e38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row12_col18, #T_0e138_row13_col18, #T_0e138_row18_col12, #T_0e138_row18_col13 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row12_col19, #T_0e138_row13_col19, #T_0e138_row19_col12, #T_0e138_row19_col13 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row15_col16, #T_0e138_row16_col15 {\n",
       "  background-color: #cd423b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_0e138_row18_col19, #T_0e138_row19_col18 {\n",
       "  background-color: #cf453c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0e138\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0e138_level0_col0\" class=\"col_heading level0 col0\" >Crude Oil Close</th>\n",
       "      <th id=\"T_0e138_level0_col1\" class=\"col_heading level0 col1\" >Brent Oil Close</th>\n",
       "      <th id=\"T_0e138_level0_col2\" class=\"col_heading level0 col2\" >EPU Index</th>\n",
       "      <th id=\"T_0e138_level0_col3\" class=\"col_heading level0 col3\" >Crude Oil Close Lag_5</th>\n",
       "      <th id=\"T_0e138_level0_col4\" class=\"col_heading level0 col4\" >Brent Oil Close Lag_5</th>\n",
       "      <th id=\"T_0e138_level0_col5\" class=\"col_heading level0 col5\" >EPU Index Lag_5</th>\n",
       "      <th id=\"T_0e138_level0_col6\" class=\"col_heading level0 col6\" >Crude Oil Close Lag_30</th>\n",
       "      <th id=\"T_0e138_level0_col7\" class=\"col_heading level0 col7\" >Brent Oil Close Lag_30</th>\n",
       "      <th id=\"T_0e138_level0_col8\" class=\"col_heading level0 col8\" >EPU Index Lag_30</th>\n",
       "      <th id=\"T_0e138_level0_col9\" class=\"col_heading level0 col9\" >Crude Oil Close Lag_90</th>\n",
       "      <th id=\"T_0e138_level0_col10\" class=\"col_heading level0 col10\" >Brent Oil Close Lag_90</th>\n",
       "      <th id=\"T_0e138_level0_col11\" class=\"col_heading level0 col11\" >EPU Index Lag_90</th>\n",
       "      <th id=\"T_0e138_level0_col12\" class=\"col_heading level0 col12\" >Crude Oil Close Lag_180</th>\n",
       "      <th id=\"T_0e138_level0_col13\" class=\"col_heading level0 col13\" >Brent Oil Close Lag_180</th>\n",
       "      <th id=\"T_0e138_level0_col14\" class=\"col_heading level0 col14\" >EPU Index Lag_180</th>\n",
       "      <th id=\"T_0e138_level0_col15\" class=\"col_heading level0 col15\" >Crude Oil Close Lag_270</th>\n",
       "      <th id=\"T_0e138_level0_col16\" class=\"col_heading level0 col16\" >Brent Oil Close Lag_270</th>\n",
       "      <th id=\"T_0e138_level0_col17\" class=\"col_heading level0 col17\" >EPU Index Lag_270</th>\n",
       "      <th id=\"T_0e138_level0_col18\" class=\"col_heading level0 col18\" >Crude Oil Close Lag_360</th>\n",
       "      <th id=\"T_0e138_level0_col19\" class=\"col_heading level0 col19\" >Brent Oil Close Lag_360</th>\n",
       "      <th id=\"T_0e138_level0_col20\" class=\"col_heading level0 col20\" >EPU Index Lag_360</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row0\" class=\"row_heading level0 row0\" >Crude Oil Close</th>\n",
       "      <td id=\"T_0e138_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_0e138_row0_col1\" class=\"data row0 col1\" >0.94</td>\n",
       "      <td id=\"T_0e138_row0_col2\" class=\"data row0 col2\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row0_col3\" class=\"data row0 col3\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row0_col4\" class=\"data row0 col4\" >-0.09</td>\n",
       "      <td id=\"T_0e138_row0_col5\" class=\"data row0 col5\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row0_col6\" class=\"data row0 col6\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row0_col7\" class=\"data row0 col7\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row0_col8\" class=\"data row0 col8\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row0_col9\" class=\"data row0 col9\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row0_col10\" class=\"data row0 col10\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row0_col11\" class=\"data row0 col11\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row0_col12\" class=\"data row0 col12\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row0_col13\" class=\"data row0 col13\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row0_col14\" class=\"data row0 col14\" >-0.08</td>\n",
       "      <td id=\"T_0e138_row0_col15\" class=\"data row0 col15\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row0_col16\" class=\"data row0 col16\" >0.02</td>\n",
       "      <td id=\"T_0e138_row0_col17\" class=\"data row0 col17\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row0_col18\" class=\"data row0 col18\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row0_col19\" class=\"data row0 col19\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row0_col20\" class=\"data row0 col20\" >-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row1\" class=\"row_heading level0 row1\" >Brent Oil Close</th>\n",
       "      <td id=\"T_0e138_row1_col0\" class=\"data row1 col0\" >0.94</td>\n",
       "      <td id=\"T_0e138_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_0e138_row1_col2\" class=\"data row1 col2\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row1_col3\" class=\"data row1 col3\" >-0.09</td>\n",
       "      <td id=\"T_0e138_row1_col4\" class=\"data row1 col4\" >-0.11</td>\n",
       "      <td id=\"T_0e138_row1_col5\" class=\"data row1 col5\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row1_col6\" class=\"data row1 col6\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row1_col7\" class=\"data row1 col7\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row1_col8\" class=\"data row1 col8\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row1_col9\" class=\"data row1 col9\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row1_col10\" class=\"data row1 col10\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row1_col12\" class=\"data row1 col12\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row1_col13\" class=\"data row1 col13\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row1_col14\" class=\"data row1 col14\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row1_col15\" class=\"data row1 col15\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row1_col16\" class=\"data row1 col16\" >0.02</td>\n",
       "      <td id=\"T_0e138_row1_col17\" class=\"data row1 col17\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row1_col18\" class=\"data row1 col18\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row1_col19\" class=\"data row1 col19\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row1_col20\" class=\"data row1 col20\" >-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row2\" class=\"row_heading level0 row2\" >EPU Index</th>\n",
       "      <td id=\"T_0e138_row2_col0\" class=\"data row2 col0\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row2_col1\" class=\"data row2 col1\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "      <td id=\"T_0e138_row2_col3\" class=\"data row2 col3\" >0.04</td>\n",
       "      <td id=\"T_0e138_row2_col4\" class=\"data row2 col4\" >0.03</td>\n",
       "      <td id=\"T_0e138_row2_col5\" class=\"data row2 col5\" >0.04</td>\n",
       "      <td id=\"T_0e138_row2_col6\" class=\"data row2 col6\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row2_col7\" class=\"data row2 col7\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row2_col8\" class=\"data row2 col8\" >0.02</td>\n",
       "      <td id=\"T_0e138_row2_col9\" class=\"data row2 col9\" >0.07</td>\n",
       "      <td id=\"T_0e138_row2_col10\" class=\"data row2 col10\" >0.06</td>\n",
       "      <td id=\"T_0e138_row2_col11\" class=\"data row2 col11\" >0.14</td>\n",
       "      <td id=\"T_0e138_row2_col12\" class=\"data row2 col12\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row2_col13\" class=\"data row2 col13\" >0.01</td>\n",
       "      <td id=\"T_0e138_row2_col14\" class=\"data row2 col14\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row2_col15\" class=\"data row2 col15\" >0.04</td>\n",
       "      <td id=\"T_0e138_row2_col16\" class=\"data row2 col16\" >0.05</td>\n",
       "      <td id=\"T_0e138_row2_col17\" class=\"data row2 col17\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row2_col18\" class=\"data row2 col18\" >0.10</td>\n",
       "      <td id=\"T_0e138_row2_col19\" class=\"data row2 col19\" >0.02</td>\n",
       "      <td id=\"T_0e138_row2_col20\" class=\"data row2 col20\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row3\" class=\"row_heading level0 row3\" >Crude Oil Close Lag_5</th>\n",
       "      <td id=\"T_0e138_row3_col0\" class=\"data row3 col0\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row3_col1\" class=\"data row3 col1\" >-0.09</td>\n",
       "      <td id=\"T_0e138_row3_col2\" class=\"data row3 col2\" >0.04</td>\n",
       "      <td id=\"T_0e138_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "      <td id=\"T_0e138_row3_col4\" class=\"data row3 col4\" >0.94</td>\n",
       "      <td id=\"T_0e138_row3_col5\" class=\"data row3 col5\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row3_col6\" class=\"data row3 col6\" >0.03</td>\n",
       "      <td id=\"T_0e138_row3_col7\" class=\"data row3 col7\" >0.01</td>\n",
       "      <td id=\"T_0e138_row3_col8\" class=\"data row3 col8\" >0.00</td>\n",
       "      <td id=\"T_0e138_row3_col9\" class=\"data row3 col9\" >0.06</td>\n",
       "      <td id=\"T_0e138_row3_col10\" class=\"data row3 col10\" >0.00</td>\n",
       "      <td id=\"T_0e138_row3_col11\" class=\"data row3 col11\" >0.09</td>\n",
       "      <td id=\"T_0e138_row3_col12\" class=\"data row3 col12\" >0.03</td>\n",
       "      <td id=\"T_0e138_row3_col13\" class=\"data row3 col13\" >0.04</td>\n",
       "      <td id=\"T_0e138_row3_col14\" class=\"data row3 col14\" >0.04</td>\n",
       "      <td id=\"T_0e138_row3_col15\" class=\"data row3 col15\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row3_col16\" class=\"data row3 col16\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row3_col17\" class=\"data row3 col17\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row3_col18\" class=\"data row3 col18\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row3_col19\" class=\"data row3 col19\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row3_col20\" class=\"data row3 col20\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row4\" class=\"row_heading level0 row4\" >Brent Oil Close Lag_5</th>\n",
       "      <td id=\"T_0e138_row4_col0\" class=\"data row4 col0\" >-0.09</td>\n",
       "      <td id=\"T_0e138_row4_col1\" class=\"data row4 col1\" >-0.11</td>\n",
       "      <td id=\"T_0e138_row4_col2\" class=\"data row4 col2\" >0.03</td>\n",
       "      <td id=\"T_0e138_row4_col3\" class=\"data row4 col3\" >0.94</td>\n",
       "      <td id=\"T_0e138_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "      <td id=\"T_0e138_row4_col5\" class=\"data row4 col5\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row4_col6\" class=\"data row4 col6\" >0.00</td>\n",
       "      <td id=\"T_0e138_row4_col7\" class=\"data row4 col7\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row4_col8\" class=\"data row4 col8\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row4_col9\" class=\"data row4 col9\" >0.03</td>\n",
       "      <td id=\"T_0e138_row4_col10\" class=\"data row4 col10\" >0.01</td>\n",
       "      <td id=\"T_0e138_row4_col11\" class=\"data row4 col11\" >0.08</td>\n",
       "      <td id=\"T_0e138_row4_col12\" class=\"data row4 col12\" >0.05</td>\n",
       "      <td id=\"T_0e138_row4_col13\" class=\"data row4 col13\" >0.05</td>\n",
       "      <td id=\"T_0e138_row4_col14\" class=\"data row4 col14\" >0.02</td>\n",
       "      <td id=\"T_0e138_row4_col15\" class=\"data row4 col15\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row4_col16\" class=\"data row4 col16\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row4_col17\" class=\"data row4 col17\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row4_col18\" class=\"data row4 col18\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row4_col19\" class=\"data row4 col19\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row4_col20\" class=\"data row4 col20\" >-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row5\" class=\"row_heading level0 row5\" >EPU Index Lag_5</th>\n",
       "      <td id=\"T_0e138_row5_col0\" class=\"data row5 col0\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row5_col1\" class=\"data row5 col1\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row5_col2\" class=\"data row5 col2\" >0.04</td>\n",
       "      <td id=\"T_0e138_row5_col3\" class=\"data row5 col3\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row5_col4\" class=\"data row5 col4\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "      <td id=\"T_0e138_row5_col6\" class=\"data row5 col6\" >0.00</td>\n",
       "      <td id=\"T_0e138_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
       "      <td id=\"T_0e138_row5_col8\" class=\"data row5 col8\" >0.00</td>\n",
       "      <td id=\"T_0e138_row5_col9\" class=\"data row5 col9\" >0.02</td>\n",
       "      <td id=\"T_0e138_row5_col10\" class=\"data row5 col10\" >0.03</td>\n",
       "      <td id=\"T_0e138_row5_col11\" class=\"data row5 col11\" >0.03</td>\n",
       "      <td id=\"T_0e138_row5_col12\" class=\"data row5 col12\" >0.03</td>\n",
       "      <td id=\"T_0e138_row5_col13\" class=\"data row5 col13\" >0.04</td>\n",
       "      <td id=\"T_0e138_row5_col14\" class=\"data row5 col14\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row5_col15\" class=\"data row5 col15\" >0.04</td>\n",
       "      <td id=\"T_0e138_row5_col16\" class=\"data row5 col16\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row5_col17\" class=\"data row5 col17\" >-0.08</td>\n",
       "      <td id=\"T_0e138_row5_col18\" class=\"data row5 col18\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row5_col19\" class=\"data row5 col19\" >0.01</td>\n",
       "      <td id=\"T_0e138_row5_col20\" class=\"data row5 col20\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row6\" class=\"row_heading level0 row6\" >Crude Oil Close Lag_30</th>\n",
       "      <td id=\"T_0e138_row6_col0\" class=\"data row6 col0\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row6_col1\" class=\"data row6 col1\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row6_col2\" class=\"data row6 col2\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row6_col3\" class=\"data row6 col3\" >0.03</td>\n",
       "      <td id=\"T_0e138_row6_col4\" class=\"data row6 col4\" >0.00</td>\n",
       "      <td id=\"T_0e138_row6_col5\" class=\"data row6 col5\" >0.00</td>\n",
       "      <td id=\"T_0e138_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "      <td id=\"T_0e138_row6_col7\" class=\"data row6 col7\" >0.94</td>\n",
       "      <td id=\"T_0e138_row6_col8\" class=\"data row6 col8\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row6_col9\" class=\"data row6 col9\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row6_col10\" class=\"data row6 col10\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row6_col11\" class=\"data row6 col11\" >0.07</td>\n",
       "      <td id=\"T_0e138_row6_col12\" class=\"data row6 col12\" >0.08</td>\n",
       "      <td id=\"T_0e138_row6_col13\" class=\"data row6 col13\" >0.06</td>\n",
       "      <td id=\"T_0e138_row6_col14\" class=\"data row6 col14\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row6_col15\" class=\"data row6 col15\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row6_col16\" class=\"data row6 col16\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row6_col17\" class=\"data row6 col17\" >0.14</td>\n",
       "      <td id=\"T_0e138_row6_col18\" class=\"data row6 col18\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row6_col19\" class=\"data row6 col19\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row6_col20\" class=\"data row6 col20\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row7\" class=\"row_heading level0 row7\" >Brent Oil Close Lag_30</th>\n",
       "      <td id=\"T_0e138_row7_col0\" class=\"data row7 col0\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row7_col1\" class=\"data row7 col1\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row7_col2\" class=\"data row7 col2\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row7_col3\" class=\"data row7 col3\" >0.01</td>\n",
       "      <td id=\"T_0e138_row7_col4\" class=\"data row7 col4\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
       "      <td id=\"T_0e138_row7_col6\" class=\"data row7 col6\" >0.94</td>\n",
       "      <td id=\"T_0e138_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "      <td id=\"T_0e138_row7_col8\" class=\"data row7 col8\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row7_col9\" class=\"data row7 col9\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row7_col10\" class=\"data row7 col10\" >-0.08</td>\n",
       "      <td id=\"T_0e138_row7_col11\" class=\"data row7 col11\" >0.05</td>\n",
       "      <td id=\"T_0e138_row7_col12\" class=\"data row7 col12\" >0.07</td>\n",
       "      <td id=\"T_0e138_row7_col13\" class=\"data row7 col13\" >0.05</td>\n",
       "      <td id=\"T_0e138_row7_col14\" class=\"data row7 col14\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row7_col15\" class=\"data row7 col15\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row7_col16\" class=\"data row7 col16\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row7_col17\" class=\"data row7 col17\" >0.14</td>\n",
       "      <td id=\"T_0e138_row7_col18\" class=\"data row7 col18\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row7_col19\" class=\"data row7 col19\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row7_col20\" class=\"data row7 col20\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row8\" class=\"row_heading level0 row8\" >EPU Index Lag_30</th>\n",
       "      <td id=\"T_0e138_row8_col0\" class=\"data row8 col0\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row8_col1\" class=\"data row8 col1\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row8_col2\" class=\"data row8 col2\" >0.02</td>\n",
       "      <td id=\"T_0e138_row8_col3\" class=\"data row8 col3\" >0.00</td>\n",
       "      <td id=\"T_0e138_row8_col4\" class=\"data row8 col4\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row8_col5\" class=\"data row8 col5\" >0.00</td>\n",
       "      <td id=\"T_0e138_row8_col6\" class=\"data row8 col6\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row8_col7\" class=\"data row8 col7\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
       "      <td id=\"T_0e138_row8_col9\" class=\"data row8 col9\" >0.04</td>\n",
       "      <td id=\"T_0e138_row8_col10\" class=\"data row8 col10\" >0.03</td>\n",
       "      <td id=\"T_0e138_row8_col11\" class=\"data row8 col11\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row8_col12\" class=\"data row8 col12\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row8_col13\" class=\"data row8 col13\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row8_col14\" class=\"data row8 col14\" >0.00</td>\n",
       "      <td id=\"T_0e138_row8_col15\" class=\"data row8 col15\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row8_col16\" class=\"data row8 col16\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row8_col17\" class=\"data row8 col17\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row8_col18\" class=\"data row8 col18\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row8_col19\" class=\"data row8 col19\" >0.01</td>\n",
       "      <td id=\"T_0e138_row8_col20\" class=\"data row8 col20\" >0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row9\" class=\"row_heading level0 row9\" >Crude Oil Close Lag_90</th>\n",
       "      <td id=\"T_0e138_row9_col0\" class=\"data row9 col0\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row9_col1\" class=\"data row9 col1\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row9_col2\" class=\"data row9 col2\" >0.07</td>\n",
       "      <td id=\"T_0e138_row9_col3\" class=\"data row9 col3\" >0.06</td>\n",
       "      <td id=\"T_0e138_row9_col4\" class=\"data row9 col4\" >0.03</td>\n",
       "      <td id=\"T_0e138_row9_col5\" class=\"data row9 col5\" >0.02</td>\n",
       "      <td id=\"T_0e138_row9_col6\" class=\"data row9 col6\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row9_col7\" class=\"data row9 col7\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row9_col8\" class=\"data row9 col8\" >0.04</td>\n",
       "      <td id=\"T_0e138_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
       "      <td id=\"T_0e138_row9_col10\" class=\"data row9 col10\" >0.93</td>\n",
       "      <td id=\"T_0e138_row9_col11\" class=\"data row9 col11\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row9_col12\" class=\"data row9 col12\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row9_col13\" class=\"data row9 col13\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row9_col14\" class=\"data row9 col14\" >0.01</td>\n",
       "      <td id=\"T_0e138_row9_col15\" class=\"data row9 col15\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row9_col16\" class=\"data row9 col16\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row9_col17\" class=\"data row9 col17\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row9_col18\" class=\"data row9 col18\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row9_col19\" class=\"data row9 col19\" >0.01</td>\n",
       "      <td id=\"T_0e138_row9_col20\" class=\"data row9 col20\" >-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row10\" class=\"row_heading level0 row10\" >Brent Oil Close Lag_90</th>\n",
       "      <td id=\"T_0e138_row10_col0\" class=\"data row10 col0\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row10_col1\" class=\"data row10 col1\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row10_col2\" class=\"data row10 col2\" >0.06</td>\n",
       "      <td id=\"T_0e138_row10_col3\" class=\"data row10 col3\" >0.00</td>\n",
       "      <td id=\"T_0e138_row10_col4\" class=\"data row10 col4\" >0.01</td>\n",
       "      <td id=\"T_0e138_row10_col5\" class=\"data row10 col5\" >0.03</td>\n",
       "      <td id=\"T_0e138_row10_col6\" class=\"data row10 col6\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row10_col7\" class=\"data row10 col7\" >-0.08</td>\n",
       "      <td id=\"T_0e138_row10_col8\" class=\"data row10 col8\" >0.03</td>\n",
       "      <td id=\"T_0e138_row10_col9\" class=\"data row10 col9\" >0.93</td>\n",
       "      <td id=\"T_0e138_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
       "      <td id=\"T_0e138_row10_col11\" class=\"data row10 col11\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row10_col12\" class=\"data row10 col12\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row10_col13\" class=\"data row10 col13\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row10_col14\" class=\"data row10 col14\" >0.01</td>\n",
       "      <td id=\"T_0e138_row10_col15\" class=\"data row10 col15\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row10_col16\" class=\"data row10 col16\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row10_col17\" class=\"data row10 col17\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row10_col18\" class=\"data row10 col18\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row10_col19\" class=\"data row10 col19\" >0.02</td>\n",
       "      <td id=\"T_0e138_row10_col20\" class=\"data row10 col20\" >-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row11\" class=\"row_heading level0 row11\" >EPU Index Lag_90</th>\n",
       "      <td id=\"T_0e138_row11_col0\" class=\"data row11 col0\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row11_col1\" class=\"data row11 col1\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row11_col2\" class=\"data row11 col2\" >0.14</td>\n",
       "      <td id=\"T_0e138_row11_col3\" class=\"data row11 col3\" >0.09</td>\n",
       "      <td id=\"T_0e138_row11_col4\" class=\"data row11 col4\" >0.08</td>\n",
       "      <td id=\"T_0e138_row11_col5\" class=\"data row11 col5\" >0.03</td>\n",
       "      <td id=\"T_0e138_row11_col6\" class=\"data row11 col6\" >0.07</td>\n",
       "      <td id=\"T_0e138_row11_col7\" class=\"data row11 col7\" >0.05</td>\n",
       "      <td id=\"T_0e138_row11_col8\" class=\"data row11 col8\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row11_col9\" class=\"data row11 col9\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row11_col10\" class=\"data row11 col10\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
       "      <td id=\"T_0e138_row11_col12\" class=\"data row11 col12\" >0.08</td>\n",
       "      <td id=\"T_0e138_row11_col13\" class=\"data row11 col13\" >0.09</td>\n",
       "      <td id=\"T_0e138_row11_col14\" class=\"data row11 col14\" >0.01</td>\n",
       "      <td id=\"T_0e138_row11_col15\" class=\"data row11 col15\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row11_col16\" class=\"data row11 col16\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row11_col17\" class=\"data row11 col17\" >0.03</td>\n",
       "      <td id=\"T_0e138_row11_col18\" class=\"data row11 col18\" >0.06</td>\n",
       "      <td id=\"T_0e138_row11_col19\" class=\"data row11 col19\" >0.07</td>\n",
       "      <td id=\"T_0e138_row11_col20\" class=\"data row11 col20\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row12\" class=\"row_heading level0 row12\" >Crude Oil Close Lag_180</th>\n",
       "      <td id=\"T_0e138_row12_col0\" class=\"data row12 col0\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row12_col1\" class=\"data row12 col1\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row12_col2\" class=\"data row12 col2\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row12_col3\" class=\"data row12 col3\" >0.03</td>\n",
       "      <td id=\"T_0e138_row12_col4\" class=\"data row12 col4\" >0.05</td>\n",
       "      <td id=\"T_0e138_row12_col5\" class=\"data row12 col5\" >0.03</td>\n",
       "      <td id=\"T_0e138_row12_col6\" class=\"data row12 col6\" >0.08</td>\n",
       "      <td id=\"T_0e138_row12_col7\" class=\"data row12 col7\" >0.07</td>\n",
       "      <td id=\"T_0e138_row12_col8\" class=\"data row12 col8\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row12_col9\" class=\"data row12 col9\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row12_col10\" class=\"data row12 col10\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row12_col11\" class=\"data row12 col11\" >0.08</td>\n",
       "      <td id=\"T_0e138_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
       "      <td id=\"T_0e138_row12_col13\" class=\"data row12 col13\" >0.93</td>\n",
       "      <td id=\"T_0e138_row12_col14\" class=\"data row12 col14\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row12_col15\" class=\"data row12 col15\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row12_col16\" class=\"data row12 col16\" >0.00</td>\n",
       "      <td id=\"T_0e138_row12_col17\" class=\"data row12 col17\" >0.00</td>\n",
       "      <td id=\"T_0e138_row12_col18\" class=\"data row12 col18\" >-0.13</td>\n",
       "      <td id=\"T_0e138_row12_col19\" class=\"data row12 col19\" >-0.15</td>\n",
       "      <td id=\"T_0e138_row12_col20\" class=\"data row12 col20\" >0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row13\" class=\"row_heading level0 row13\" >Brent Oil Close Lag_180</th>\n",
       "      <td id=\"T_0e138_row13_col0\" class=\"data row13 col0\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row13_col1\" class=\"data row13 col1\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row13_col2\" class=\"data row13 col2\" >0.01</td>\n",
       "      <td id=\"T_0e138_row13_col3\" class=\"data row13 col3\" >0.04</td>\n",
       "      <td id=\"T_0e138_row13_col4\" class=\"data row13 col4\" >0.05</td>\n",
       "      <td id=\"T_0e138_row13_col5\" class=\"data row13 col5\" >0.04</td>\n",
       "      <td id=\"T_0e138_row13_col6\" class=\"data row13 col6\" >0.06</td>\n",
       "      <td id=\"T_0e138_row13_col7\" class=\"data row13 col7\" >0.05</td>\n",
       "      <td id=\"T_0e138_row13_col8\" class=\"data row13 col8\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row13_col9\" class=\"data row13 col9\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row13_col10\" class=\"data row13 col10\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row13_col11\" class=\"data row13 col11\" >0.09</td>\n",
       "      <td id=\"T_0e138_row13_col12\" class=\"data row13 col12\" >0.93</td>\n",
       "      <td id=\"T_0e138_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
       "      <td id=\"T_0e138_row13_col14\" class=\"data row13 col14\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row13_col15\" class=\"data row13 col15\" >0.00</td>\n",
       "      <td id=\"T_0e138_row13_col16\" class=\"data row13 col16\" >0.00</td>\n",
       "      <td id=\"T_0e138_row13_col17\" class=\"data row13 col17\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row13_col18\" class=\"data row13 col18\" >-0.13</td>\n",
       "      <td id=\"T_0e138_row13_col19\" class=\"data row13 col19\" >-0.15</td>\n",
       "      <td id=\"T_0e138_row13_col20\" class=\"data row13 col20\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row14\" class=\"row_heading level0 row14\" >EPU Index Lag_180</th>\n",
       "      <td id=\"T_0e138_row14_col0\" class=\"data row14 col0\" >-0.08</td>\n",
       "      <td id=\"T_0e138_row14_col1\" class=\"data row14 col1\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row14_col2\" class=\"data row14 col2\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row14_col3\" class=\"data row14 col3\" >0.04</td>\n",
       "      <td id=\"T_0e138_row14_col4\" class=\"data row14 col4\" >0.02</td>\n",
       "      <td id=\"T_0e138_row14_col5\" class=\"data row14 col5\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row14_col6\" class=\"data row14 col6\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row14_col7\" class=\"data row14 col7\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row14_col8\" class=\"data row14 col8\" >0.00</td>\n",
       "      <td id=\"T_0e138_row14_col9\" class=\"data row14 col9\" >0.01</td>\n",
       "      <td id=\"T_0e138_row14_col10\" class=\"data row14 col10\" >0.01</td>\n",
       "      <td id=\"T_0e138_row14_col11\" class=\"data row14 col11\" >0.01</td>\n",
       "      <td id=\"T_0e138_row14_col12\" class=\"data row14 col12\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row14_col13\" class=\"data row14 col13\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
       "      <td id=\"T_0e138_row14_col15\" class=\"data row14 col15\" >0.04</td>\n",
       "      <td id=\"T_0e138_row14_col16\" class=\"data row14 col16\" >0.02</td>\n",
       "      <td id=\"T_0e138_row14_col17\" class=\"data row14 col17\" >0.06</td>\n",
       "      <td id=\"T_0e138_row14_col18\" class=\"data row14 col18\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row14_col19\" class=\"data row14 col19\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row14_col20\" class=\"data row14 col20\" >0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row15\" class=\"row_heading level0 row15\" >Crude Oil Close Lag_270</th>\n",
       "      <td id=\"T_0e138_row15_col0\" class=\"data row15 col0\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row15_col1\" class=\"data row15 col1\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row15_col2\" class=\"data row15 col2\" >0.04</td>\n",
       "      <td id=\"T_0e138_row15_col3\" class=\"data row15 col3\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row15_col4\" class=\"data row15 col4\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row15_col5\" class=\"data row15 col5\" >0.04</td>\n",
       "      <td id=\"T_0e138_row15_col6\" class=\"data row15 col6\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row15_col7\" class=\"data row15 col7\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row15_col8\" class=\"data row15 col8\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row15_col9\" class=\"data row15 col9\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row15_col10\" class=\"data row15 col10\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row15_col11\" class=\"data row15 col11\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row15_col12\" class=\"data row15 col12\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row15_col13\" class=\"data row15 col13\" >0.00</td>\n",
       "      <td id=\"T_0e138_row15_col14\" class=\"data row15 col14\" >0.04</td>\n",
       "      <td id=\"T_0e138_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
       "      <td id=\"T_0e138_row15_col16\" class=\"data row15 col16\" >0.92</td>\n",
       "      <td id=\"T_0e138_row15_col17\" class=\"data row15 col17\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row15_col18\" class=\"data row15 col18\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row15_col19\" class=\"data row15 col19\" >0.01</td>\n",
       "      <td id=\"T_0e138_row15_col20\" class=\"data row15 col20\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row16\" class=\"row_heading level0 row16\" >Brent Oil Close Lag_270</th>\n",
       "      <td id=\"T_0e138_row16_col0\" class=\"data row16 col0\" >0.02</td>\n",
       "      <td id=\"T_0e138_row16_col1\" class=\"data row16 col1\" >0.02</td>\n",
       "      <td id=\"T_0e138_row16_col2\" class=\"data row16 col2\" >0.05</td>\n",
       "      <td id=\"T_0e138_row16_col3\" class=\"data row16 col3\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row16_col4\" class=\"data row16 col4\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row16_col5\" class=\"data row16 col5\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row16_col6\" class=\"data row16 col6\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row16_col7\" class=\"data row16 col7\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row16_col8\" class=\"data row16 col8\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row16_col9\" class=\"data row16 col9\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row16_col10\" class=\"data row16 col10\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row16_col11\" class=\"data row16 col11\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row16_col12\" class=\"data row16 col12\" >0.00</td>\n",
       "      <td id=\"T_0e138_row16_col13\" class=\"data row16 col13\" >0.00</td>\n",
       "      <td id=\"T_0e138_row16_col14\" class=\"data row16 col14\" >0.02</td>\n",
       "      <td id=\"T_0e138_row16_col15\" class=\"data row16 col15\" >0.92</td>\n",
       "      <td id=\"T_0e138_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
       "      <td id=\"T_0e138_row16_col17\" class=\"data row16 col17\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row16_col18\" class=\"data row16 col18\" >0.02</td>\n",
       "      <td id=\"T_0e138_row16_col19\" class=\"data row16 col19\" >0.01</td>\n",
       "      <td id=\"T_0e138_row16_col20\" class=\"data row16 col20\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row17\" class=\"row_heading level0 row17\" >EPU Index Lag_270</th>\n",
       "      <td id=\"T_0e138_row17_col0\" class=\"data row17 col0\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row17_col1\" class=\"data row17 col1\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row17_col2\" class=\"data row17 col2\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row17_col3\" class=\"data row17 col3\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row17_col4\" class=\"data row17 col4\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row17_col5\" class=\"data row17 col5\" >-0.08</td>\n",
       "      <td id=\"T_0e138_row17_col6\" class=\"data row17 col6\" >0.14</td>\n",
       "      <td id=\"T_0e138_row17_col7\" class=\"data row17 col7\" >0.14</td>\n",
       "      <td id=\"T_0e138_row17_col8\" class=\"data row17 col8\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row17_col9\" class=\"data row17 col9\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row17_col10\" class=\"data row17 col10\" >-0.07</td>\n",
       "      <td id=\"T_0e138_row17_col11\" class=\"data row17 col11\" >0.03</td>\n",
       "      <td id=\"T_0e138_row17_col12\" class=\"data row17 col12\" >0.00</td>\n",
       "      <td id=\"T_0e138_row17_col13\" class=\"data row17 col13\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row17_col14\" class=\"data row17 col14\" >0.06</td>\n",
       "      <td id=\"T_0e138_row17_col15\" class=\"data row17 col15\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row17_col16\" class=\"data row17 col16\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
       "      <td id=\"T_0e138_row17_col18\" class=\"data row17 col18\" >0.06</td>\n",
       "      <td id=\"T_0e138_row17_col19\" class=\"data row17 col19\" >0.04</td>\n",
       "      <td id=\"T_0e138_row17_col20\" class=\"data row17 col20\" >0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row18\" class=\"row_heading level0 row18\" >Crude Oil Close Lag_360</th>\n",
       "      <td id=\"T_0e138_row18_col0\" class=\"data row18 col0\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row18_col1\" class=\"data row18 col1\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row18_col2\" class=\"data row18 col2\" >0.10</td>\n",
       "      <td id=\"T_0e138_row18_col3\" class=\"data row18 col3\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row18_col4\" class=\"data row18 col4\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row18_col5\" class=\"data row18 col5\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row18_col6\" class=\"data row18 col6\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row18_col7\" class=\"data row18 col7\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row18_col8\" class=\"data row18 col8\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row18_col9\" class=\"data row18 col9\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row18_col10\" class=\"data row18 col10\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row18_col11\" class=\"data row18 col11\" >0.06</td>\n",
       "      <td id=\"T_0e138_row18_col12\" class=\"data row18 col12\" >-0.13</td>\n",
       "      <td id=\"T_0e138_row18_col13\" class=\"data row18 col13\" >-0.13</td>\n",
       "      <td id=\"T_0e138_row18_col14\" class=\"data row18 col14\" >-0.10</td>\n",
       "      <td id=\"T_0e138_row18_col15\" class=\"data row18 col15\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row18_col16\" class=\"data row18 col16\" >0.02</td>\n",
       "      <td id=\"T_0e138_row18_col17\" class=\"data row18 col17\" >0.06</td>\n",
       "      <td id=\"T_0e138_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
       "      <td id=\"T_0e138_row18_col19\" class=\"data row18 col19\" >0.91</td>\n",
       "      <td id=\"T_0e138_row18_col20\" class=\"data row18 col20\" >-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row19\" class=\"row_heading level0 row19\" >Brent Oil Close Lag_360</th>\n",
       "      <td id=\"T_0e138_row19_col0\" class=\"data row19 col0\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row19_col1\" class=\"data row19 col1\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row19_col2\" class=\"data row19 col2\" >0.02</td>\n",
       "      <td id=\"T_0e138_row19_col3\" class=\"data row19 col3\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row19_col4\" class=\"data row19 col4\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row19_col5\" class=\"data row19 col5\" >0.01</td>\n",
       "      <td id=\"T_0e138_row19_col6\" class=\"data row19 col6\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row19_col7\" class=\"data row19 col7\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row19_col8\" class=\"data row19 col8\" >0.01</td>\n",
       "      <td id=\"T_0e138_row19_col9\" class=\"data row19 col9\" >0.01</td>\n",
       "      <td id=\"T_0e138_row19_col10\" class=\"data row19 col10\" >0.02</td>\n",
       "      <td id=\"T_0e138_row19_col11\" class=\"data row19 col11\" >0.07</td>\n",
       "      <td id=\"T_0e138_row19_col12\" class=\"data row19 col12\" >-0.15</td>\n",
       "      <td id=\"T_0e138_row19_col13\" class=\"data row19 col13\" >-0.15</td>\n",
       "      <td id=\"T_0e138_row19_col14\" class=\"data row19 col14\" >-0.03</td>\n",
       "      <td id=\"T_0e138_row19_col15\" class=\"data row19 col15\" >0.01</td>\n",
       "      <td id=\"T_0e138_row19_col16\" class=\"data row19 col16\" >0.01</td>\n",
       "      <td id=\"T_0e138_row19_col17\" class=\"data row19 col17\" >0.04</td>\n",
       "      <td id=\"T_0e138_row19_col18\" class=\"data row19 col18\" >0.91</td>\n",
       "      <td id=\"T_0e138_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
       "      <td id=\"T_0e138_row19_col20\" class=\"data row19 col20\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0e138_level0_row20\" class=\"row_heading level0 row20\" >EPU Index Lag_360</th>\n",
       "      <td id=\"T_0e138_row20_col0\" class=\"data row20 col0\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row20_col1\" class=\"data row20 col1\" >-0.04</td>\n",
       "      <td id=\"T_0e138_row20_col2\" class=\"data row20 col2\" >-0.11</td>\n",
       "      <td id=\"T_0e138_row20_col3\" class=\"data row20 col3\" >0.00</td>\n",
       "      <td id=\"T_0e138_row20_col4\" class=\"data row20 col4\" >-0.01</td>\n",
       "      <td id=\"T_0e138_row20_col5\" class=\"data row20 col5\" >0.00</td>\n",
       "      <td id=\"T_0e138_row20_col6\" class=\"data row20 col6\" >-0.09</td>\n",
       "      <td id=\"T_0e138_row20_col7\" class=\"data row20 col7\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row20_col8\" class=\"data row20 col8\" >0.03</td>\n",
       "      <td id=\"T_0e138_row20_col9\" class=\"data row20 col9\" >-0.16</td>\n",
       "      <td id=\"T_0e138_row20_col10\" class=\"data row20 col10\" >-0.14</td>\n",
       "      <td id=\"T_0e138_row20_col11\" class=\"data row20 col11\" >-0.11</td>\n",
       "      <td id=\"T_0e138_row20_col12\" class=\"data row20 col12\" >0.05</td>\n",
       "      <td id=\"T_0e138_row20_col13\" class=\"data row20 col13\" >-0.00</td>\n",
       "      <td id=\"T_0e138_row20_col14\" class=\"data row20 col14\" >0.07</td>\n",
       "      <td id=\"T_0e138_row20_col15\" class=\"data row20 col15\" >-0.05</td>\n",
       "      <td id=\"T_0e138_row20_col16\" class=\"data row20 col16\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row20_col17\" class=\"data row20 col17\" >0.04</td>\n",
       "      <td id=\"T_0e138_row20_col18\" class=\"data row20 col18\" >-0.02</td>\n",
       "      <td id=\"T_0e138_row20_col19\" class=\"data row20 col19\" >-0.06</td>\n",
       "      <td id=\"T_0e138_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14f3a7190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating percentage changes\n",
    "data_lag = data.pct_change()\n",
    "\n",
    "# Introducing lags for both columns\n",
    "lags = [5, 30, 90, 180, 270, 360]\n",
    "for lag in lags:\n",
    "    for column in data.columns:\n",
    "        data_lag[f'{column} Lag_{lag}'] = data_lag[column].shift(lag)\n",
    "\n",
    "# Calculating the correlation matrix including the lags\n",
    "correlation_matrix = data_lag.corr()\n",
    "\n",
    "# Display the correlation matrix as a table\n",
    "styled_table = correlation_matrix.style.background_gradient(cmap='coolwarm', axis=None).format(\"{:.2f}\")\n",
    "title = \"#### (With Lags) Stock Price and EPU Index Correlation Heatmap\\nStock Price is intended as daily percentage returns, EPU Index is intended as daily percentage change in EPU Index.\"\n",
    "display(Markdown(title))\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "#### There seems to be **no significant correlation** between the daily percentage returns of oil prices and daily percentage change of EPU Index, even including lags.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Stock Close Price and EPU Index Volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Stock Price Volatility and EPU Index Volatility Correlation Heatmap\n",
       "Volatility intended as 30-day StD."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_599f4_row0_col0, #T_599f4_row1_col1, #T_599f4_row2_col2 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_599f4_row0_col1, #T_599f4_row1_col0 {\n",
       "  background-color: #b50927;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_599f4_row0_col2, #T_599f4_row2_col0 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_599f4_row1_col2, #T_599f4_row2_col1 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_599f4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_599f4_level0_col0\" class=\"col_heading level0 col0\" >Crude Oil Close</th>\n",
       "      <th id=\"T_599f4_level0_col1\" class=\"col_heading level0 col1\" >Brent Oil Close</th>\n",
       "      <th id=\"T_599f4_level0_col2\" class=\"col_heading level0 col2\" >EPU Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_599f4_level0_row0\" class=\"row_heading level0 row0\" >Crude Oil Close</th>\n",
       "      <td id=\"T_599f4_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_599f4_row0_col1\" class=\"data row0 col1\" >0.99</td>\n",
       "      <td id=\"T_599f4_row0_col2\" class=\"data row0 col2\" >-0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_599f4_level0_row1\" class=\"row_heading level0 row1\" >Brent Oil Close</th>\n",
       "      <td id=\"T_599f4_row1_col0\" class=\"data row1 col0\" >0.99</td>\n",
       "      <td id=\"T_599f4_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_599f4_row1_col2\" class=\"data row1 col2\" >-0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_599f4_level0_row2\" class=\"row_heading level0 row2\" >EPU Index</th>\n",
       "      <td id=\"T_599f4_row2_col0\" class=\"data row2 col0\" >-0.43</td>\n",
       "      <td id=\"T_599f4_row2_col1\" class=\"data row2 col1\" >-0.47</td>\n",
       "      <td id=\"T_599f4_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14f8d7f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "data_vol = data.copy()\n",
    "data_vol['EPU Index'] = data['EPU Index'].pct_change().rolling(window=30).std()\n",
    "correlation_matrix = data_vol.corr()\n",
    "\n",
    "# Display the correlation matrix as a table\n",
    "styled_table = correlation_matrix.style.background_gradient(cmap='coolwarm', axis=None).format(\"{:.2f}\")\n",
    "title = \"#### Stock Price Volatility and EPU Index Volatility Correlation Heatmap\\nVolatility intended as 30-day StD.\"\n",
    "display(Markdown(title))\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### (With Lags) Stock Price and EPU Index Volatility Correlation Heatmap\n",
       "Volatility intended as 30-day StD."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "}\n",
       "#T_232e0_row0_col14, #T_232e0_row8_col18, #T_232e0_row14_col0, #T_232e0_row18_col8 {\n",
       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row0_col15, #T_232e0_row10_col19, #T_232e0_row15_col0, #T_232e0_row19_col10 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row0_col17, #T_232e0_row1_col18, #T_232e0_row2_col20, #T_232e0_row17_col0, #T_232e0_row18_col1, #T_232e0_row20_col2 {\n",
       "  background-color: #f7b79b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row0_col18, #T_232e0_row3_col17, #T_232e0_row17_col3, #T_232e0_row18_col0 {\n",
       "  background-color: #f7ba9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row0_col19, #T_232e0_row19_col0 {\n",
       "  background-color: #f7b89c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row0_col20, #T_232e0_row1_col20, #T_232e0_row20_col0, #T_232e0_row20_col1 {\n",
       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row1_col2, #T_232e0_row2_col1, #T_232e0_row4_col15, #T_232e0_row9_col11, #T_232e0_row11_col9, #T_232e0_row15_col4 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col4, #T_232e0_row4_col1 {\n",
       "  background-color: #c73635;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col5, #T_232e0_row5_col1 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col6, #T_232e0_row4_col7, #T_232e0_row6_col1, #T_232e0_row7_col4 {\n",
       "  background-color: #e7745b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col7, #T_232e0_row7_col1 {\n",
       "  background-color: #eb7d62;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col8, #T_232e0_row8_col1 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col9, #T_232e0_row9_col1 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row1_col10, #T_232e0_row1_col14, #T_232e0_row10_col1, #T_232e0_row14_col1 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row1_col11, #T_232e0_row6_col20, #T_232e0_row11_col1, #T_232e0_row20_col6 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col12, #T_232e0_row4_col13, #T_232e0_row6_col12, #T_232e0_row9_col15, #T_232e0_row12_col1, #T_232e0_row12_col6, #T_232e0_row12_col16, #T_232e0_row13_col4, #T_232e0_row15_col9, #T_232e0_row16_col12 {\n",
       "  background-color: #b3cdfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row1_col13, #T_232e0_row13_col1, #T_232e0_row13_col17, #T_232e0_row17_col13 {\n",
       "  background-color: #a6c4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row1_col15, #T_232e0_row6_col8, #T_232e0_row8_col6, #T_232e0_row15_col1 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row1_col17, #T_232e0_row17_col1 {\n",
       "  background-color: #f7b599;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row1_col19, #T_232e0_row19_col1 {\n",
       "  background-color: #f7b497;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row2_col4, #T_232e0_row4_col2 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col5, #T_232e0_row5_col2 {\n",
       "  background-color: #c32e31;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col6, #T_232e0_row3_col5, #T_232e0_row3_col15, #T_232e0_row5_col3, #T_232e0_row5_col6, #T_232e0_row6_col2, #T_232e0_row6_col5, #T_232e0_row11_col15, #T_232e0_row15_col3, #T_232e0_row15_col11 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col7, #T_232e0_row5_col7, #T_232e0_row7_col2, #T_232e0_row7_col5 {\n",
       "  background-color: #6788ee;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col9, #T_232e0_row9_col2 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col10, #T_232e0_row2_col12, #T_232e0_row5_col12, #T_232e0_row8_col13, #T_232e0_row10_col2, #T_232e0_row12_col2, #T_232e0_row12_col5, #T_232e0_row13_col8 {\n",
       "  background-color: #445acc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col11, #T_232e0_row11_col2 {\n",
       "  background-color: #e57058;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col13, #T_232e0_row13_col2 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col14, #T_232e0_row14_col2 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row2_col15, #T_232e0_row15_col2 {\n",
       "  background-color: #779af7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row2_col16, #T_232e0_row8_col16, #T_232e0_row16_col2, #T_232e0_row16_col8 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row2_col17, #T_232e0_row17_col2, #T_232e0_row19_col20, #T_232e0_row20_col19 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row2_col18, #T_232e0_row18_col2 {\n",
       "  background-color: #f5c1a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row2_col19, #T_232e0_row4_col9, #T_232e0_row9_col4, #T_232e0_row19_col2 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col6, #T_232e0_row6_col3 {\n",
       "  background-color: #e67259;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row3_col8, #T_232e0_row8_col3, #T_232e0_row10_col18, #T_232e0_row18_col10 {\n",
       "  background-color: #82a6fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row3_col9, #T_232e0_row9_col3 {\n",
       "  background-color: #f1ccb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col10, #T_232e0_row10_col3 {\n",
       "  background-color: #ead5c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col11, #T_232e0_row9_col20, #T_232e0_row11_col3, #T_232e0_row20_col9 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col12, #T_232e0_row12_col3, #T_232e0_row16_col17, #T_232e0_row17_col16 {\n",
       "  background-color: #a7c5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col13, #T_232e0_row7_col11, #T_232e0_row11_col7, #T_232e0_row13_col3, #T_232e0_row17_col20, #T_232e0_row20_col17 {\n",
       "  background-color: #9fbfff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col14, #T_232e0_row14_col3 {\n",
       "  background-color: #f7b99e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col16, #T_232e0_row4_col8, #T_232e0_row8_col4, #T_232e0_row16_col3 {\n",
       "  background-color: #7b9ff9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row3_col18, #T_232e0_row3_col19, #T_232e0_row18_col3, #T_232e0_row19_col3 {\n",
       "  background-color: #f7a688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row3_col20, #T_232e0_row20_col3 {\n",
       "  background-color: #d3dbe7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col5, #T_232e0_row5_col4, #T_232e0_row10_col14, #T_232e0_row14_col10 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row4_col6, #T_232e0_row6_col4 {\n",
       "  background-color: #e36c55;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row4_col10, #T_232e0_row10_col4 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col11, #T_232e0_row11_col4 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col12, #T_232e0_row5_col17, #T_232e0_row12_col4, #T_232e0_row16_col20, #T_232e0_row17_col5, #T_232e0_row20_col16 {\n",
       "  background-color: #bed2f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col14, #T_232e0_row5_col14, #T_232e0_row5_col20, #T_232e0_row14_col4, #T_232e0_row14_col5, #T_232e0_row20_col5 {\n",
       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col16, #T_232e0_row5_col15, #T_232e0_row15_col5, #T_232e0_row16_col4 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row4_col17, #T_232e0_row17_col4 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col18, #T_232e0_row6_col9, #T_232e0_row9_col6, #T_232e0_row18_col4 {\n",
       "  background-color: #f6a385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col19, #T_232e0_row19_col4 {\n",
       "  background-color: #f6a283;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row4_col20, #T_232e0_row20_col4 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row5_col8, #T_232e0_row5_col11, #T_232e0_row8_col5, #T_232e0_row8_col11, #T_232e0_row11_col5, #T_232e0_row11_col8 {\n",
       "  background-color: #e9785d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row5_col9, #T_232e0_row5_col13, #T_232e0_row9_col5, #T_232e0_row13_col5 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row5_col10, #T_232e0_row10_col5, #T_232e0_row16_col18, #T_232e0_row18_col16 {\n",
       "  background-color: #455cce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row5_col16, #T_232e0_row8_col15, #T_232e0_row11_col16, #T_232e0_row15_col8, #T_232e0_row16_col5, #T_232e0_row16_col11 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row5_col18, #T_232e0_row18_col5 {\n",
       "  background-color: #f7af91;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row5_col19, #T_232e0_row19_col5 {\n",
       "  background-color: #f7a98b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row6_col10, #T_232e0_row10_col6 {\n",
       "  background-color: #f7ad90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row6_col11, #T_232e0_row6_col13, #T_232e0_row9_col19, #T_232e0_row11_col6, #T_232e0_row13_col6, #T_232e0_row15_col17, #T_232e0_row17_col15, #T_232e0_row19_col9 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row6_col14, #T_232e0_row9_col13, #T_232e0_row13_col9, #T_232e0_row14_col6 {\n",
       "  background-color: #e8d6cc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row6_col15, #T_232e0_row8_col10, #T_232e0_row10_col8, #T_232e0_row15_col6 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row6_col16, #T_232e0_row10_col11, #T_232e0_row11_col10, #T_232e0_row16_col6 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row6_col17, #T_232e0_row11_col14, #T_232e0_row14_col11, #T_232e0_row17_col6 {\n",
       "  background-color: #efcfbf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row6_col18, #T_232e0_row18_col6 {\n",
       "  background-color: #f18d6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row6_col19, #T_232e0_row7_col18, #T_232e0_row7_col19, #T_232e0_row18_col7, #T_232e0_row19_col6, #T_232e0_row19_col7 {\n",
       "  background-color: #f18f71;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row7_col9, #T_232e0_row9_col7 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row7_col10, #T_232e0_row10_col7 {\n",
       "  background-color: #f5a081;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row7_col12, #T_232e0_row12_col7 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row7_col13, #T_232e0_row13_col7 {\n",
       "  background-color: #c0d4f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row7_col14, #T_232e0_row14_col7 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row7_col15, #T_232e0_row15_col7 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row7_col16, #T_232e0_row16_col7 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row7_col17, #T_232e0_row17_col7 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row8_col9, #T_232e0_row9_col8, #T_232e0_row13_col18, #T_232e0_row18_col13 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row8_col12, #T_232e0_row12_col8 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row8_col14, #T_232e0_row14_col8 {\n",
       "  background-color: #ebd3c6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row8_col17, #T_232e0_row17_col8 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row8_col19, #T_232e0_row10_col12, #T_232e0_row12_col10, #T_232e0_row14_col19, #T_232e0_row19_col8, #T_232e0_row19_col14 {\n",
       "  background-color: #f5c0a7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row8_col20, #T_232e0_row20_col8 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row9_col12, #T_232e0_row10_col13, #T_232e0_row12_col9, #T_232e0_row13_col10 {\n",
       "  background-color: #f1cdba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row9_col14, #T_232e0_row14_col9 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row9_col16, #T_232e0_row9_col18, #T_232e0_row10_col20, #T_232e0_row14_col16, #T_232e0_row16_col9, #T_232e0_row16_col14, #T_232e0_row18_col9, #T_232e0_row20_col10 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row9_col17, #T_232e0_row17_col9 {\n",
       "  background-color: #bad0f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row10_col15, #T_232e0_row15_col10 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row10_col16, #T_232e0_row16_col10 {\n",
       "  background-color: #b1cbfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row10_col17, #T_232e0_row17_col10 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row11_col12, #T_232e0_row12_col11, #T_232e0_row13_col19, #T_232e0_row19_col13 {\n",
       "  background-color: #4f69d9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row11_col13, #T_232e0_row13_col11 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row11_col17, #T_232e0_row17_col11 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row11_col18, #T_232e0_row18_col11 {\n",
       "  background-color: #dddcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row11_col19, #T_232e0_row19_col11 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row11_col20, #T_232e0_row20_col11 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row12_col14, #T_232e0_row14_col12 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row12_col15, #T_232e0_row15_col12 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row12_col17, #T_232e0_row17_col12 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row12_col18, #T_232e0_row18_col12 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row12_col19, #T_232e0_row19_col12 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row12_col20, #T_232e0_row20_col12 {\n",
       "  background-color: #f39475;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row13_col14, #T_232e0_row14_col13 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row13_col15, #T_232e0_row14_col20, #T_232e0_row15_col13, #T_232e0_row20_col14 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row13_col16, #T_232e0_row16_col13 {\n",
       "  background-color: #bfd3f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row13_col20, #T_232e0_row20_col13 {\n",
       "  background-color: #f29274;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row14_col15, #T_232e0_row15_col14 {\n",
       "  background-color: #97b8ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row14_col17, #T_232e0_row17_col14 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row14_col18, #T_232e0_row18_col14 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row15_col16, #T_232e0_row16_col15 {\n",
       "  background-color: #b8122a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row15_col18, #T_232e0_row16_col19, #T_232e0_row18_col15, #T_232e0_row19_col16 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row15_col19, #T_232e0_row19_col15 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row15_col20, #T_232e0_row20_col15 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row17_col18, #T_232e0_row18_col17 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row17_col19, #T_232e0_row19_col17 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_232e0_row18_col19, #T_232e0_row19_col18 {\n",
       "  background-color: #b70d28;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_232e0_row18_col20, #T_232e0_row20_col18 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_232e0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_232e0_level0_col0\" class=\"col_heading level0 col0\" >Crude Oil Close</th>\n",
       "      <th id=\"T_232e0_level0_col1\" class=\"col_heading level0 col1\" >Brent Oil Close</th>\n",
       "      <th id=\"T_232e0_level0_col2\" class=\"col_heading level0 col2\" >EPU Index</th>\n",
       "      <th id=\"T_232e0_level0_col3\" class=\"col_heading level0 col3\" >Crude Lag_5</th>\n",
       "      <th id=\"T_232e0_level0_col4\" class=\"col_heading level0 col4\" >Brent Lag_5</th>\n",
       "      <th id=\"T_232e0_level0_col5\" class=\"col_heading level0 col5\" >EPU I Lag_5</th>\n",
       "      <th id=\"T_232e0_level0_col6\" class=\"col_heading level0 col6\" >Crude Lag_30</th>\n",
       "      <th id=\"T_232e0_level0_col7\" class=\"col_heading level0 col7\" >Brent Lag_30</th>\n",
       "      <th id=\"T_232e0_level0_col8\" class=\"col_heading level0 col8\" >EPU I Lag_30</th>\n",
       "      <th id=\"T_232e0_level0_col9\" class=\"col_heading level0 col9\" >Crude Lag_90</th>\n",
       "      <th id=\"T_232e0_level0_col10\" class=\"col_heading level0 col10\" >Brent Lag_90</th>\n",
       "      <th id=\"T_232e0_level0_col11\" class=\"col_heading level0 col11\" >EPU I Lag_90</th>\n",
       "      <th id=\"T_232e0_level0_col12\" class=\"col_heading level0 col12\" >Crude Lag_180</th>\n",
       "      <th id=\"T_232e0_level0_col13\" class=\"col_heading level0 col13\" >Brent Lag_180</th>\n",
       "      <th id=\"T_232e0_level0_col14\" class=\"col_heading level0 col14\" >EPU I Lag_180</th>\n",
       "      <th id=\"T_232e0_level0_col15\" class=\"col_heading level0 col15\" >Crude Lag_270</th>\n",
       "      <th id=\"T_232e0_level0_col16\" class=\"col_heading level0 col16\" >Brent Lag_270</th>\n",
       "      <th id=\"T_232e0_level0_col17\" class=\"col_heading level0 col17\" >EPU I Lag_270</th>\n",
       "      <th id=\"T_232e0_level0_col18\" class=\"col_heading level0 col18\" >Crude Lag_360</th>\n",
       "      <th id=\"T_232e0_level0_col19\" class=\"col_heading level0 col19\" >Brent Lag_360</th>\n",
       "      <th id=\"T_232e0_level0_col20\" class=\"col_heading level0 col20\" >EPU I Lag_360</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row0\" class=\"row_heading level0 row0\" >Crude Oil Close</th>\n",
       "      <td id=\"T_232e0_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_232e0_row0_col1\" class=\"data row0 col1\" >0.99</td>\n",
       "      <td id=\"T_232e0_row0_col2\" class=\"data row0 col2\" >-0.43</td>\n",
       "      <td id=\"T_232e0_row0_col3\" class=\"data row0 col3\" >0.91</td>\n",
       "      <td id=\"T_232e0_row0_col4\" class=\"data row0 col4\" >0.90</td>\n",
       "      <td id=\"T_232e0_row0_col5\" class=\"data row0 col5\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row0_col6\" class=\"data row0 col6\" >0.69</td>\n",
       "      <td id=\"T_232e0_row0_col7\" class=\"data row0 col7\" >0.65</td>\n",
       "      <td id=\"T_232e0_row0_col8\" class=\"data row0 col8\" >-0.32</td>\n",
       "      <td id=\"T_232e0_row0_col9\" class=\"data row0 col9\" >0.27</td>\n",
       "      <td id=\"T_232e0_row0_col10\" class=\"data row0 col10\" >0.21</td>\n",
       "      <td id=\"T_232e0_row0_col11\" class=\"data row0 col11\" >-0.27</td>\n",
       "      <td id=\"T_232e0_row0_col12\" class=\"data row0 col12\" >-0.19</td>\n",
       "      <td id=\"T_232e0_row0_col13\" class=\"data row0 col13\" >-0.24</td>\n",
       "      <td id=\"T_232e0_row0_col14\" class=\"data row0 col14\" >0.37</td>\n",
       "      <td id=\"T_232e0_row0_col15\" class=\"data row0 col15\" >-0.30</td>\n",
       "      <td id=\"T_232e0_row0_col16\" class=\"data row0 col16\" >-0.24</td>\n",
       "      <td id=\"T_232e0_row0_col17\" class=\"data row0 col17\" >0.44</td>\n",
       "      <td id=\"T_232e0_row0_col18\" class=\"data row0 col18\" >0.42</td>\n",
       "      <td id=\"T_232e0_row0_col19\" class=\"data row0 col19\" >0.43</td>\n",
       "      <td id=\"T_232e0_row0_col20\" class=\"data row0 col20\" >0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row1\" class=\"row_heading level0 row1\" >Brent Oil Close</th>\n",
       "      <td id=\"T_232e0_row1_col0\" class=\"data row1 col0\" >0.99</td>\n",
       "      <td id=\"T_232e0_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_232e0_row1_col2\" class=\"data row1 col2\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row1_col3\" class=\"data row1 col3\" >0.91</td>\n",
       "      <td id=\"T_232e0_row1_col4\" class=\"data row1 col4\" >0.91</td>\n",
       "      <td id=\"T_232e0_row1_col5\" class=\"data row1 col5\" >-0.46</td>\n",
       "      <td id=\"T_232e0_row1_col6\" class=\"data row1 col6\" >0.72</td>\n",
       "      <td id=\"T_232e0_row1_col7\" class=\"data row1 col7\" >0.68</td>\n",
       "      <td id=\"T_232e0_row1_col8\" class=\"data row1 col8\" >-0.36</td>\n",
       "      <td id=\"T_232e0_row1_col9\" class=\"data row1 col9\" >0.36</td>\n",
       "      <td id=\"T_232e0_row1_col10\" class=\"data row1 col10\" >0.30</td>\n",
       "      <td id=\"T_232e0_row1_col11\" class=\"data row1 col11\" >-0.31</td>\n",
       "      <td id=\"T_232e0_row1_col12\" class=\"data row1 col12\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row1_col13\" class=\"data row1 col13\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row1_col14\" class=\"data row1 col14\" >0.30</td>\n",
       "      <td id=\"T_232e0_row1_col15\" class=\"data row1 col15\" >-0.37</td>\n",
       "      <td id=\"T_232e0_row1_col16\" class=\"data row1 col16\" >-0.32</td>\n",
       "      <td id=\"T_232e0_row1_col17\" class=\"data row1 col17\" >0.44</td>\n",
       "      <td id=\"T_232e0_row1_col18\" class=\"data row1 col18\" >0.44</td>\n",
       "      <td id=\"T_232e0_row1_col19\" class=\"data row1 col19\" >0.45</td>\n",
       "      <td id=\"T_232e0_row1_col20\" class=\"data row1 col20\" >0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row2\" class=\"row_heading level0 row2\" >EPU Index</th>\n",
       "      <td id=\"T_232e0_row2_col0\" class=\"data row2 col0\" >-0.43</td>\n",
       "      <td id=\"T_232e0_row2_col1\" class=\"data row2 col1\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "      <td id=\"T_232e0_row2_col3\" class=\"data row2 col3\" >-0.44</td>\n",
       "      <td id=\"T_232e0_row2_col4\" class=\"data row2 col4\" >-0.48</td>\n",
       "      <td id=\"T_232e0_row2_col5\" class=\"data row2 col5\" >0.93</td>\n",
       "      <td id=\"T_232e0_row2_col6\" class=\"data row2 col6\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row2_col7\" class=\"data row2 col7\" >-0.46</td>\n",
       "      <td id=\"T_232e0_row2_col8\" class=\"data row2 col8\" >0.69</td>\n",
       "      <td id=\"T_232e0_row2_col9\" class=\"data row2 col9\" >-0.63</td>\n",
       "      <td id=\"T_232e0_row2_col10\" class=\"data row2 col10\" >-0.64</td>\n",
       "      <td id=\"T_232e0_row2_col11\" class=\"data row2 col11\" >0.73</td>\n",
       "      <td id=\"T_232e0_row2_col12\" class=\"data row2 col12\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row2_col13\" class=\"data row2 col13\" >-0.61</td>\n",
       "      <td id=\"T_232e0_row2_col14\" class=\"data row2 col14\" >0.28</td>\n",
       "      <td id=\"T_232e0_row2_col15\" class=\"data row2 col15\" >-0.38</td>\n",
       "      <td id=\"T_232e0_row2_col16\" class=\"data row2 col16\" >-0.28</td>\n",
       "      <td id=\"T_232e0_row2_col17\" class=\"data row2 col17\" >-0.17</td>\n",
       "      <td id=\"T_232e0_row2_col18\" class=\"data row2 col18\" >0.37</td>\n",
       "      <td id=\"T_232e0_row2_col19\" class=\"data row2 col19\" >0.40</td>\n",
       "      <td id=\"T_232e0_row2_col20\" class=\"data row2 col20\" >0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row3\" class=\"row_heading level0 row3\" >Crude Lag_5</th>\n",
       "      <td id=\"T_232e0_row3_col0\" class=\"data row3 col0\" >0.91</td>\n",
       "      <td id=\"T_232e0_row3_col1\" class=\"data row3 col1\" >0.91</td>\n",
       "      <td id=\"T_232e0_row3_col2\" class=\"data row3 col2\" >-0.44</td>\n",
       "      <td id=\"T_232e0_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "      <td id=\"T_232e0_row3_col4\" class=\"data row3 col4\" >0.99</td>\n",
       "      <td id=\"T_232e0_row3_col5\" class=\"data row3 col5\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row3_col6\" class=\"data row3 col6\" >0.72</td>\n",
       "      <td id=\"T_232e0_row3_col7\" class=\"data row3 col7\" >0.69</td>\n",
       "      <td id=\"T_232e0_row3_col8\" class=\"data row3 col8\" >-0.33</td>\n",
       "      <td id=\"T_232e0_row3_col9\" class=\"data row3 col9\" >0.31</td>\n",
       "      <td id=\"T_232e0_row3_col10\" class=\"data row3 col10\" >0.24</td>\n",
       "      <td id=\"T_232e0_row3_col11\" class=\"data row3 col11\" >-0.23</td>\n",
       "      <td id=\"T_232e0_row3_col12\" class=\"data row3 col12\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row3_col13\" class=\"data row3 col13\" >-0.19</td>\n",
       "      <td id=\"T_232e0_row3_col14\" class=\"data row3 col14\" >0.42</td>\n",
       "      <td id=\"T_232e0_row3_col15\" class=\"data row3 col15\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row3_col16\" class=\"data row3 col16\" >-0.36</td>\n",
       "      <td id=\"T_232e0_row3_col17\" class=\"data row3 col17\" >0.42</td>\n",
       "      <td id=\"T_232e0_row3_col18\" class=\"data row3 col18\" >0.52</td>\n",
       "      <td id=\"T_232e0_row3_col19\" class=\"data row3 col19\" >0.52</td>\n",
       "      <td id=\"T_232e0_row3_col20\" class=\"data row3 col20\" >0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row4\" class=\"row_heading level0 row4\" >Brent Lag_5</th>\n",
       "      <td id=\"T_232e0_row4_col0\" class=\"data row4 col0\" >0.90</td>\n",
       "      <td id=\"T_232e0_row4_col1\" class=\"data row4 col1\" >0.91</td>\n",
       "      <td id=\"T_232e0_row4_col2\" class=\"data row4 col2\" >-0.48</td>\n",
       "      <td id=\"T_232e0_row4_col3\" class=\"data row4 col3\" >0.99</td>\n",
       "      <td id=\"T_232e0_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "      <td id=\"T_232e0_row4_col5\" class=\"data row4 col5\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row4_col6\" class=\"data row4 col6\" >0.74</td>\n",
       "      <td id=\"T_232e0_row4_col7\" class=\"data row4 col7\" >0.71</td>\n",
       "      <td id=\"T_232e0_row4_col8\" class=\"data row4 col8\" >-0.36</td>\n",
       "      <td id=\"T_232e0_row4_col9\" class=\"data row4 col9\" >0.40</td>\n",
       "      <td id=\"T_232e0_row4_col10\" class=\"data row4 col10\" >0.33</td>\n",
       "      <td id=\"T_232e0_row4_col11\" class=\"data row4 col11\" >-0.29</td>\n",
       "      <td id=\"T_232e0_row4_col12\" class=\"data row4 col12\" >-0.03</td>\n",
       "      <td id=\"T_232e0_row4_col13\" class=\"data row4 col13\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row4_col14\" class=\"data row4 col14\" >0.34</td>\n",
       "      <td id=\"T_232e0_row4_col15\" class=\"data row4 col15\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row4_col16\" class=\"data row4 col16\" >-0.43</td>\n",
       "      <td id=\"T_232e0_row4_col17\" class=\"data row4 col17\" >0.41</td>\n",
       "      <td id=\"T_232e0_row4_col18\" class=\"data row4 col18\" >0.53</td>\n",
       "      <td id=\"T_232e0_row4_col19\" class=\"data row4 col19\" >0.53</td>\n",
       "      <td id=\"T_232e0_row4_col20\" class=\"data row4 col20\" >0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row5\" class=\"row_heading level0 row5\" >EPU I Lag_5</th>\n",
       "      <td id=\"T_232e0_row5_col0\" class=\"data row5 col0\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row5_col1\" class=\"data row5 col1\" >-0.46</td>\n",
       "      <td id=\"T_232e0_row5_col2\" class=\"data row5 col2\" >0.93</td>\n",
       "      <td id=\"T_232e0_row5_col3\" class=\"data row5 col3\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row5_col4\" class=\"data row5 col4\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "      <td id=\"T_232e0_row5_col6\" class=\"data row5 col6\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row5_col7\" class=\"data row5 col7\" >-0.46</td>\n",
       "      <td id=\"T_232e0_row5_col8\" class=\"data row5 col8\" >0.71</td>\n",
       "      <td id=\"T_232e0_row5_col9\" class=\"data row5 col9\" >-0.62</td>\n",
       "      <td id=\"T_232e0_row5_col10\" class=\"data row5 col10\" >-0.64</td>\n",
       "      <td id=\"T_232e0_row5_col11\" class=\"data row5 col11\" >0.70</td>\n",
       "      <td id=\"T_232e0_row5_col12\" class=\"data row5 col12\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row5_col13\" class=\"data row5 col13\" >-0.62</td>\n",
       "      <td id=\"T_232e0_row5_col14\" class=\"data row5 col14\" >0.34</td>\n",
       "      <td id=\"T_232e0_row5_col15\" class=\"data row5 col15\" >-0.43</td>\n",
       "      <td id=\"T_232e0_row5_col16\" class=\"data row5 col16\" >-0.35</td>\n",
       "      <td id=\"T_232e0_row5_col17\" class=\"data row5 col17\" >-0.03</td>\n",
       "      <td id=\"T_232e0_row5_col18\" class=\"data row5 col18\" >0.48</td>\n",
       "      <td id=\"T_232e0_row5_col19\" class=\"data row5 col19\" >0.50</td>\n",
       "      <td id=\"T_232e0_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row6\" class=\"row_heading level0 row6\" >Crude Lag_30</th>\n",
       "      <td id=\"T_232e0_row6_col0\" class=\"data row6 col0\" >0.69</td>\n",
       "      <td id=\"T_232e0_row6_col1\" class=\"data row6 col1\" >0.72</td>\n",
       "      <td id=\"T_232e0_row6_col2\" class=\"data row6 col2\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row6_col3\" class=\"data row6 col3\" >0.72</td>\n",
       "      <td id=\"T_232e0_row6_col4\" class=\"data row6 col4\" >0.74</td>\n",
       "      <td id=\"T_232e0_row6_col5\" class=\"data row6 col5\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "      <td id=\"T_232e0_row6_col7\" class=\"data row6 col7\" >0.99</td>\n",
       "      <td id=\"T_232e0_row6_col8\" class=\"data row6 col8\" >-0.37</td>\n",
       "      <td id=\"T_232e0_row6_col9\" class=\"data row6 col9\" >0.53</td>\n",
       "      <td id=\"T_232e0_row6_col10\" class=\"data row6 col10\" >0.48</td>\n",
       "      <td id=\"T_232e0_row6_col11\" class=\"data row6 col11\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row6_col12\" class=\"data row6 col12\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row6_col13\" class=\"data row6 col13\" >-0.14</td>\n",
       "      <td id=\"T_232e0_row6_col14\" class=\"data row6 col14\" >0.23</td>\n",
       "      <td id=\"T_232e0_row6_col15\" class=\"data row6 col15\" >-0.55</td>\n",
       "      <td id=\"T_232e0_row6_col16\" class=\"data row6 col16\" >-0.51</td>\n",
       "      <td id=\"T_232e0_row6_col17\" class=\"data row6 col17\" >0.29</td>\n",
       "      <td id=\"T_232e0_row6_col18\" class=\"data row6 col18\" >0.62</td>\n",
       "      <td id=\"T_232e0_row6_col19\" class=\"data row6 col19\" >0.62</td>\n",
       "      <td id=\"T_232e0_row6_col20\" class=\"data row6 col20\" >-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row7\" class=\"row_heading level0 row7\" >Brent Lag_30</th>\n",
       "      <td id=\"T_232e0_row7_col0\" class=\"data row7 col0\" >0.65</td>\n",
       "      <td id=\"T_232e0_row7_col1\" class=\"data row7 col1\" >0.68</td>\n",
       "      <td id=\"T_232e0_row7_col2\" class=\"data row7 col2\" >-0.46</td>\n",
       "      <td id=\"T_232e0_row7_col3\" class=\"data row7 col3\" >0.69</td>\n",
       "      <td id=\"T_232e0_row7_col4\" class=\"data row7 col4\" >0.71</td>\n",
       "      <td id=\"T_232e0_row7_col5\" class=\"data row7 col5\" >-0.46</td>\n",
       "      <td id=\"T_232e0_row7_col6\" class=\"data row7 col6\" >0.99</td>\n",
       "      <td id=\"T_232e0_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "      <td id=\"T_232e0_row7_col8\" class=\"data row7 col8\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row7_col9\" class=\"data row7 col9\" >0.59</td>\n",
       "      <td id=\"T_232e0_row7_col10\" class=\"data row7 col10\" >0.54</td>\n",
       "      <td id=\"T_232e0_row7_col11\" class=\"data row7 col11\" >-0.19</td>\n",
       "      <td id=\"T_232e0_row7_col12\" class=\"data row7 col12\" >0.03</td>\n",
       "      <td id=\"T_232e0_row7_col13\" class=\"data row7 col13\" >-0.02</td>\n",
       "      <td id=\"T_232e0_row7_col14\" class=\"data row7 col14\" >0.13</td>\n",
       "      <td id=\"T_232e0_row7_col15\" class=\"data row7 col15\" >-0.54</td>\n",
       "      <td id=\"T_232e0_row7_col16\" class=\"data row7 col16\" >-0.51</td>\n",
       "      <td id=\"T_232e0_row7_col17\" class=\"data row7 col17\" >0.27</td>\n",
       "      <td id=\"T_232e0_row7_col18\" class=\"data row7 col18\" >0.62</td>\n",
       "      <td id=\"T_232e0_row7_col19\" class=\"data row7 col19\" >0.61</td>\n",
       "      <td id=\"T_232e0_row7_col20\" class=\"data row7 col20\" >-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row8\" class=\"row_heading level0 row8\" >EPU I Lag_30</th>\n",
       "      <td id=\"T_232e0_row8_col0\" class=\"data row8 col0\" >-0.32</td>\n",
       "      <td id=\"T_232e0_row8_col1\" class=\"data row8 col1\" >-0.36</td>\n",
       "      <td id=\"T_232e0_row8_col2\" class=\"data row8 col2\" >0.69</td>\n",
       "      <td id=\"T_232e0_row8_col3\" class=\"data row8 col3\" >-0.33</td>\n",
       "      <td id=\"T_232e0_row8_col4\" class=\"data row8 col4\" >-0.36</td>\n",
       "      <td id=\"T_232e0_row8_col5\" class=\"data row8 col5\" >0.71</td>\n",
       "      <td id=\"T_232e0_row8_col6\" class=\"data row8 col6\" >-0.37</td>\n",
       "      <td id=\"T_232e0_row8_col7\" class=\"data row8 col7\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
       "      <td id=\"T_232e0_row8_col9\" class=\"data row8 col9\" >-0.53</td>\n",
       "      <td id=\"T_232e0_row8_col10\" class=\"data row8 col10\" >-0.55</td>\n",
       "      <td id=\"T_232e0_row8_col11\" class=\"data row8 col11\" >0.70</td>\n",
       "      <td id=\"T_232e0_row8_col12\" class=\"data row8 col12\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row8_col13\" class=\"data row8 col13\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row8_col14\" class=\"data row8 col14\" >0.25</td>\n",
       "      <td id=\"T_232e0_row8_col15\" class=\"data row8 col15\" >-0.35</td>\n",
       "      <td id=\"T_232e0_row8_col16\" class=\"data row8 col16\" >-0.28</td>\n",
       "      <td id=\"T_232e0_row8_col17\" class=\"data row8 col17\" >0.13</td>\n",
       "      <td id=\"T_232e0_row8_col18\" class=\"data row8 col18\" >0.37</td>\n",
       "      <td id=\"T_232e0_row8_col19\" class=\"data row8 col19\" >0.38</td>\n",
       "      <td id=\"T_232e0_row8_col20\" class=\"data row8 col20\" >-0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row9\" class=\"row_heading level0 row9\" >Crude Lag_90</th>\n",
       "      <td id=\"T_232e0_row9_col0\" class=\"data row9 col0\" >0.27</td>\n",
       "      <td id=\"T_232e0_row9_col1\" class=\"data row9 col1\" >0.36</td>\n",
       "      <td id=\"T_232e0_row9_col2\" class=\"data row9 col2\" >-0.63</td>\n",
       "      <td id=\"T_232e0_row9_col3\" class=\"data row9 col3\" >0.31</td>\n",
       "      <td id=\"T_232e0_row9_col4\" class=\"data row9 col4\" >0.40</td>\n",
       "      <td id=\"T_232e0_row9_col5\" class=\"data row9 col5\" >-0.62</td>\n",
       "      <td id=\"T_232e0_row9_col6\" class=\"data row9 col6\" >0.53</td>\n",
       "      <td id=\"T_232e0_row9_col7\" class=\"data row9 col7\" >0.59</td>\n",
       "      <td id=\"T_232e0_row9_col8\" class=\"data row9 col8\" >-0.53</td>\n",
       "      <td id=\"T_232e0_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
       "      <td id=\"T_232e0_row9_col10\" class=\"data row9 col10\" >0.99</td>\n",
       "      <td id=\"T_232e0_row9_col11\" class=\"data row9 col11\" >-0.48</td>\n",
       "      <td id=\"T_232e0_row9_col12\" class=\"data row9 col12\" >0.30</td>\n",
       "      <td id=\"T_232e0_row9_col13\" class=\"data row9 col13\" >0.23</td>\n",
       "      <td id=\"T_232e0_row9_col14\" class=\"data row9 col14\" >-0.44</td>\n",
       "      <td id=\"T_232e0_row9_col15\" class=\"data row9 col15\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row9_col16\" class=\"data row9 col16\" >-0.18</td>\n",
       "      <td id=\"T_232e0_row9_col17\" class=\"data row9 col17\" >-0.05</td>\n",
       "      <td id=\"T_232e0_row9_col18\" class=\"data row9 col18\" >-0.18</td>\n",
       "      <td id=\"T_232e0_row9_col19\" class=\"data row9 col19\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row9_col20\" class=\"data row9 col20\" >-0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row10\" class=\"row_heading level0 row10\" >Brent Lag_90</th>\n",
       "      <td id=\"T_232e0_row10_col0\" class=\"data row10 col0\" >0.21</td>\n",
       "      <td id=\"T_232e0_row10_col1\" class=\"data row10 col1\" >0.30</td>\n",
       "      <td id=\"T_232e0_row10_col2\" class=\"data row10 col2\" >-0.64</td>\n",
       "      <td id=\"T_232e0_row10_col3\" class=\"data row10 col3\" >0.24</td>\n",
       "      <td id=\"T_232e0_row10_col4\" class=\"data row10 col4\" >0.33</td>\n",
       "      <td id=\"T_232e0_row10_col5\" class=\"data row10 col5\" >-0.64</td>\n",
       "      <td id=\"T_232e0_row10_col6\" class=\"data row10 col6\" >0.48</td>\n",
       "      <td id=\"T_232e0_row10_col7\" class=\"data row10 col7\" >0.54</td>\n",
       "      <td id=\"T_232e0_row10_col8\" class=\"data row10 col8\" >-0.55</td>\n",
       "      <td id=\"T_232e0_row10_col9\" class=\"data row10 col9\" >0.99</td>\n",
       "      <td id=\"T_232e0_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
       "      <td id=\"T_232e0_row10_col11\" class=\"data row10 col11\" >-0.52</td>\n",
       "      <td id=\"T_232e0_row10_col12\" class=\"data row10 col12\" >0.38</td>\n",
       "      <td id=\"T_232e0_row10_col13\" class=\"data row10 col13\" >0.31</td>\n",
       "      <td id=\"T_232e0_row10_col14\" class=\"data row10 col14\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row10_col15\" class=\"data row10 col15\" >0.00</td>\n",
       "      <td id=\"T_232e0_row10_col16\" class=\"data row10 col16\" >-0.10</td>\n",
       "      <td id=\"T_232e0_row10_col17\" class=\"data row10 col17\" >-0.06</td>\n",
       "      <td id=\"T_232e0_row10_col18\" class=\"data row10 col18\" >-0.33</td>\n",
       "      <td id=\"T_232e0_row10_col19\" class=\"data row10 col19\" >-0.30</td>\n",
       "      <td id=\"T_232e0_row10_col20\" class=\"data row10 col20\" >-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row11\" class=\"row_heading level0 row11\" >EPU I Lag_90</th>\n",
       "      <td id=\"T_232e0_row11_col0\" class=\"data row11 col0\" >-0.27</td>\n",
       "      <td id=\"T_232e0_row11_col1\" class=\"data row11 col1\" >-0.31</td>\n",
       "      <td id=\"T_232e0_row11_col2\" class=\"data row11 col2\" >0.73</td>\n",
       "      <td id=\"T_232e0_row11_col3\" class=\"data row11 col3\" >-0.23</td>\n",
       "      <td id=\"T_232e0_row11_col4\" class=\"data row11 col4\" >-0.29</td>\n",
       "      <td id=\"T_232e0_row11_col5\" class=\"data row11 col5\" >0.70</td>\n",
       "      <td id=\"T_232e0_row11_col6\" class=\"data row11 col6\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row11_col7\" class=\"data row11 col7\" >-0.19</td>\n",
       "      <td id=\"T_232e0_row11_col8\" class=\"data row11 col8\" >0.70</td>\n",
       "      <td id=\"T_232e0_row11_col9\" class=\"data row11 col9\" >-0.48</td>\n",
       "      <td id=\"T_232e0_row11_col10\" class=\"data row11 col10\" >-0.52</td>\n",
       "      <td id=\"T_232e0_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
       "      <td id=\"T_232e0_row11_col12\" class=\"data row11 col12\" >-0.59</td>\n",
       "      <td id=\"T_232e0_row11_col13\" class=\"data row11 col13\" >-0.58</td>\n",
       "      <td id=\"T_232e0_row11_col14\" class=\"data row11 col14\" >0.29</td>\n",
       "      <td id=\"T_232e0_row11_col15\" class=\"data row11 col15\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row11_col16\" class=\"data row11 col16\" >-0.35</td>\n",
       "      <td id=\"T_232e0_row11_col17\" class=\"data row11 col17\" >-0.08</td>\n",
       "      <td id=\"T_232e0_row11_col18\" class=\"data row11 col18\" >0.15</td>\n",
       "      <td id=\"T_232e0_row11_col19\" class=\"data row11 col19\" >0.21</td>\n",
       "      <td id=\"T_232e0_row11_col20\" class=\"data row11 col20\" >-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row12\" class=\"row_heading level0 row12\" >Crude Lag_180</th>\n",
       "      <td id=\"T_232e0_row12_col0\" class=\"data row12 col0\" >-0.19</td>\n",
       "      <td id=\"T_232e0_row12_col1\" class=\"data row12 col1\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row12_col2\" class=\"data row12 col2\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row12_col3\" class=\"data row12 col3\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row12_col4\" class=\"data row12 col4\" >-0.03</td>\n",
       "      <td id=\"T_232e0_row12_col5\" class=\"data row12 col5\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row12_col6\" class=\"data row12 col6\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row12_col7\" class=\"data row12 col7\" >0.03</td>\n",
       "      <td id=\"T_232e0_row12_col8\" class=\"data row12 col8\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row12_col9\" class=\"data row12 col9\" >0.30</td>\n",
       "      <td id=\"T_232e0_row12_col10\" class=\"data row12 col10\" >0.38</td>\n",
       "      <td id=\"T_232e0_row12_col11\" class=\"data row12 col11\" >-0.59</td>\n",
       "      <td id=\"T_232e0_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
       "      <td id=\"T_232e0_row12_col13\" class=\"data row12 col13\" >0.99</td>\n",
       "      <td id=\"T_232e0_row12_col14\" class=\"data row12 col14\" >-0.40</td>\n",
       "      <td id=\"T_232e0_row12_col15\" class=\"data row12 col15\" >0.03</td>\n",
       "      <td id=\"T_232e0_row12_col16\" class=\"data row12 col16\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row12_col17\" class=\"data row12 col17\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row12_col18\" class=\"data row12 col18\" >-0.57</td>\n",
       "      <td id=\"T_232e0_row12_col19\" class=\"data row12 col19\" >-0.60</td>\n",
       "      <td id=\"T_232e0_row12_col20\" class=\"data row12 col20\" >0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row13\" class=\"row_heading level0 row13\" >Brent Lag_180</th>\n",
       "      <td id=\"T_232e0_row13_col0\" class=\"data row13 col0\" >-0.24</td>\n",
       "      <td id=\"T_232e0_row13_col1\" class=\"data row13 col1\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row13_col2\" class=\"data row13 col2\" >-0.61</td>\n",
       "      <td id=\"T_232e0_row13_col3\" class=\"data row13 col3\" >-0.19</td>\n",
       "      <td id=\"T_232e0_row13_col4\" class=\"data row13 col4\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row13_col5\" class=\"data row13 col5\" >-0.62</td>\n",
       "      <td id=\"T_232e0_row13_col6\" class=\"data row13 col6\" >-0.14</td>\n",
       "      <td id=\"T_232e0_row13_col7\" class=\"data row13 col7\" >-0.02</td>\n",
       "      <td id=\"T_232e0_row13_col8\" class=\"data row13 col8\" >-0.65</td>\n",
       "      <td id=\"T_232e0_row13_col9\" class=\"data row13 col9\" >0.23</td>\n",
       "      <td id=\"T_232e0_row13_col10\" class=\"data row13 col10\" >0.31</td>\n",
       "      <td id=\"T_232e0_row13_col11\" class=\"data row13 col11\" >-0.58</td>\n",
       "      <td id=\"T_232e0_row13_col12\" class=\"data row13 col12\" >0.99</td>\n",
       "      <td id=\"T_232e0_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
       "      <td id=\"T_232e0_row13_col14\" class=\"data row13 col14\" >-0.40</td>\n",
       "      <td id=\"T_232e0_row13_col15\" class=\"data row13 col15\" >0.10</td>\n",
       "      <td id=\"T_232e0_row13_col16\" class=\"data row13 col16\" >-0.03</td>\n",
       "      <td id=\"T_232e0_row13_col17\" class=\"data row13 col17\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row13_col18\" class=\"data row13 col18\" >-0.54</td>\n",
       "      <td id=\"T_232e0_row13_col19\" class=\"data row13 col19\" >-0.59</td>\n",
       "      <td id=\"T_232e0_row13_col20\" class=\"data row13 col20\" >0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row14\" class=\"row_heading level0 row14\" >EPU I Lag_180</th>\n",
       "      <td id=\"T_232e0_row14_col0\" class=\"data row14 col0\" >0.37</td>\n",
       "      <td id=\"T_232e0_row14_col1\" class=\"data row14 col1\" >0.30</td>\n",
       "      <td id=\"T_232e0_row14_col2\" class=\"data row14 col2\" >0.28</td>\n",
       "      <td id=\"T_232e0_row14_col3\" class=\"data row14 col3\" >0.42</td>\n",
       "      <td id=\"T_232e0_row14_col4\" class=\"data row14 col4\" >0.34</td>\n",
       "      <td id=\"T_232e0_row14_col5\" class=\"data row14 col5\" >0.34</td>\n",
       "      <td id=\"T_232e0_row14_col6\" class=\"data row14 col6\" >0.23</td>\n",
       "      <td id=\"T_232e0_row14_col7\" class=\"data row14 col7\" >0.13</td>\n",
       "      <td id=\"T_232e0_row14_col8\" class=\"data row14 col8\" >0.25</td>\n",
       "      <td id=\"T_232e0_row14_col9\" class=\"data row14 col9\" >-0.44</td>\n",
       "      <td id=\"T_232e0_row14_col10\" class=\"data row14 col10\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row14_col11\" class=\"data row14 col11\" >0.29</td>\n",
       "      <td id=\"T_232e0_row14_col12\" class=\"data row14 col12\" >-0.40</td>\n",
       "      <td id=\"T_232e0_row14_col13\" class=\"data row14 col13\" >-0.40</td>\n",
       "      <td id=\"T_232e0_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
       "      <td id=\"T_232e0_row14_col15\" class=\"data row14 col15\" >-0.23</td>\n",
       "      <td id=\"T_232e0_row14_col16\" class=\"data row14 col16\" >-0.17</td>\n",
       "      <td id=\"T_232e0_row14_col17\" class=\"data row14 col17\" >0.29</td>\n",
       "      <td id=\"T_232e0_row14_col18\" class=\"data row14 col18\" >0.41</td>\n",
       "      <td id=\"T_232e0_row14_col19\" class=\"data row14 col19\" >0.38</td>\n",
       "      <td id=\"T_232e0_row14_col20\" class=\"data row14 col20\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row15\" class=\"row_heading level0 row15\" >Crude Lag_270</th>\n",
       "      <td id=\"T_232e0_row15_col0\" class=\"data row15 col0\" >-0.30</td>\n",
       "      <td id=\"T_232e0_row15_col1\" class=\"data row15 col1\" >-0.37</td>\n",
       "      <td id=\"T_232e0_row15_col2\" class=\"data row15 col2\" >-0.38</td>\n",
       "      <td id=\"T_232e0_row15_col3\" class=\"data row15 col3\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row15_col4\" class=\"data row15 col4\" >-0.47</td>\n",
       "      <td id=\"T_232e0_row15_col5\" class=\"data row15 col5\" >-0.43</td>\n",
       "      <td id=\"T_232e0_row15_col6\" class=\"data row15 col6\" >-0.55</td>\n",
       "      <td id=\"T_232e0_row15_col7\" class=\"data row15 col7\" >-0.54</td>\n",
       "      <td id=\"T_232e0_row15_col8\" class=\"data row15 col8\" >-0.35</td>\n",
       "      <td id=\"T_232e0_row15_col9\" class=\"data row15 col9\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row15_col10\" class=\"data row15 col10\" >0.00</td>\n",
       "      <td id=\"T_232e0_row15_col11\" class=\"data row15 col11\" >-0.42</td>\n",
       "      <td id=\"T_232e0_row15_col12\" class=\"data row15 col12\" >0.03</td>\n",
       "      <td id=\"T_232e0_row15_col13\" class=\"data row15 col13\" >0.10</td>\n",
       "      <td id=\"T_232e0_row15_col14\" class=\"data row15 col14\" >-0.23</td>\n",
       "      <td id=\"T_232e0_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
       "      <td id=\"T_232e0_row15_col16\" class=\"data row15 col16\" >0.98</td>\n",
       "      <td id=\"T_232e0_row15_col17\" class=\"data row15 col17\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row15_col18\" class=\"data row15 col18\" >-0.67</td>\n",
       "      <td id=\"T_232e0_row15_col19\" class=\"data row15 col19\" >-0.70</td>\n",
       "      <td id=\"T_232e0_row15_col20\" class=\"data row15 col20\" >-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row16\" class=\"row_heading level0 row16\" >Brent Lag_270</th>\n",
       "      <td id=\"T_232e0_row16_col0\" class=\"data row16 col0\" >-0.24</td>\n",
       "      <td id=\"T_232e0_row16_col1\" class=\"data row16 col1\" >-0.32</td>\n",
       "      <td id=\"T_232e0_row16_col2\" class=\"data row16 col2\" >-0.28</td>\n",
       "      <td id=\"T_232e0_row16_col3\" class=\"data row16 col3\" >-0.36</td>\n",
       "      <td id=\"T_232e0_row16_col4\" class=\"data row16 col4\" >-0.43</td>\n",
       "      <td id=\"T_232e0_row16_col5\" class=\"data row16 col5\" >-0.35</td>\n",
       "      <td id=\"T_232e0_row16_col6\" class=\"data row16 col6\" >-0.51</td>\n",
       "      <td id=\"T_232e0_row16_col7\" class=\"data row16 col7\" >-0.51</td>\n",
       "      <td id=\"T_232e0_row16_col8\" class=\"data row16 col8\" >-0.28</td>\n",
       "      <td id=\"T_232e0_row16_col9\" class=\"data row16 col9\" >-0.18</td>\n",
       "      <td id=\"T_232e0_row16_col10\" class=\"data row16 col10\" >-0.10</td>\n",
       "      <td id=\"T_232e0_row16_col11\" class=\"data row16 col11\" >-0.35</td>\n",
       "      <td id=\"T_232e0_row16_col12\" class=\"data row16 col12\" >-0.09</td>\n",
       "      <td id=\"T_232e0_row16_col13\" class=\"data row16 col13\" >-0.03</td>\n",
       "      <td id=\"T_232e0_row16_col14\" class=\"data row16 col14\" >-0.17</td>\n",
       "      <td id=\"T_232e0_row16_col15\" class=\"data row16 col15\" >0.98</td>\n",
       "      <td id=\"T_232e0_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
       "      <td id=\"T_232e0_row16_col17\" class=\"data row16 col17\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row16_col18\" class=\"data row16 col18\" >-0.64</td>\n",
       "      <td id=\"T_232e0_row16_col19\" class=\"data row16 col19\" >-0.67</td>\n",
       "      <td id=\"T_232e0_row16_col20\" class=\"data row16 col20\" >-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row17\" class=\"row_heading level0 row17\" >EPU I Lag_270</th>\n",
       "      <td id=\"T_232e0_row17_col0\" class=\"data row17 col0\" >0.44</td>\n",
       "      <td id=\"T_232e0_row17_col1\" class=\"data row17 col1\" >0.44</td>\n",
       "      <td id=\"T_232e0_row17_col2\" class=\"data row17 col2\" >-0.17</td>\n",
       "      <td id=\"T_232e0_row17_col3\" class=\"data row17 col3\" >0.42</td>\n",
       "      <td id=\"T_232e0_row17_col4\" class=\"data row17 col4\" >0.41</td>\n",
       "      <td id=\"T_232e0_row17_col5\" class=\"data row17 col5\" >-0.03</td>\n",
       "      <td id=\"T_232e0_row17_col6\" class=\"data row17 col6\" >0.29</td>\n",
       "      <td id=\"T_232e0_row17_col7\" class=\"data row17 col7\" >0.27</td>\n",
       "      <td id=\"T_232e0_row17_col8\" class=\"data row17 col8\" >0.13</td>\n",
       "      <td id=\"T_232e0_row17_col9\" class=\"data row17 col9\" >-0.05</td>\n",
       "      <td id=\"T_232e0_row17_col10\" class=\"data row17 col10\" >-0.06</td>\n",
       "      <td id=\"T_232e0_row17_col11\" class=\"data row17 col11\" >-0.08</td>\n",
       "      <td id=\"T_232e0_row17_col12\" class=\"data row17 col12\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row17_col13\" class=\"data row17 col13\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row17_col14\" class=\"data row17 col14\" >0.29</td>\n",
       "      <td id=\"T_232e0_row17_col15\" class=\"data row17 col15\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row17_col16\" class=\"data row17 col16\" >-0.15</td>\n",
       "      <td id=\"T_232e0_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
       "      <td id=\"T_232e0_row17_col18\" class=\"data row17 col18\" >0.16</td>\n",
       "      <td id=\"T_232e0_row17_col19\" class=\"data row17 col19\" >0.17</td>\n",
       "      <td id=\"T_232e0_row17_col20\" class=\"data row17 col20\" >-0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row18\" class=\"row_heading level0 row18\" >Crude Lag_360</th>\n",
       "      <td id=\"T_232e0_row18_col0\" class=\"data row18 col0\" >0.42</td>\n",
       "      <td id=\"T_232e0_row18_col1\" class=\"data row18 col1\" >0.44</td>\n",
       "      <td id=\"T_232e0_row18_col2\" class=\"data row18 col2\" >0.37</td>\n",
       "      <td id=\"T_232e0_row18_col3\" class=\"data row18 col3\" >0.52</td>\n",
       "      <td id=\"T_232e0_row18_col4\" class=\"data row18 col4\" >0.53</td>\n",
       "      <td id=\"T_232e0_row18_col5\" class=\"data row18 col5\" >0.48</td>\n",
       "      <td id=\"T_232e0_row18_col6\" class=\"data row18 col6\" >0.62</td>\n",
       "      <td id=\"T_232e0_row18_col7\" class=\"data row18 col7\" >0.62</td>\n",
       "      <td id=\"T_232e0_row18_col8\" class=\"data row18 col8\" >0.37</td>\n",
       "      <td id=\"T_232e0_row18_col9\" class=\"data row18 col9\" >-0.18</td>\n",
       "      <td id=\"T_232e0_row18_col10\" class=\"data row18 col10\" >-0.33</td>\n",
       "      <td id=\"T_232e0_row18_col11\" class=\"data row18 col11\" >0.15</td>\n",
       "      <td id=\"T_232e0_row18_col12\" class=\"data row18 col12\" >-0.57</td>\n",
       "      <td id=\"T_232e0_row18_col13\" class=\"data row18 col13\" >-0.54</td>\n",
       "      <td id=\"T_232e0_row18_col14\" class=\"data row18 col14\" >0.41</td>\n",
       "      <td id=\"T_232e0_row18_col15\" class=\"data row18 col15\" >-0.67</td>\n",
       "      <td id=\"T_232e0_row18_col16\" class=\"data row18 col16\" >-0.64</td>\n",
       "      <td id=\"T_232e0_row18_col17\" class=\"data row18 col17\" >0.16</td>\n",
       "      <td id=\"T_232e0_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
       "      <td id=\"T_232e0_row18_col19\" class=\"data row18 col19\" >0.98</td>\n",
       "      <td id=\"T_232e0_row18_col20\" class=\"data row18 col20\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row19\" class=\"row_heading level0 row19\" >Brent Lag_360</th>\n",
       "      <td id=\"T_232e0_row19_col0\" class=\"data row19 col0\" >0.43</td>\n",
       "      <td id=\"T_232e0_row19_col1\" class=\"data row19 col1\" >0.45</td>\n",
       "      <td id=\"T_232e0_row19_col2\" class=\"data row19 col2\" >0.40</td>\n",
       "      <td id=\"T_232e0_row19_col3\" class=\"data row19 col3\" >0.52</td>\n",
       "      <td id=\"T_232e0_row19_col4\" class=\"data row19 col4\" >0.53</td>\n",
       "      <td id=\"T_232e0_row19_col5\" class=\"data row19 col5\" >0.50</td>\n",
       "      <td id=\"T_232e0_row19_col6\" class=\"data row19 col6\" >0.62</td>\n",
       "      <td id=\"T_232e0_row19_col7\" class=\"data row19 col7\" >0.61</td>\n",
       "      <td id=\"T_232e0_row19_col8\" class=\"data row19 col8\" >0.38</td>\n",
       "      <td id=\"T_232e0_row19_col9\" class=\"data row19 col9\" >-0.13</td>\n",
       "      <td id=\"T_232e0_row19_col10\" class=\"data row19 col10\" >-0.30</td>\n",
       "      <td id=\"T_232e0_row19_col11\" class=\"data row19 col11\" >0.21</td>\n",
       "      <td id=\"T_232e0_row19_col12\" class=\"data row19 col12\" >-0.60</td>\n",
       "      <td id=\"T_232e0_row19_col13\" class=\"data row19 col13\" >-0.59</td>\n",
       "      <td id=\"T_232e0_row19_col14\" class=\"data row19 col14\" >0.38</td>\n",
       "      <td id=\"T_232e0_row19_col15\" class=\"data row19 col15\" >-0.70</td>\n",
       "      <td id=\"T_232e0_row19_col16\" class=\"data row19 col16\" >-0.67</td>\n",
       "      <td id=\"T_232e0_row19_col17\" class=\"data row19 col17\" >0.17</td>\n",
       "      <td id=\"T_232e0_row19_col18\" class=\"data row19 col18\" >0.98</td>\n",
       "      <td id=\"T_232e0_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
       "      <td id=\"T_232e0_row19_col20\" class=\"data row19 col20\" >-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_232e0_level0_row20\" class=\"row_heading level0 row20\" >EPU I Lag_360</th>\n",
       "      <td id=\"T_232e0_row20_col0\" class=\"data row20 col0\" >0.12</td>\n",
       "      <td id=\"T_232e0_row20_col1\" class=\"data row20 col1\" >0.12</td>\n",
       "      <td id=\"T_232e0_row20_col2\" class=\"data row20 col2\" >0.44</td>\n",
       "      <td id=\"T_232e0_row20_col3\" class=\"data row20 col3\" >0.09</td>\n",
       "      <td id=\"T_232e0_row20_col4\" class=\"data row20 col4\" >0.05</td>\n",
       "      <td id=\"T_232e0_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
       "      <td id=\"T_232e0_row20_col6\" class=\"data row20 col6\" >-0.32</td>\n",
       "      <td id=\"T_232e0_row20_col7\" class=\"data row20 col7\" >-0.32</td>\n",
       "      <td id=\"T_232e0_row20_col8\" class=\"data row20 col8\" >-0.63</td>\n",
       "      <td id=\"T_232e0_row20_col9\" class=\"data row20 col9\" >-0.23</td>\n",
       "      <td id=\"T_232e0_row20_col10\" class=\"data row20 col10\" >-0.18</td>\n",
       "      <td id=\"T_232e0_row20_col11\" class=\"data row20 col11\" >-0.26</td>\n",
       "      <td id=\"T_232e0_row20_col12\" class=\"data row20 col12\" >0.60</td>\n",
       "      <td id=\"T_232e0_row20_col13\" class=\"data row20 col13\" >0.60</td>\n",
       "      <td id=\"T_232e0_row20_col14\" class=\"data row20 col14\" >0.10</td>\n",
       "      <td id=\"T_232e0_row20_col15\" class=\"data row20 col15\" >-0.01</td>\n",
       "      <td id=\"T_232e0_row20_col16\" class=\"data row20 col16\" >-0.04</td>\n",
       "      <td id=\"T_232e0_row20_col17\" class=\"data row20 col17\" >-0.19</td>\n",
       "      <td id=\"T_232e0_row20_col18\" class=\"data row20 col18\" >-0.11</td>\n",
       "      <td id=\"T_232e0_row20_col19\" class=\"data row20 col19\" >-0.17</td>\n",
       "      <td id=\"T_232e0_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14fb684d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "data_vol = data.copy()\n",
    "data_vol['EPU Index'] = data['EPU Index'].pct_change().rolling(window=30).std()\n",
    "\n",
    "# Introducing lags for both columns\n",
    "for lag in lags:\n",
    "    for column in data.columns:\n",
    "        data_vol[f'{column[:5]} Lag_{lag}'] = data_vol[column].shift(lag)\n",
    "\n",
    "# Calculating the correlation matrix including the lags\n",
    "correlation_matrix = data_vol.corr()\n",
    "\n",
    "# Display the correlation matrix as a table\n",
    "styled_table = correlation_matrix.style.background_gradient(cmap='coolwarm', axis=None).format(\"{:.2f}\")\n",
    "title = \"#### (With Lags) Stock Price and EPU Index Volatility Correlation Heatmap\\nVolatility intended as 30-day StD.\"\n",
    "display(Markdown(title))\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "#### There seems to be a **moderate to strong inverse correlation** between Crude Oil prices and EPU Index Volatility (30d StD), peaking at around **60%** at 90-180 days of lag.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Stock Close Price Volatility and EPU Index Volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Stock Price Pct Daily Returns Volatility and EPU Index Volatility Correlation Heatmap\n",
       "Volatility intended as 90-day StD."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4627c_row0_col0, #T_4627c_row1_col1, #T_4627c_row2_col2 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4627c_row0_col1, #T_4627c_row1_col0 {\n",
       "  background-color: #b50927;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4627c_row0_col2, #T_4627c_row2_col0 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4627c_row1_col2, #T_4627c_row2_col1 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4627c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4627c_level0_col0\" class=\"col_heading level0 col0\" >Crude Oil Close</th>\n",
       "      <th id=\"T_4627c_level0_col1\" class=\"col_heading level0 col1\" >Brent Oil Close</th>\n",
       "      <th id=\"T_4627c_level0_col2\" class=\"col_heading level0 col2\" >EPU Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4627c_level0_row0\" class=\"row_heading level0 row0\" >Crude Oil Close</th>\n",
       "      <td id=\"T_4627c_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_4627c_row0_col1\" class=\"data row0 col1\" >0.99</td>\n",
       "      <td id=\"T_4627c_row0_col2\" class=\"data row0 col2\" >-0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4627c_level0_row1\" class=\"row_heading level0 row1\" >Brent Oil Close</th>\n",
       "      <td id=\"T_4627c_row1_col0\" class=\"data row1 col0\" >0.99</td>\n",
       "      <td id=\"T_4627c_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_4627c_row1_col2\" class=\"data row1 col2\" >-0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4627c_level0_row2\" class=\"row_heading level0 row2\" >EPU Index</th>\n",
       "      <td id=\"T_4627c_row2_col0\" class=\"data row2 col0\" >-0.73</td>\n",
       "      <td id=\"T_4627c_row2_col1\" class=\"data row2 col1\" >-0.70</td>\n",
       "      <td id=\"T_4627c_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14fac1e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = data.pct_change().rolling(window=90).std().corr()\n",
    "\n",
    "# Display the correlation matrix as a table\n",
    "styled_table = correlation_matrix.style.background_gradient(cmap='coolwarm', axis=None).format(\"{:.2f}\")\n",
    "title = \"#### Stock Price Pct Daily Returns Volatility and EPU Index Volatility Correlation Heatmap\\nVolatility intended as 90-day StD.\"\n",
    "display(Markdown(title))\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### (With Lags) Stock Price Pct Daily Returns Volatility and EPU Index Volatility Correlation Heatmap\n",
       "Volatility intended as 90-day StD."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col1, #T_30c39_row0_col3, #T_30c39_row0_col4, #T_30c39_row1_col0, #T_30c39_row1_col4, #T_30c39_row2_col5, #T_30c39_row3_col0, #T_30c39_row3_col4, #T_30c39_row4_col0, #T_30c39_row4_col1, #T_30c39_row4_col3, #T_30c39_row5_col2, #T_30c39_row6_col7, #T_30c39_row7_col6, #T_30c39_row9_col10, #T_30c39_row10_col9, #T_30c39_row12_col13, #T_30c39_row13_col12, #T_30c39_row15_col16, #T_30c39_row16_col15 {\n",
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col6, #T_30c39_row1_col7, #T_30c39_row6_col0, #T_30c39_row7_col1, #T_30c39_row7_col10, #T_30c39_row10_col7, #T_30c39_row13_col16, #T_30c39_row16_col13 {\n",
       "  background-color: #c43032;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col7, #T_30c39_row3_col6, #T_30c39_row6_col3, #T_30c39_row7_col0, #T_30c39_row18_col19, #T_30c39_row19_col18 {\n",
       "  background-color: #c12b30;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col8, #T_30c39_row0_col17, #T_30c39_row1_col2, #T_30c39_row2_col1, #T_30c39_row8_col0, #T_30c39_row8_col10, #T_30c39_row10_col8, #T_30c39_row15_col17, #T_30c39_row17_col0, #T_30c39_row17_col15 {\n",
       "  background-color: #5f7fe8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col9, #T_30c39_row9_col0, #T_30c39_row12_col15, #T_30c39_row15_col12 {\n",
       "  background-color: #cd423b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col10, #T_30c39_row2_col11, #T_30c39_row10_col0, #T_30c39_row11_col2, #T_30c39_row13_col15, #T_30c39_row15_col13 {\n",
       "  background-color: #ca3b37;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col11, #T_30c39_row9_col14, #T_30c39_row11_col0, #T_30c39_row14_col9 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col12, #T_30c39_row12_col0 {\n",
       "  background-color: #ed8366;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col13, #T_30c39_row1_col13, #T_30c39_row4_col12, #T_30c39_row4_col15, #T_30c39_row12_col4, #T_30c39_row13_col0, #T_30c39_row13_col1, #T_30c39_row15_col4, #T_30c39_row15_col19, #T_30c39_row19_col15 {\n",
       "  background-color: #eb7d62;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col14, #T_30c39_row14_col0 {\n",
       "  background-color: #8fb1fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row0_col15, #T_30c39_row15_col0 {\n",
       "  background-color: #de614d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col16, #T_30c39_row16_col0 {\n",
       "  background-color: #e16751;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row0_col18, #T_30c39_row1_col18, #T_30c39_row18_col0, #T_30c39_row18_col1 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row0_col19, #T_30c39_row1_col14, #T_30c39_row14_col1, #T_30c39_row19_col0 {\n",
       "  background-color: #9fbfff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row0_col20, #T_30c39_row1_col20, #T_30c39_row4_col18, #T_30c39_row18_col4, #T_30c39_row20_col0, #T_30c39_row20_col1 {\n",
       "  background-color: #bed2f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row1_col3, #T_30c39_row3_col1 {\n",
       "  background-color: #b8122a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col5, #T_30c39_row5_col1, #T_30c39_row16_col17, #T_30c39_row17_col16 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col6, #T_30c39_row5_col11, #T_30c39_row6_col1, #T_30c39_row6_col10, #T_30c39_row7_col9, #T_30c39_row9_col7, #T_30c39_row10_col6, #T_30c39_row11_col5 {\n",
       "  background-color: #c83836;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col8, #T_30c39_row3_col11, #T_30c39_row8_col1, #T_30c39_row11_col3 {\n",
       "  background-color: #6788ee;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col9, #T_30c39_row3_col9, #T_30c39_row6_col9, #T_30c39_row9_col1, #T_30c39_row9_col3, #T_30c39_row9_col6, #T_30c39_row10_col12, #T_30c39_row12_col10 {\n",
       "  background-color: #cc403a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col10, #T_30c39_row4_col9, #T_30c39_row9_col4, #T_30c39_row9_col13, #T_30c39_row10_col1, #T_30c39_row13_col9 {\n",
       "  background-color: #cb3e38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col11, #T_30c39_row7_col17, #T_30c39_row11_col1, #T_30c39_row17_col7 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col12, #T_30c39_row12_col1 {\n",
       "  background-color: #ef886b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col15, #T_30c39_row15_col1 {\n",
       "  background-color: #f39475;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row1_col16, #T_30c39_row16_col1 {\n",
       "  background-color: #f59c7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row1_col17, #T_30c39_row17_col1 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row1_col19, #T_30c39_row19_col1 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row2_col3, #T_30c39_row3_col2, #T_30c39_row3_col5, #T_30c39_row5_col3, #T_30c39_row5_col7, #T_30c39_row7_col5, #T_30c39_row11_col13, #T_30c39_row13_col11 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col4, #T_30c39_row4_col2 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col6, #T_30c39_row5_col9, #T_30c39_row6_col2, #T_30c39_row9_col5 {\n",
       "  background-color: #5470de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col7, #T_30c39_row5_col10, #T_30c39_row6_col8, #T_30c39_row7_col2, #T_30c39_row8_col6, #T_30c39_row10_col5 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col8, #T_30c39_row8_col2 {\n",
       "  background-color: #bb1b2c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col9, #T_30c39_row9_col2, #T_30c39_row11_col15, #T_30c39_row15_col11, #T_30c39_row18_col20, #T_30c39_row20_col18 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col10, #T_30c39_row5_col6, #T_30c39_row6_col5, #T_30c39_row10_col2 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col12, #T_30c39_row8_col16, #T_30c39_row12_col2, #T_30c39_row16_col8 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col13, #T_30c39_row5_col12, #T_30c39_row12_col5, #T_30c39_row13_col2 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col14, #T_30c39_row6_col12, #T_30c39_row12_col6, #T_30c39_row14_col2, #T_30c39_row17_col19, #T_30c39_row19_col17 {\n",
       "  background-color: #d55042;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col15, #T_30c39_row15_col2 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col16, #T_30c39_row8_col13, #T_30c39_row13_col8, #T_30c39_row16_col2 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row2_col17, #T_30c39_row17_col2 {\n",
       "  background-color: #f7a688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row2_col18, #T_30c39_row18_col2 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row2_col19, #T_30c39_row19_col2 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row2_col20, #T_30c39_row20_col2 {\n",
       "  background-color: #c0d4f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row3_col7, #T_30c39_row7_col3 {\n",
       "  background-color: #be242e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col8, #T_30c39_row4_col5, #T_30c39_row5_col4, #T_30c39_row8_col3, #T_30c39_row8_col9, #T_30c39_row9_col8, #T_30c39_row10_col14, #T_30c39_row14_col10 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col10, #T_30c39_row4_col10, #T_30c39_row10_col3, #T_30c39_row10_col4, #T_30c39_row10_col13, #T_30c39_row13_col10 {\n",
       "  background-color: #c73635;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col12, #T_30c39_row12_col3 {\n",
       "  background-color: #e9785d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col13, #T_30c39_row4_col13, #T_30c39_row13_col3, #T_30c39_row13_col4 {\n",
       "  background-color: #e8765c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col14, #T_30c39_row14_col3 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col15, #T_30c39_row7_col12, #T_30c39_row12_col7, #T_30c39_row15_col3 {\n",
       "  background-color: #d65244;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col16, #T_30c39_row6_col13, #T_30c39_row7_col13, #T_30c39_row13_col6, #T_30c39_row13_col7, #T_30c39_row16_col3 {\n",
       "  background-color: #d85646;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row3_col18, #T_30c39_row18_col3 {\n",
       "  background-color: #c1d4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row3_col19, #T_30c39_row4_col19, #T_30c39_row7_col19, #T_30c39_row19_col3, #T_30c39_row19_col4, #T_30c39_row19_col7 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row3_col20, #T_30c39_row16_col20, #T_30c39_row20_col3, #T_30c39_row20_col16 {\n",
       "  background-color: #cbd8ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row4_col6, #T_30c39_row6_col4, #T_30c39_row12_col16, #T_30c39_row16_col12 {\n",
       "  background-color: #c53334;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row4_col7, #T_30c39_row7_col4 {\n",
       "  background-color: #c0282f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row4_col8, #T_30c39_row4_col17, #T_30c39_row8_col4, #T_30c39_row10_col11, #T_30c39_row11_col10, #T_30c39_row17_col4 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row4_col11, #T_30c39_row11_col4, #T_30c39_row12_col17, #T_30c39_row17_col12 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row4_col14, #T_30c39_row14_col4 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row4_col16, #T_30c39_row10_col19, #T_30c39_row16_col4, #T_30c39_row19_col10 {\n",
       "  background-color: #ee8669;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row4_col20, #T_30c39_row10_col20, #T_30c39_row12_col19, #T_30c39_row19_col12, #T_30c39_row20_col4, #T_30c39_row20_col10 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row5_col8, #T_30c39_row8_col5 {\n",
       "  background-color: #ba162b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row5_col13, #T_30c39_row8_col15, #T_30c39_row13_col5, #T_30c39_row15_col8 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row5_col14, #T_30c39_row8_col14, #T_30c39_row9_col12, #T_30c39_row12_col9, #T_30c39_row14_col5, #T_30c39_row14_col8 {\n",
       "  background-color: #d44e41;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row5_col15, #T_30c39_row15_col5 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row5_col16, #T_30c39_row8_col12, #T_30c39_row12_col8, #T_30c39_row16_col5 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row5_col17, #T_30c39_row17_col5 {\n",
       "  background-color: #f59d7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row5_col18, #T_30c39_row18_col5 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row5_col19, #T_30c39_row19_col5 {\n",
       "  background-color: #92b4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row5_col20, #T_30c39_row20_col5 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row6_col14, #T_30c39_row11_col16, #T_30c39_row14_col6, #T_30c39_row16_col11 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row6_col15, #T_30c39_row6_col16, #T_30c39_row7_col16, #T_30c39_row15_col6, #T_30c39_row16_col6, #T_30c39_row16_col7 {\n",
       "  background-color: #e0654f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row6_col17, #T_30c39_row17_col6 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row6_col18, #T_30c39_row18_col6 {\n",
       "  background-color: #bbd1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row6_col19, #T_30c39_row19_col6 {\n",
       "  background-color: #9ebeff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row6_col20, #T_30c39_row20_col6 {\n",
       "  background-color: #ebd3c6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row7_col14, #T_30c39_row11_col12, #T_30c39_row12_col11, #T_30c39_row14_col7 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row7_col15, #T_30c39_row15_col7 {\n",
       "  background-color: #df634e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row7_col18, #T_30c39_row8_col20, #T_30c39_row18_col7, #T_30c39_row20_col8 {\n",
       "  background-color: #b7cff9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row7_col20, #T_30c39_row9_col15, #T_30c39_row15_col9, #T_30c39_row20_col7 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row8_col11, #T_30c39_row11_col8 {\n",
       "  background-color: #c32e31;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row8_col17, #T_30c39_row17_col8 {\n",
       "  background-color: #f08b6e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row8_col18, #T_30c39_row18_col8 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row8_col19, #T_30c39_row12_col18, #T_30c39_row18_col12, #T_30c39_row19_col8 {\n",
       "  background-color: #c6d6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row9_col11, #T_30c39_row11_col9, #T_30c39_row14_col15, #T_30c39_row15_col14 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row9_col16, #T_30c39_row16_col9 {\n",
       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row9_col17, #T_30c39_row17_col9 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row9_col18, #T_30c39_row18_col9 {\n",
       "  background-color: #f5c1a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row9_col19, #T_30c39_row19_col9 {\n",
       "  background-color: #f18d6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row9_col20, #T_30c39_row20_col9 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row10_col15, #T_30c39_row15_col10 {\n",
       "  background-color: #f6a385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row10_col16, #T_30c39_row16_col10 {\n",
       "  background-color: #f7a98b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row10_col17, #T_30c39_row17_col10 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row10_col18, #T_30c39_row12_col20, #T_30c39_row18_col10, #T_30c39_row20_col12 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row11_col14, #T_30c39_row14_col11 {\n",
       "  background-color: #da5a49;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row11_col17, #T_30c39_row17_col11 {\n",
       "  background-color: #f7b497;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row11_col18, #T_30c39_row18_col11 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row11_col19, #T_30c39_row19_col11 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row11_col20, #T_30c39_row20_col11 {\n",
       "  background-color: #a6c4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row12_col14, #T_30c39_row14_col12 {\n",
       "  background-color: #445acc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row13_col14, #T_30c39_row14_col13 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row13_col17, #T_30c39_row17_col13 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row13_col18, #T_30c39_row18_col13 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row13_col19, #T_30c39_row19_col13 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row13_col20, #T_30c39_row20_col13 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row14_col16, #T_30c39_row16_col14, #T_30c39_row19_col20, #T_30c39_row20_col19 {\n",
       "  background-color: #6282ea;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row14_col17, #T_30c39_row17_col14 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row14_col18, #T_30c39_row18_col14 {\n",
       "  background-color: #84a7fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row14_col19, #T_30c39_row19_col14 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row14_col20, #T_30c39_row20_col14 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row15_col18, #T_30c39_row18_col15 {\n",
       "  background-color: #f7b79b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row15_col20, #T_30c39_row20_col15 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row16_col18, #T_30c39_row18_col16 {\n",
       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row16_col19, #T_30c39_row19_col16 {\n",
       "  background-color: #f39577;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30c39_row17_col18, #T_30c39_row18_col17 {\n",
       "  background-color: #e36c55;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30c39_row17_col20, #T_30c39_row20_col17 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_30c39\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_30c39_level0_col0\" class=\"col_heading level0 col0\" >Crude Oil Close</th>\n",
       "      <th id=\"T_30c39_level0_col1\" class=\"col_heading level0 col1\" >Brent Oil Close</th>\n",
       "      <th id=\"T_30c39_level0_col2\" class=\"col_heading level0 col2\" >EPU Index</th>\n",
       "      <th id=\"T_30c39_level0_col3\" class=\"col_heading level0 col3\" >Crude Lag_5</th>\n",
       "      <th id=\"T_30c39_level0_col4\" class=\"col_heading level0 col4\" >Brent Lag_5</th>\n",
       "      <th id=\"T_30c39_level0_col5\" class=\"col_heading level0 col5\" >EPU I Lag_5</th>\n",
       "      <th id=\"T_30c39_level0_col6\" class=\"col_heading level0 col6\" >Crude Lag_30</th>\n",
       "      <th id=\"T_30c39_level0_col7\" class=\"col_heading level0 col7\" >Brent Lag_30</th>\n",
       "      <th id=\"T_30c39_level0_col8\" class=\"col_heading level0 col8\" >EPU I Lag_30</th>\n",
       "      <th id=\"T_30c39_level0_col9\" class=\"col_heading level0 col9\" >Crude Lag_90</th>\n",
       "      <th id=\"T_30c39_level0_col10\" class=\"col_heading level0 col10\" >Brent Lag_90</th>\n",
       "      <th id=\"T_30c39_level0_col11\" class=\"col_heading level0 col11\" >EPU I Lag_90</th>\n",
       "      <th id=\"T_30c39_level0_col12\" class=\"col_heading level0 col12\" >Crude Lag_180</th>\n",
       "      <th id=\"T_30c39_level0_col13\" class=\"col_heading level0 col13\" >Brent Lag_180</th>\n",
       "      <th id=\"T_30c39_level0_col14\" class=\"col_heading level0 col14\" >EPU I Lag_180</th>\n",
       "      <th id=\"T_30c39_level0_col15\" class=\"col_heading level0 col15\" >Crude Lag_270</th>\n",
       "      <th id=\"T_30c39_level0_col16\" class=\"col_heading level0 col16\" >Brent Lag_270</th>\n",
       "      <th id=\"T_30c39_level0_col17\" class=\"col_heading level0 col17\" >EPU I Lag_270</th>\n",
       "      <th id=\"T_30c39_level0_col18\" class=\"col_heading level0 col18\" >Crude Lag_360</th>\n",
       "      <th id=\"T_30c39_level0_col19\" class=\"col_heading level0 col19\" >Brent Lag_360</th>\n",
       "      <th id=\"T_30c39_level0_col20\" class=\"col_heading level0 col20\" >EPU I Lag_360</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row0\" class=\"row_heading level0 row0\" >Crude Oil Close</th>\n",
       "      <td id=\"T_30c39_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_30c39_row0_col1\" class=\"data row0 col1\" >0.99</td>\n",
       "      <td id=\"T_30c39_row0_col2\" class=\"data row0 col2\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row0_col3\" class=\"data row0 col3\" >0.99</td>\n",
       "      <td id=\"T_30c39_row0_col4\" class=\"data row0 col4\" >0.99</td>\n",
       "      <td id=\"T_30c39_row0_col5\" class=\"data row0 col5\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row0_col6\" class=\"data row0 col6\" >0.91</td>\n",
       "      <td id=\"T_30c39_row0_col7\" class=\"data row0 col7\" >0.93</td>\n",
       "      <td id=\"T_30c39_row0_col8\" class=\"data row0 col8\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row0_col9\" class=\"data row0 col9\" >0.86</td>\n",
       "      <td id=\"T_30c39_row0_col10\" class=\"data row0 col10\" >0.88</td>\n",
       "      <td id=\"T_30c39_row0_col11\" class=\"data row0 col11\" >-0.63</td>\n",
       "      <td id=\"T_30c39_row0_col12\" class=\"data row0 col12\" >0.62</td>\n",
       "      <td id=\"T_30c39_row0_col13\" class=\"data row0 col13\" >0.64</td>\n",
       "      <td id=\"T_30c39_row0_col14\" class=\"data row0 col14\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row0_col15\" class=\"data row0 col15\" >0.75</td>\n",
       "      <td id=\"T_30c39_row0_col16\" class=\"data row0 col16\" >0.73</td>\n",
       "      <td id=\"T_30c39_row0_col17\" class=\"data row0 col17\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row0_col18\" class=\"data row0 col18\" >-0.10</td>\n",
       "      <td id=\"T_30c39_row0_col19\" class=\"data row0 col19\" >-0.34</td>\n",
       "      <td id=\"T_30c39_row0_col20\" class=\"data row0 col20\" >-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row1\" class=\"row_heading level0 row1\" >Brent Oil Close</th>\n",
       "      <td id=\"T_30c39_row1_col0\" class=\"data row1 col0\" >0.99</td>\n",
       "      <td id=\"T_30c39_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_30c39_row1_col2\" class=\"data row1 col2\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row1_col3\" class=\"data row1 col3\" >0.98</td>\n",
       "      <td id=\"T_30c39_row1_col4\" class=\"data row1 col4\" >0.99</td>\n",
       "      <td id=\"T_30c39_row1_col5\" class=\"data row1 col5\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row1_col6\" class=\"data row1 col6\" >0.89</td>\n",
       "      <td id=\"T_30c39_row1_col7\" class=\"data row1 col7\" >0.92</td>\n",
       "      <td id=\"T_30c39_row1_col8\" class=\"data row1 col8\" >-0.65</td>\n",
       "      <td id=\"T_30c39_row1_col9\" class=\"data row1 col9\" >0.87</td>\n",
       "      <td id=\"T_30c39_row1_col10\" class=\"data row1 col10\" >0.88</td>\n",
       "      <td id=\"T_30c39_row1_col11\" class=\"data row1 col11\" >-0.57</td>\n",
       "      <td id=\"T_30c39_row1_col12\" class=\"data row1 col12\" >0.60</td>\n",
       "      <td id=\"T_30c39_row1_col13\" class=\"data row1 col13\" >0.64</td>\n",
       "      <td id=\"T_30c39_row1_col14\" class=\"data row1 col14\" >-0.34</td>\n",
       "      <td id=\"T_30c39_row1_col15\" class=\"data row1 col15\" >0.55</td>\n",
       "      <td id=\"T_30c39_row1_col16\" class=\"data row1 col16\" >0.51</td>\n",
       "      <td id=\"T_30c39_row1_col17\" class=\"data row1 col17\" >-0.61</td>\n",
       "      <td id=\"T_30c39_row1_col18\" class=\"data row1 col18\" >-0.10</td>\n",
       "      <td id=\"T_30c39_row1_col19\" class=\"data row1 col19\" >-0.34</td>\n",
       "      <td id=\"T_30c39_row1_col20\" class=\"data row1 col20\" >-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row2\" class=\"row_heading level0 row2\" >EPU Index</th>\n",
       "      <td id=\"T_30c39_row2_col0\" class=\"data row2 col0\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row2_col1\" class=\"data row2 col1\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "      <td id=\"T_30c39_row2_col3\" class=\"data row2 col3\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row2_col4\" class=\"data row2 col4\" >-0.71</td>\n",
       "      <td id=\"T_30c39_row2_col5\" class=\"data row2 col5\" >0.99</td>\n",
       "      <td id=\"T_30c39_row2_col6\" class=\"data row2 col6\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row2_col7\" class=\"data row2 col7\" >-0.75</td>\n",
       "      <td id=\"T_30c39_row2_col8\" class=\"data row2 col8\" >0.96</td>\n",
       "      <td id=\"T_30c39_row2_col9\" class=\"data row2 col9\" >-0.78</td>\n",
       "      <td id=\"T_30c39_row2_col10\" class=\"data row2 col10\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row2_col11\" class=\"data row2 col11\" >0.88</td>\n",
       "      <td id=\"T_30c39_row2_col12\" class=\"data row2 col12\" >-0.92</td>\n",
       "      <td id=\"T_30c39_row2_col13\" class=\"data row2 col13\" >-0.91</td>\n",
       "      <td id=\"T_30c39_row2_col14\" class=\"data row2 col14\" >0.81</td>\n",
       "      <td id=\"T_30c39_row2_col15\" class=\"data row2 col15\" >-0.83</td>\n",
       "      <td id=\"T_30c39_row2_col16\" class=\"data row2 col16\" >-0.87</td>\n",
       "      <td id=\"T_30c39_row2_col17\" class=\"data row2 col17\" >0.45</td>\n",
       "      <td id=\"T_30c39_row2_col18\" class=\"data row2 col18\" >0.02</td>\n",
       "      <td id=\"T_30c39_row2_col19\" class=\"data row2 col19\" >-0.21</td>\n",
       "      <td id=\"T_30c39_row2_col20\" class=\"data row2 col20\" >-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row3\" class=\"row_heading level0 row3\" >Crude Lag_5</th>\n",
       "      <td id=\"T_30c39_row3_col0\" class=\"data row3 col0\" >0.99</td>\n",
       "      <td id=\"T_30c39_row3_col1\" class=\"data row3 col1\" >0.98</td>\n",
       "      <td id=\"T_30c39_row3_col2\" class=\"data row3 col2\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "      <td id=\"T_30c39_row3_col4\" class=\"data row3 col4\" >0.99</td>\n",
       "      <td id=\"T_30c39_row3_col5\" class=\"data row3 col5\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row3_col6\" class=\"data row3 col6\" >0.93</td>\n",
       "      <td id=\"T_30c39_row3_col7\" class=\"data row3 col7\" >0.94</td>\n",
       "      <td id=\"T_30c39_row3_col8\" class=\"data row3 col8\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row3_col9\" class=\"data row3 col9\" >0.87</td>\n",
       "      <td id=\"T_30c39_row3_col10\" class=\"data row3 col10\" >0.90</td>\n",
       "      <td id=\"T_30c39_row3_col11\" class=\"data row3 col11\" >-0.65</td>\n",
       "      <td id=\"T_30c39_row3_col12\" class=\"data row3 col12\" >0.66</td>\n",
       "      <td id=\"T_30c39_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
       "      <td id=\"T_30c39_row3_col14\" class=\"data row3 col14\" >-0.53</td>\n",
       "      <td id=\"T_30c39_row3_col15\" class=\"data row3 col15\" >0.81</td>\n",
       "      <td id=\"T_30c39_row3_col16\" class=\"data row3 col16\" >0.79</td>\n",
       "      <td id=\"T_30c39_row3_col17\" class=\"data row3 col17\" >-0.72</td>\n",
       "      <td id=\"T_30c39_row3_col18\" class=\"data row3 col18\" >-0.15</td>\n",
       "      <td id=\"T_30c39_row3_col19\" class=\"data row3 col19\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row3_col20\" class=\"data row3 col20\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row4\" class=\"row_heading level0 row4\" >Brent Lag_5</th>\n",
       "      <td id=\"T_30c39_row4_col0\" class=\"data row4 col0\" >0.99</td>\n",
       "      <td id=\"T_30c39_row4_col1\" class=\"data row4 col1\" >0.99</td>\n",
       "      <td id=\"T_30c39_row4_col2\" class=\"data row4 col2\" >-0.71</td>\n",
       "      <td id=\"T_30c39_row4_col3\" class=\"data row4 col3\" >0.99</td>\n",
       "      <td id=\"T_30c39_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "      <td id=\"T_30c39_row4_col5\" class=\"data row4 col5\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row4_col6\" class=\"data row4 col6\" >0.91</td>\n",
       "      <td id=\"T_30c39_row4_col7\" class=\"data row4 col7\" >0.93</td>\n",
       "      <td id=\"T_30c39_row4_col8\" class=\"data row4 col8\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row4_col9\" class=\"data row4 col9\" >0.88</td>\n",
       "      <td id=\"T_30c39_row4_col10\" class=\"data row4 col10\" >0.90</td>\n",
       "      <td id=\"T_30c39_row4_col11\" class=\"data row4 col11\" >-0.61</td>\n",
       "      <td id=\"T_30c39_row4_col12\" class=\"data row4 col12\" >0.65</td>\n",
       "      <td id=\"T_30c39_row4_col13\" class=\"data row4 col13\" >0.67</td>\n",
       "      <td id=\"T_30c39_row4_col14\" class=\"data row4 col14\" >-0.45</td>\n",
       "      <td id=\"T_30c39_row4_col15\" class=\"data row4 col15\" >0.64</td>\n",
       "      <td id=\"T_30c39_row4_col16\" class=\"data row4 col16\" >0.61</td>\n",
       "      <td id=\"T_30c39_row4_col17\" class=\"data row4 col17\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row4_col18\" class=\"data row4 col18\" >-0.17</td>\n",
       "      <td id=\"T_30c39_row4_col19\" class=\"data row4 col19\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row4_col20\" class=\"data row4 col20\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row5\" class=\"row_heading level0 row5\" >EPU I Lag_5</th>\n",
       "      <td id=\"T_30c39_row5_col0\" class=\"data row5 col0\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row5_col1\" class=\"data row5 col1\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row5_col2\" class=\"data row5 col2\" >0.99</td>\n",
       "      <td id=\"T_30c39_row5_col3\" class=\"data row5 col3\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row5_col4\" class=\"data row5 col4\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "      <td id=\"T_30c39_row5_col6\" class=\"data row5 col6\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row5_col7\" class=\"data row5 col7\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row5_col8\" class=\"data row5 col8\" >0.96</td>\n",
       "      <td id=\"T_30c39_row5_col9\" class=\"data row5 col9\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row5_col10\" class=\"data row5 col10\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row5_col11\" class=\"data row5 col11\" >0.89</td>\n",
       "      <td id=\"T_30c39_row5_col12\" class=\"data row5 col12\" >-0.92</td>\n",
       "      <td id=\"T_30c39_row5_col13\" class=\"data row5 col13\" >-0.90</td>\n",
       "      <td id=\"T_30c39_row5_col14\" class=\"data row5 col14\" >0.82</td>\n",
       "      <td id=\"T_30c39_row5_col15\" class=\"data row5 col15\" >-0.85</td>\n",
       "      <td id=\"T_30c39_row5_col16\" class=\"data row5 col16\" >-0.88</td>\n",
       "      <td id=\"T_30c39_row5_col17\" class=\"data row5 col17\" >0.50</td>\n",
       "      <td id=\"T_30c39_row5_col18\" class=\"data row5 col18\" >-0.20</td>\n",
       "      <td id=\"T_30c39_row5_col19\" class=\"data row5 col19\" >-0.42</td>\n",
       "      <td id=\"T_30c39_row5_col20\" class=\"data row5 col20\" >-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row6\" class=\"row_heading level0 row6\" >Crude Lag_30</th>\n",
       "      <td id=\"T_30c39_row6_col0\" class=\"data row6 col0\" >0.91</td>\n",
       "      <td id=\"T_30c39_row6_col1\" class=\"data row6 col1\" >0.89</td>\n",
       "      <td id=\"T_30c39_row6_col2\" class=\"data row6 col2\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row6_col3\" class=\"data row6 col3\" >0.93</td>\n",
       "      <td id=\"T_30c39_row6_col4\" class=\"data row6 col4\" >0.91</td>\n",
       "      <td id=\"T_30c39_row6_col5\" class=\"data row6 col5\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "      <td id=\"T_30c39_row6_col7\" class=\"data row6 col7\" >0.99</td>\n",
       "      <td id=\"T_30c39_row6_col8\" class=\"data row6 col8\" >-0.75</td>\n",
       "      <td id=\"T_30c39_row6_col9\" class=\"data row6 col9\" >0.86</td>\n",
       "      <td id=\"T_30c39_row6_col10\" class=\"data row6 col10\" >0.89</td>\n",
       "      <td id=\"T_30c39_row6_col11\" class=\"data row6 col11\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row6_col12\" class=\"data row6 col12\" >0.82</td>\n",
       "      <td id=\"T_30c39_row6_col13\" class=\"data row6 col13\" >0.79</td>\n",
       "      <td id=\"T_30c39_row6_col14\" class=\"data row6 col14\" >-0.81</td>\n",
       "      <td id=\"T_30c39_row6_col15\" class=\"data row6 col15\" >0.74</td>\n",
       "      <td id=\"T_30c39_row6_col16\" class=\"data row6 col16\" >0.74</td>\n",
       "      <td id=\"T_30c39_row6_col17\" class=\"data row6 col17\" >-0.54</td>\n",
       "      <td id=\"T_30c39_row6_col18\" class=\"data row6 col18\" >-0.19</td>\n",
       "      <td id=\"T_30c39_row6_col19\" class=\"data row6 col19\" >-0.35</td>\n",
       "      <td id=\"T_30c39_row6_col20\" class=\"data row6 col20\" >0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row7\" class=\"row_heading level0 row7\" >Brent Lag_30</th>\n",
       "      <td id=\"T_30c39_row7_col0\" class=\"data row7 col0\" >0.93</td>\n",
       "      <td id=\"T_30c39_row7_col1\" class=\"data row7 col1\" >0.92</td>\n",
       "      <td id=\"T_30c39_row7_col2\" class=\"data row7 col2\" >-0.75</td>\n",
       "      <td id=\"T_30c39_row7_col3\" class=\"data row7 col3\" >0.94</td>\n",
       "      <td id=\"T_30c39_row7_col4\" class=\"data row7 col4\" >0.93</td>\n",
       "      <td id=\"T_30c39_row7_col5\" class=\"data row7 col5\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row7_col6\" class=\"data row7 col6\" >0.99</td>\n",
       "      <td id=\"T_30c39_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "      <td id=\"T_30c39_row7_col8\" class=\"data row7 col8\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row7_col9\" class=\"data row7 col9\" >0.89</td>\n",
       "      <td id=\"T_30c39_row7_col10\" class=\"data row7 col10\" >0.91</td>\n",
       "      <td id=\"T_30c39_row7_col11\" class=\"data row7 col11\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row7_col12\" class=\"data row7 col12\" >0.81</td>\n",
       "      <td id=\"T_30c39_row7_col13\" class=\"data row7 col13\" >0.79</td>\n",
       "      <td id=\"T_30c39_row7_col14\" class=\"data row7 col14\" >-0.80</td>\n",
       "      <td id=\"T_30c39_row7_col15\" class=\"data row7 col15\" >0.75</td>\n",
       "      <td id=\"T_30c39_row7_col16\" class=\"data row7 col16\" >0.74</td>\n",
       "      <td id=\"T_30c39_row7_col17\" class=\"data row7 col17\" >-0.58</td>\n",
       "      <td id=\"T_30c39_row7_col18\" class=\"data row7 col18\" >-0.21</td>\n",
       "      <td id=\"T_30c39_row7_col19\" class=\"data row7 col19\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row7_col20\" class=\"data row7 col20\" >0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row8\" class=\"row_heading level0 row8\" >EPU I Lag_30</th>\n",
       "      <td id=\"T_30c39_row8_col0\" class=\"data row8 col0\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row8_col1\" class=\"data row8 col1\" >-0.65</td>\n",
       "      <td id=\"T_30c39_row8_col2\" class=\"data row8 col2\" >0.96</td>\n",
       "      <td id=\"T_30c39_row8_col3\" class=\"data row8 col3\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row8_col4\" class=\"data row8 col4\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row8_col5\" class=\"data row8 col5\" >0.96</td>\n",
       "      <td id=\"T_30c39_row8_col6\" class=\"data row8 col6\" >-0.75</td>\n",
       "      <td id=\"T_30c39_row8_col7\" class=\"data row8 col7\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
       "      <td id=\"T_30c39_row8_col9\" class=\"data row8 col9\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row8_col10\" class=\"data row8 col10\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row8_col11\" class=\"data row8 col11\" >0.92</td>\n",
       "      <td id=\"T_30c39_row8_col12\" class=\"data row8 col12\" >-0.89</td>\n",
       "      <td id=\"T_30c39_row8_col13\" class=\"data row8 col13\" >-0.87</td>\n",
       "      <td id=\"T_30c39_row8_col14\" class=\"data row8 col14\" >0.82</td>\n",
       "      <td id=\"T_30c39_row8_col15\" class=\"data row8 col15\" >-0.90</td>\n",
       "      <td id=\"T_30c39_row8_col16\" class=\"data row8 col16\" >-0.92</td>\n",
       "      <td id=\"T_30c39_row8_col17\" class=\"data row8 col17\" >0.58</td>\n",
       "      <td id=\"T_30c39_row8_col18\" class=\"data row8 col18\" >-0.04</td>\n",
       "      <td id=\"T_30c39_row8_col19\" class=\"data row8 col19\" >-0.11</td>\n",
       "      <td id=\"T_30c39_row8_col20\" class=\"data row8 col20\" >-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row9\" class=\"row_heading level0 row9\" >Crude Lag_90</th>\n",
       "      <td id=\"T_30c39_row9_col0\" class=\"data row9 col0\" >0.86</td>\n",
       "      <td id=\"T_30c39_row9_col1\" class=\"data row9 col1\" >0.87</td>\n",
       "      <td id=\"T_30c39_row9_col2\" class=\"data row9 col2\" >-0.78</td>\n",
       "      <td id=\"T_30c39_row9_col3\" class=\"data row9 col3\" >0.87</td>\n",
       "      <td id=\"T_30c39_row9_col4\" class=\"data row9 col4\" >0.88</td>\n",
       "      <td id=\"T_30c39_row9_col5\" class=\"data row9 col5\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row9_col6\" class=\"data row9 col6\" >0.86</td>\n",
       "      <td id=\"T_30c39_row9_col7\" class=\"data row9 col7\" >0.89</td>\n",
       "      <td id=\"T_30c39_row9_col8\" class=\"data row9 col8\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
       "      <td id=\"T_30c39_row9_col10\" class=\"data row9 col10\" >0.99</td>\n",
       "      <td id=\"T_30c39_row9_col11\" class=\"data row9 col11\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row9_col12\" class=\"data row9 col12\" >0.83</td>\n",
       "      <td id=\"T_30c39_row9_col13\" class=\"data row9 col13\" >0.87</td>\n",
       "      <td id=\"T_30c39_row9_col14\" class=\"data row9 col14\" >-0.63</td>\n",
       "      <td id=\"T_30c39_row9_col15\" class=\"data row9 col15\" >0.06</td>\n",
       "      <td id=\"T_30c39_row9_col16\" class=\"data row9 col16\" >0.05</td>\n",
       "      <td id=\"T_30c39_row9_col17\" class=\"data row9 col17\" >0.33</td>\n",
       "      <td id=\"T_30c39_row9_col18\" class=\"data row9 col18\" >0.30</td>\n",
       "      <td id=\"T_30c39_row9_col19\" class=\"data row9 col19\" >0.58</td>\n",
       "      <td id=\"T_30c39_row9_col20\" class=\"data row9 col20\" >-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row10\" class=\"row_heading level0 row10\" >Brent Lag_90</th>\n",
       "      <td id=\"T_30c39_row10_col0\" class=\"data row10 col0\" >0.88</td>\n",
       "      <td id=\"T_30c39_row10_col1\" class=\"data row10 col1\" >0.88</td>\n",
       "      <td id=\"T_30c39_row10_col2\" class=\"data row10 col2\" >-0.76</td>\n",
       "      <td id=\"T_30c39_row10_col3\" class=\"data row10 col3\" >0.90</td>\n",
       "      <td id=\"T_30c39_row10_col4\" class=\"data row10 col4\" >0.90</td>\n",
       "      <td id=\"T_30c39_row10_col5\" class=\"data row10 col5\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row10_col6\" class=\"data row10 col6\" >0.89</td>\n",
       "      <td id=\"T_30c39_row10_col7\" class=\"data row10 col7\" >0.91</td>\n",
       "      <td id=\"T_30c39_row10_col8\" class=\"data row10 col8\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row10_col9\" class=\"data row10 col9\" >0.99</td>\n",
       "      <td id=\"T_30c39_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
       "      <td id=\"T_30c39_row10_col11\" class=\"data row10 col11\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row10_col12\" class=\"data row10 col12\" >0.87</td>\n",
       "      <td id=\"T_30c39_row10_col13\" class=\"data row10 col13\" >0.90</td>\n",
       "      <td id=\"T_30c39_row10_col14\" class=\"data row10 col14\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row10_col15\" class=\"data row10 col15\" >0.47</td>\n",
       "      <td id=\"T_30c39_row10_col16\" class=\"data row10 col16\" >0.44</td>\n",
       "      <td id=\"T_30c39_row10_col17\" class=\"data row10 col17\" >0.07</td>\n",
       "      <td id=\"T_30c39_row10_col18\" class=\"data row10 col18\" >0.33</td>\n",
       "      <td id=\"T_30c39_row10_col19\" class=\"data row10 col19\" >0.60</td>\n",
       "      <td id=\"T_30c39_row10_col20\" class=\"data row10 col20\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row11\" class=\"row_heading level0 row11\" >EPU I Lag_90</th>\n",
       "      <td id=\"T_30c39_row11_col0\" class=\"data row11 col0\" >-0.63</td>\n",
       "      <td id=\"T_30c39_row11_col1\" class=\"data row11 col1\" >-0.57</td>\n",
       "      <td id=\"T_30c39_row11_col2\" class=\"data row11 col2\" >0.88</td>\n",
       "      <td id=\"T_30c39_row11_col3\" class=\"data row11 col3\" >-0.65</td>\n",
       "      <td id=\"T_30c39_row11_col4\" class=\"data row11 col4\" >-0.61</td>\n",
       "      <td id=\"T_30c39_row11_col5\" class=\"data row11 col5\" >0.89</td>\n",
       "      <td id=\"T_30c39_row11_col6\" class=\"data row11 col6\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row11_col7\" class=\"data row11 col7\" >-0.73</td>\n",
       "      <td id=\"T_30c39_row11_col8\" class=\"data row11 col8\" >0.92</td>\n",
       "      <td id=\"T_30c39_row11_col9\" class=\"data row11 col9\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row11_col10\" class=\"data row11 col10\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
       "      <td id=\"T_30c39_row11_col12\" class=\"data row11 col12\" >-0.80</td>\n",
       "      <td id=\"T_30c39_row11_col13\" class=\"data row11 col13\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row11_col14\" class=\"data row11 col14\" >0.78</td>\n",
       "      <td id=\"T_30c39_row11_col15\" class=\"data row11 col15\" >-0.78</td>\n",
       "      <td id=\"T_30c39_row11_col16\" class=\"data row11 col16\" >-0.81</td>\n",
       "      <td id=\"T_30c39_row11_col17\" class=\"data row11 col17\" >0.38</td>\n",
       "      <td id=\"T_30c39_row11_col18\" class=\"data row11 col18\" >0.01</td>\n",
       "      <td id=\"T_30c39_row11_col19\" class=\"data row11 col19\" >-0.28</td>\n",
       "      <td id=\"T_30c39_row11_col20\" class=\"data row11 col20\" >-0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row12\" class=\"row_heading level0 row12\" >Crude Lag_180</th>\n",
       "      <td id=\"T_30c39_row12_col0\" class=\"data row12 col0\" >0.62</td>\n",
       "      <td id=\"T_30c39_row12_col1\" class=\"data row12 col1\" >0.60</td>\n",
       "      <td id=\"T_30c39_row12_col2\" class=\"data row12 col2\" >-0.92</td>\n",
       "      <td id=\"T_30c39_row12_col3\" class=\"data row12 col3\" >0.66</td>\n",
       "      <td id=\"T_30c39_row12_col4\" class=\"data row12 col4\" >0.65</td>\n",
       "      <td id=\"T_30c39_row12_col5\" class=\"data row12 col5\" >-0.92</td>\n",
       "      <td id=\"T_30c39_row12_col6\" class=\"data row12 col6\" >0.82</td>\n",
       "      <td id=\"T_30c39_row12_col7\" class=\"data row12 col7\" >0.81</td>\n",
       "      <td id=\"T_30c39_row12_col8\" class=\"data row12 col8\" >-0.89</td>\n",
       "      <td id=\"T_30c39_row12_col9\" class=\"data row12 col9\" >0.83</td>\n",
       "      <td id=\"T_30c39_row12_col10\" class=\"data row12 col10\" >0.87</td>\n",
       "      <td id=\"T_30c39_row12_col11\" class=\"data row12 col11\" >-0.80</td>\n",
       "      <td id=\"T_30c39_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
       "      <td id=\"T_30c39_row12_col13\" class=\"data row12 col13\" >0.99</td>\n",
       "      <td id=\"T_30c39_row12_col14\" class=\"data row12 col14\" >-0.86</td>\n",
       "      <td id=\"T_30c39_row12_col15\" class=\"data row12 col15\" >0.86</td>\n",
       "      <td id=\"T_30c39_row12_col16\" class=\"data row12 col16\" >0.91</td>\n",
       "      <td id=\"T_30c39_row12_col17\" class=\"data row12 col17\" >-0.61</td>\n",
       "      <td id=\"T_30c39_row12_col18\" class=\"data row12 col18\" >-0.11</td>\n",
       "      <td id=\"T_30c39_row12_col19\" class=\"data row12 col19\" >-0.09</td>\n",
       "      <td id=\"T_30c39_row12_col20\" class=\"data row12 col20\" >0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row13\" class=\"row_heading level0 row13\" >Brent Lag_180</th>\n",
       "      <td id=\"T_30c39_row13_col0\" class=\"data row13 col0\" >0.64</td>\n",
       "      <td id=\"T_30c39_row13_col1\" class=\"data row13 col1\" >0.64</td>\n",
       "      <td id=\"T_30c39_row13_col2\" class=\"data row13 col2\" >-0.91</td>\n",
       "      <td id=\"T_30c39_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
       "      <td id=\"T_30c39_row13_col4\" class=\"data row13 col4\" >0.67</td>\n",
       "      <td id=\"T_30c39_row13_col5\" class=\"data row13 col5\" >-0.90</td>\n",
       "      <td id=\"T_30c39_row13_col6\" class=\"data row13 col6\" >0.79</td>\n",
       "      <td id=\"T_30c39_row13_col7\" class=\"data row13 col7\" >0.79</td>\n",
       "      <td id=\"T_30c39_row13_col8\" class=\"data row13 col8\" >-0.87</td>\n",
       "      <td id=\"T_30c39_row13_col9\" class=\"data row13 col9\" >0.87</td>\n",
       "      <td id=\"T_30c39_row13_col10\" class=\"data row13 col10\" >0.90</td>\n",
       "      <td id=\"T_30c39_row13_col11\" class=\"data row13 col11\" >-0.74</td>\n",
       "      <td id=\"T_30c39_row13_col12\" class=\"data row13 col12\" >0.99</td>\n",
       "      <td id=\"T_30c39_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
       "      <td id=\"T_30c39_row13_col14\" class=\"data row13 col14\" >-0.83</td>\n",
       "      <td id=\"T_30c39_row13_col15\" class=\"data row13 col15\" >0.89</td>\n",
       "      <td id=\"T_30c39_row13_col16\" class=\"data row13 col16\" >0.92</td>\n",
       "      <td id=\"T_30c39_row13_col17\" class=\"data row13 col17\" >-0.66</td>\n",
       "      <td id=\"T_30c39_row13_col18\" class=\"data row13 col18\" >-0.25</td>\n",
       "      <td id=\"T_30c39_row13_col19\" class=\"data row13 col19\" >-0.45</td>\n",
       "      <td id=\"T_30c39_row13_col20\" class=\"data row13 col20\" >0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row14\" class=\"row_heading level0 row14\" >EPU I Lag_180</th>\n",
       "      <td id=\"T_30c39_row14_col0\" class=\"data row14 col0\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row14_col1\" class=\"data row14 col1\" >-0.34</td>\n",
       "      <td id=\"T_30c39_row14_col2\" class=\"data row14 col2\" >0.81</td>\n",
       "      <td id=\"T_30c39_row14_col3\" class=\"data row14 col3\" >-0.53</td>\n",
       "      <td id=\"T_30c39_row14_col4\" class=\"data row14 col4\" >-0.45</td>\n",
       "      <td id=\"T_30c39_row14_col5\" class=\"data row14 col5\" >0.82</td>\n",
       "      <td id=\"T_30c39_row14_col6\" class=\"data row14 col6\" >-0.81</td>\n",
       "      <td id=\"T_30c39_row14_col7\" class=\"data row14 col7\" >-0.80</td>\n",
       "      <td id=\"T_30c39_row14_col8\" class=\"data row14 col8\" >0.82</td>\n",
       "      <td id=\"T_30c39_row14_col9\" class=\"data row14 col9\" >-0.63</td>\n",
       "      <td id=\"T_30c39_row14_col10\" class=\"data row14 col10\" >-0.70</td>\n",
       "      <td id=\"T_30c39_row14_col11\" class=\"data row14 col11\" >0.78</td>\n",
       "      <td id=\"T_30c39_row14_col12\" class=\"data row14 col12\" >-0.86</td>\n",
       "      <td id=\"T_30c39_row14_col13\" class=\"data row14 col13\" >-0.83</td>\n",
       "      <td id=\"T_30c39_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
       "      <td id=\"T_30c39_row14_col15\" class=\"data row14 col15\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row14_col16\" class=\"data row14 col16\" >-0.68</td>\n",
       "      <td id=\"T_30c39_row14_col17\" class=\"data row14 col17\" >0.28</td>\n",
       "      <td id=\"T_30c39_row14_col18\" class=\"data row14 col18\" >-0.49</td>\n",
       "      <td id=\"T_30c39_row14_col19\" class=\"data row14 col19\" >-0.62</td>\n",
       "      <td id=\"T_30c39_row14_col20\" class=\"data row14 col20\" >0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row15\" class=\"row_heading level0 row15\" >Crude Lag_270</th>\n",
       "      <td id=\"T_30c39_row15_col0\" class=\"data row15 col0\" >0.75</td>\n",
       "      <td id=\"T_30c39_row15_col1\" class=\"data row15 col1\" >0.55</td>\n",
       "      <td id=\"T_30c39_row15_col2\" class=\"data row15 col2\" >-0.83</td>\n",
       "      <td id=\"T_30c39_row15_col3\" class=\"data row15 col3\" >0.81</td>\n",
       "      <td id=\"T_30c39_row15_col4\" class=\"data row15 col4\" >0.64</td>\n",
       "      <td id=\"T_30c39_row15_col5\" class=\"data row15 col5\" >-0.85</td>\n",
       "      <td id=\"T_30c39_row15_col6\" class=\"data row15 col6\" >0.74</td>\n",
       "      <td id=\"T_30c39_row15_col7\" class=\"data row15 col7\" >0.75</td>\n",
       "      <td id=\"T_30c39_row15_col8\" class=\"data row15 col8\" >-0.90</td>\n",
       "      <td id=\"T_30c39_row15_col9\" class=\"data row15 col9\" >0.06</td>\n",
       "      <td id=\"T_30c39_row15_col10\" class=\"data row15 col10\" >0.47</td>\n",
       "      <td id=\"T_30c39_row15_col11\" class=\"data row15 col11\" >-0.78</td>\n",
       "      <td id=\"T_30c39_row15_col12\" class=\"data row15 col12\" >0.86</td>\n",
       "      <td id=\"T_30c39_row15_col13\" class=\"data row15 col13\" >0.89</td>\n",
       "      <td id=\"T_30c39_row15_col14\" class=\"data row15 col14\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
       "      <td id=\"T_30c39_row15_col16\" class=\"data row15 col16\" >0.99</td>\n",
       "      <td id=\"T_30c39_row15_col17\" class=\"data row15 col17\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row15_col18\" class=\"data row15 col18\" >0.36</td>\n",
       "      <td id=\"T_30c39_row15_col19\" class=\"data row15 col19\" >0.65</td>\n",
       "      <td id=\"T_30c39_row15_col20\" class=\"data row15 col20\" >-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row16\" class=\"row_heading level0 row16\" >Brent Lag_270</th>\n",
       "      <td id=\"T_30c39_row16_col0\" class=\"data row16 col0\" >0.73</td>\n",
       "      <td id=\"T_30c39_row16_col1\" class=\"data row16 col1\" >0.51</td>\n",
       "      <td id=\"T_30c39_row16_col2\" class=\"data row16 col2\" >-0.87</td>\n",
       "      <td id=\"T_30c39_row16_col3\" class=\"data row16 col3\" >0.79</td>\n",
       "      <td id=\"T_30c39_row16_col4\" class=\"data row16 col4\" >0.61</td>\n",
       "      <td id=\"T_30c39_row16_col5\" class=\"data row16 col5\" >-0.88</td>\n",
       "      <td id=\"T_30c39_row16_col6\" class=\"data row16 col6\" >0.74</td>\n",
       "      <td id=\"T_30c39_row16_col7\" class=\"data row16 col7\" >0.74</td>\n",
       "      <td id=\"T_30c39_row16_col8\" class=\"data row16 col8\" >-0.92</td>\n",
       "      <td id=\"T_30c39_row16_col9\" class=\"data row16 col9\" >0.05</td>\n",
       "      <td id=\"T_30c39_row16_col10\" class=\"data row16 col10\" >0.44</td>\n",
       "      <td id=\"T_30c39_row16_col11\" class=\"data row16 col11\" >-0.81</td>\n",
       "      <td id=\"T_30c39_row16_col12\" class=\"data row16 col12\" >0.91</td>\n",
       "      <td id=\"T_30c39_row16_col13\" class=\"data row16 col13\" >0.92</td>\n",
       "      <td id=\"T_30c39_row16_col14\" class=\"data row16 col14\" >-0.68</td>\n",
       "      <td id=\"T_30c39_row16_col15\" class=\"data row16 col15\" >0.99</td>\n",
       "      <td id=\"T_30c39_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
       "      <td id=\"T_30c39_row16_col17\" class=\"data row16 col17\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row16_col18\" class=\"data row16 col18\" >0.26</td>\n",
       "      <td id=\"T_30c39_row16_col19\" class=\"data row16 col19\" >0.54</td>\n",
       "      <td id=\"T_30c39_row16_col20\" class=\"data row16 col20\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row17\" class=\"row_heading level0 row17\" >EPU I Lag_270</th>\n",
       "      <td id=\"T_30c39_row17_col0\" class=\"data row17 col0\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row17_col1\" class=\"data row17 col1\" >-0.61</td>\n",
       "      <td id=\"T_30c39_row17_col2\" class=\"data row17 col2\" >0.45</td>\n",
       "      <td id=\"T_30c39_row17_col3\" class=\"data row17 col3\" >-0.72</td>\n",
       "      <td id=\"T_30c39_row17_col4\" class=\"data row17 col4\" >-0.67</td>\n",
       "      <td id=\"T_30c39_row17_col5\" class=\"data row17 col5\" >0.50</td>\n",
       "      <td id=\"T_30c39_row17_col6\" class=\"data row17 col6\" >-0.54</td>\n",
       "      <td id=\"T_30c39_row17_col7\" class=\"data row17 col7\" >-0.58</td>\n",
       "      <td id=\"T_30c39_row17_col8\" class=\"data row17 col8\" >0.58</td>\n",
       "      <td id=\"T_30c39_row17_col9\" class=\"data row17 col9\" >0.33</td>\n",
       "      <td id=\"T_30c39_row17_col10\" class=\"data row17 col10\" >0.07</td>\n",
       "      <td id=\"T_30c39_row17_col11\" class=\"data row17 col11\" >0.38</td>\n",
       "      <td id=\"T_30c39_row17_col12\" class=\"data row17 col12\" >-0.61</td>\n",
       "      <td id=\"T_30c39_row17_col13\" class=\"data row17 col13\" >-0.66</td>\n",
       "      <td id=\"T_30c39_row17_col14\" class=\"data row17 col14\" >0.28</td>\n",
       "      <td id=\"T_30c39_row17_col15\" class=\"data row17 col15\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row17_col16\" class=\"data row17 col16\" >-0.69</td>\n",
       "      <td id=\"T_30c39_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
       "      <td id=\"T_30c39_row17_col18\" class=\"data row17 col18\" >0.71</td>\n",
       "      <td id=\"T_30c39_row17_col19\" class=\"data row17 col19\" >0.81</td>\n",
       "      <td id=\"T_30c39_row17_col20\" class=\"data row17 col20\" >-0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row18\" class=\"row_heading level0 row18\" >Crude Lag_360</th>\n",
       "      <td id=\"T_30c39_row18_col0\" class=\"data row18 col0\" >-0.10</td>\n",
       "      <td id=\"T_30c39_row18_col1\" class=\"data row18 col1\" >-0.10</td>\n",
       "      <td id=\"T_30c39_row18_col2\" class=\"data row18 col2\" >0.02</td>\n",
       "      <td id=\"T_30c39_row18_col3\" class=\"data row18 col3\" >-0.15</td>\n",
       "      <td id=\"T_30c39_row18_col4\" class=\"data row18 col4\" >-0.17</td>\n",
       "      <td id=\"T_30c39_row18_col5\" class=\"data row18 col5\" >-0.20</td>\n",
       "      <td id=\"T_30c39_row18_col6\" class=\"data row18 col6\" >-0.19</td>\n",
       "      <td id=\"T_30c39_row18_col7\" class=\"data row18 col7\" >-0.21</td>\n",
       "      <td id=\"T_30c39_row18_col8\" class=\"data row18 col8\" >-0.04</td>\n",
       "      <td id=\"T_30c39_row18_col9\" class=\"data row18 col9\" >0.30</td>\n",
       "      <td id=\"T_30c39_row18_col10\" class=\"data row18 col10\" >0.33</td>\n",
       "      <td id=\"T_30c39_row18_col11\" class=\"data row18 col11\" >0.01</td>\n",
       "      <td id=\"T_30c39_row18_col12\" class=\"data row18 col12\" >-0.11</td>\n",
       "      <td id=\"T_30c39_row18_col13\" class=\"data row18 col13\" >-0.25</td>\n",
       "      <td id=\"T_30c39_row18_col14\" class=\"data row18 col14\" >-0.49</td>\n",
       "      <td id=\"T_30c39_row18_col15\" class=\"data row18 col15\" >0.36</td>\n",
       "      <td id=\"T_30c39_row18_col16\" class=\"data row18 col16\" >0.26</td>\n",
       "      <td id=\"T_30c39_row18_col17\" class=\"data row18 col17\" >0.71</td>\n",
       "      <td id=\"T_30c39_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
       "      <td id=\"T_30c39_row18_col19\" class=\"data row18 col19\" >0.93</td>\n",
       "      <td id=\"T_30c39_row18_col20\" class=\"data row18 col20\" >-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row19\" class=\"row_heading level0 row19\" >Brent Lag_360</th>\n",
       "      <td id=\"T_30c39_row19_col0\" class=\"data row19 col0\" >-0.34</td>\n",
       "      <td id=\"T_30c39_row19_col1\" class=\"data row19 col1\" >-0.34</td>\n",
       "      <td id=\"T_30c39_row19_col2\" class=\"data row19 col2\" >-0.21</td>\n",
       "      <td id=\"T_30c39_row19_col3\" class=\"data row19 col3\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row19_col4\" class=\"data row19 col4\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row19_col5\" class=\"data row19 col5\" >-0.42</td>\n",
       "      <td id=\"T_30c39_row19_col6\" class=\"data row19 col6\" >-0.35</td>\n",
       "      <td id=\"T_30c39_row19_col7\" class=\"data row19 col7\" >-0.43</td>\n",
       "      <td id=\"T_30c39_row19_col8\" class=\"data row19 col8\" >-0.11</td>\n",
       "      <td id=\"T_30c39_row19_col9\" class=\"data row19 col9\" >0.58</td>\n",
       "      <td id=\"T_30c39_row19_col10\" class=\"data row19 col10\" >0.60</td>\n",
       "      <td id=\"T_30c39_row19_col11\" class=\"data row19 col11\" >-0.28</td>\n",
       "      <td id=\"T_30c39_row19_col12\" class=\"data row19 col12\" >-0.09</td>\n",
       "      <td id=\"T_30c39_row19_col13\" class=\"data row19 col13\" >-0.45</td>\n",
       "      <td id=\"T_30c39_row19_col14\" class=\"data row19 col14\" >-0.62</td>\n",
       "      <td id=\"T_30c39_row19_col15\" class=\"data row19 col15\" >0.65</td>\n",
       "      <td id=\"T_30c39_row19_col16\" class=\"data row19 col16\" >0.54</td>\n",
       "      <td id=\"T_30c39_row19_col17\" class=\"data row19 col17\" >0.81</td>\n",
       "      <td id=\"T_30c39_row19_col18\" class=\"data row19 col18\" >0.93</td>\n",
       "      <td id=\"T_30c39_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
       "      <td id=\"T_30c39_row19_col20\" class=\"data row19 col20\" >-0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30c39_level0_row20\" class=\"row_heading level0 row20\" >EPU I Lag_360</th>\n",
       "      <td id=\"T_30c39_row20_col0\" class=\"data row20 col0\" >-0.17</td>\n",
       "      <td id=\"T_30c39_row20_col1\" class=\"data row20 col1\" >-0.17</td>\n",
       "      <td id=\"T_30c39_row20_col2\" class=\"data row20 col2\" >-0.15</td>\n",
       "      <td id=\"T_30c39_row20_col3\" class=\"data row20 col3\" >-0.09</td>\n",
       "      <td id=\"T_30c39_row20_col4\" class=\"data row20 col4\" >-0.09</td>\n",
       "      <td id=\"T_30c39_row20_col5\" class=\"data row20 col5\" >-0.14</td>\n",
       "      <td id=\"T_30c39_row20_col6\" class=\"data row20 col6\" >0.15</td>\n",
       "      <td id=\"T_30c39_row20_col7\" class=\"data row20 col7\" >0.07</td>\n",
       "      <td id=\"T_30c39_row20_col8\" class=\"data row20 col8\" >-0.21</td>\n",
       "      <td id=\"T_30c39_row20_col9\" class=\"data row20 col9\" >-0.07</td>\n",
       "      <td id=\"T_30c39_row20_col10\" class=\"data row20 col10\" >-0.09</td>\n",
       "      <td id=\"T_30c39_row20_col11\" class=\"data row20 col11\" >-0.30</td>\n",
       "      <td id=\"T_30c39_row20_col12\" class=\"data row20 col12\" >0.32</td>\n",
       "      <td id=\"T_30c39_row20_col13\" class=\"data row20 col13\" >0.20</td>\n",
       "      <td id=\"T_30c39_row20_col14\" class=\"data row20 col14\" >0.17</td>\n",
       "      <td id=\"T_30c39_row20_col15\" class=\"data row20 col15\" >-0.28</td>\n",
       "      <td id=\"T_30c39_row20_col16\" class=\"data row20 col16\" >-0.09</td>\n",
       "      <td id=\"T_30c39_row20_col17\" class=\"data row20 col17\" >-0.72</td>\n",
       "      <td id=\"T_30c39_row20_col18\" class=\"data row20 col18\" >-0.77</td>\n",
       "      <td id=\"T_30c39_row20_col19\" class=\"data row20 col19\" >-0.68</td>\n",
       "      <td id=\"T_30c39_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14fc3e6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "data_vol2 = data.pct_change().rolling(window=90).std()\n",
    "\n",
    "# Introducing lags for both columns\n",
    "for lag in lags:\n",
    "    for column in data.columns:\n",
    "        data_vol2[f'{column[:5]} Lag_{lag}'] = data_vol2[column].shift(lag)\n",
    "\n",
    "# Calculating the correlation matrix including the lags\n",
    "correlation_matrix = data_vol2.corr()\n",
    "\n",
    "# Display the correlation matrix as a table\n",
    "styled_table = correlation_matrix.style.background_gradient(cmap='coolwarm', axis=None).format(\"{:.2f}\")\n",
    "title = \"#### (With Lags) Stock Price Pct Daily Returns Volatility and EPU Index Volatility Correlation Heatmap\\nVolatility intended as 90-day StD.\"\n",
    "display(Markdown(title))\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "#### There seems to be a **very strong inverse correlation** between EPU Index Volatility and Crude Oil Prices' Volatility (90d StD), peaking at about **90%** at 180 days of lag.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "### Relevant Findings\n",
    "\n",
    "Over the past two years: \n",
    "* CL=F and BZ=F close prices have had **low volatility**, while EPU index has had **very high volatility**.\n",
    "\n",
    "| | Average change in percentage daily returns (2-year mean) | Percentage volatility of percentage daily returns (2-year StD) |\n",
    "|----------|----------|----------|\n",
    "| CL=F Close | **0.02%** | **2.6%** |\n",
    "| BZ=F Close | **0.03%** | **2.5%** |\n",
    "| EPU Index | **22%** | **97%** |\n",
    "    \n",
    "* The stock prices of CL=F and BZ=F at close have been **highly correlated**, with a **99%** value.\n",
    "* There seems to be **no significant correlation** between the daily percentage returns of oil prices and daily percentage change of EPU Index, even including lags.\n",
    "* There seems to be a **moderate to strong inverse correlation** between Crude Oil prices and EPU Index Volatility (30d StD), peaking at around **60% at 90-180 days** of lag.\n",
    "* There seems to be a **very strong inverse correlation** between EPU Index Volatility (90d StD) and Crude Oil Prices' Volatility (90d StD), peaking at around **90% at 180 days** of lag.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "### Discussion\n",
    "\n",
    "It is expected that the stock prices of CL=F and BZ=F would be highly correlated, since they are both oil prices, though they are different types of crude oil.\n",
    "\n",
    "It is more challenging to identify a relationship between the EPU Index and crude oil prices. This part of the analysis has showed that overall, _the change in value of the EPU Index is not enough to predict the change in crude oil prices at any point in time_, i.e. they do not have a significant correlation. \n",
    "* This is likely because of the very high volatility of the EPU Index, which is not commonly observed in commodity prices such as crude oil.\n",
    "\n",
    "However, the degree of volatility in the medium-long term (30-90d) of the EPU Index does have a significant impact on both the price and specifically volatility of crude oil, which is mostly observed after a lag period of around **180 days**. \n",
    "* Specifically, there is a **~50%** inverse correlation between EPU Index Volatility (30d StD) and Crude Oil Prices at ~180d lag.\n",
    "    * If EPU Index has higher volatility, there is a **high chance** the crude oil prices will be lower, and vice versa.\n",
    "* Additionally, there is a **~90%** inverse correlation between EPU Index Volatility (90d StD) and Crude Oil Prices Volatility (90d StD) at ~180d lag.\n",
    "    * If EPU Index has higher volatility, there is a **very high chance** the crude oil prices will have less volatility, and vice versa.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "A profitable trading strategy that utilizes insights from both market data and economic indicators will have these characteristics:\n",
    "* When EPU Index is in a state of higher volatility, the value of crude oil will be proportionally lower in about 180d. \n",
    "    * When EPU volatility is higher, sell crude oil and buy in 180 days.\n",
    "* When EPU Index is in a state of lower volatility, the value of crude oil will be proportionally higher in about 180d. \n",
    "    * When EPU volatility is lower, buy crude oil and sell in 180 days.\n",
    "\n",
    "Overall, this trading strategy leverages insights from both market data (crude oil prices) and economic indicators (EPU index volatility) to make informed decisions about buying or selling crude oil assets. It aims to capitalize on the expected relationship between EPU volatility and future crude oil prices, providing opportunities for profitable trading within a specified time frame.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Strategy Development [Main Task]\n",
    "Develop a strategy that leverages correlations between equity price movements and external economic factors by editing `NewStrategy`. For documentation on the backtesting library, see: [Backtesting.py Documentation](https://kernc.github.io/backtesting.py/doc/backtesting/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SMACrossover(Strategy):\n",
    "    \"\"\"\n",
    "    Simple moving average crossover strategy.\n",
    "    \"\"\"\n",
    "    short_window = 40\n",
    "    long_window = 100\n",
    "\n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "        Initialise the strategy by declaring indicators for short and long simple moving averages.\n",
    "        \"\"\"\n",
    "        self.sma_short = self.I(SMA, self.data.Close, self.short_window)\n",
    "        self.sma_long = self.I(SMA, self.data.Close, self.long_window)\n",
    "\n",
    "    def next(self):\n",
    "        \"\"\"\n",
    "        Main strategy runtime method. If the short SMA crosses above the long SMA, buy.\n",
    "        If the short SMA crosses below the long SMA, sell.\n",
    "        \"\"\"\n",
    "        if crossover(self.sma_short, self.sma_long):\n",
    "            self.buy()\n",
    "\n",
    "        elif crossover(self.sma_long, self.sma_short):\n",
    "            self.sell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling_std(series, window):\n",
    "    \"\"\"\n",
    "    Calculate the rolling standard deviation of an array of percentage changes.\n",
    "    \"\"\"\n",
    "    # Simple implementation of rolling standard deviation\n",
    "    return pd.Series(series).rolling(window).std()\n",
    "\n",
    "class VolatilityCrossover(Strategy):\n",
    "    \"\"\"\n",
    "    This strategy leverages the volatility of the Economic Policy Uncertainty (EPU) Index\n",
    "    to make trading decisions on crude oil futures. It is based on the premise that \n",
    "    increases in the volatility of the EPU Index signal potential decreases in oil prices,\n",
    "    and vice versa. \n",
    "    \"\"\"\n",
    "    short_window = 40\n",
    "    long_window = 100\n",
    "\n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "        Initialise the strategy by declaring indicators for short and long rolling windows.\n",
    "        \"\"\"\n",
    "        # Compute volatility of EPU Index as standard deviation with different rolling windows\n",
    "        self.short_vol = self.I(rolling_std, self.data.USEPUINDXD, self.short_window)\n",
    "        self.long_vol = self.I(rolling_std, self.data.USEPUINDXD, self.long_window)\n",
    "\n",
    "    def next(self):\n",
    "        \"\"\"\n",
    "        Main strategy runtime method. \n",
    "        \"\"\"\n",
    "        # If short-term volatility crosses above long-term volatility\n",
    "        # EPU Index volatility is increasing\n",
    "        # Price of crude oil is expected to decrease\n",
    "        # Therefore close any long positions and open a short position\n",
    "        if crossover(self.short_vol, self.long_vol):\n",
    "            self.position.close()\n",
    "            self.sell()\n",
    "\n",
    "        # If long-term volatility crosses above short-term volatility\n",
    "        # EPU Index volatility is decreasing\n",
    "        # Price of crude oil is expected to increase\n",
    "        # Therefore close any short positions and open a long position\n",
    "        if crossover(self.long_vol, self.short_vol):\n",
    "            self.position.close()\n",
    "            self.buy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Strategy Testing [Structured]\n",
    "Evaluate the effectiveness and risk-adjusted performance of your strategy. For this purpose, we have provided the `StrategyExecutor` class and a backtesting example to facilitate the process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StrategyExecutor:\n",
    "    def __init__(self, crude_epu_data, brent_epu_data, cash=10_000, commission=0.002):\n",
    "        \"\"\"\n",
    "        Initialise the executor with the combined data for a specific ticker.\n",
    "        \n",
    "        :param crude_epu_data: DataFrame containing the combined data for crude oil and EPU index.\n",
    "        :param brent_epu_data: DataFrame containing the combined data for brent oil and EPU index.\n",
    "        :param cash: Starting cash for the backtesting (default 10,000).\n",
    "        :param commission: Commission per transaction (default 0.002).\n",
    "        \"\"\"\n",
    "        self.crude_epu_data = crude_epu_data\n",
    "        self.brent_epu_data = brent_epu_data\n",
    "        self.config = {\n",
    "            'cash': cash,\n",
    "            'commission': commission\n",
    "        }\n",
    "\n",
    "    def run_strategy_for_ticker(self, strategy, data_type, params=None, display_stats=None):\n",
    "        \"\"\"\n",
    "        Runs the given strategy on the prepared data.\n",
    "\n",
    "        :param strategy: The strategy class to be executed.\n",
    "        :param data_type: The type of data to run the strategy on ('crude' or 'brent').\n",
    "        :param params: Additional parameters for the strategy.\n",
    "        :param display_stats: List of statistics to display. If None, all stats are returned.\n",
    "        :return: The backtesting statistics.\n",
    "        \"\"\"\n",
    "        if data_type == 'CL=F':\n",
    "            data = self.crude_epu_data\n",
    "        elif data_type == 'BZ=F':\n",
    "            data = self.brent_epu_data\n",
    "        else:\n",
    "            raise ValueError(\"Invalid data type specified\")\n",
    "        \n",
    "        # Create a Backtest instance with the filtered data and strategy\n",
    "        bt = Backtest(data=data,\n",
    "                      strategy=strategy,\n",
    "                      cash=self.config['cash'],\n",
    "                      commission=self.config['commission'],\n",
    "                      exclusive_orders=True)\n",
    "\n",
    "        # Run the backtest\n",
    "        stats = bt.run(**(params or {}))\n",
    "\n",
    "        # Return the required statistics\n",
    "        return {stat: stats[stat] for stat in display_stats} if display_stats is not None else stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for SMACrossover on CL=F:\n",
      "Return [%]: 9.395733583984384\n",
      "Buy & Hold Return [%]: -5.822818347209244\n",
      "Sharpe Ratio: 0.17667757557696726\n",
      "\n",
      "Statistics for SMACrossover on BZ=F:\n",
      "Return [%]: 10.182767126922608\n",
      "Buy & Hold Return [%]: -2.4563210419716177\n",
      "Sharpe Ratio: 0.19546314403752402\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the executor then specify the strategy and desired statistics\n",
    "executor = StrategyExecutor(crude_epu_data, brent_epu_data)\n",
    "strategy = SMACrossover\n",
    "desired_stats = ['Return [%]', 'Buy & Hold Return [%]', 'Sharpe Ratio']\n",
    "\n",
    "for ticker in tickers:\n",
    "    # Run the strategy for each ticker and specify the parameters\n",
    "    stats = executor.run_strategy_for_ticker(\n",
    "        strategy,\n",
    "        ticker,\n",
    "        {\n",
    "            'short_window': 40,\n",
    "            'long_window': 100\n",
    "        }, \n",
    "        display_stats=desired_stats\n",
    "    )\n",
    "\n",
    "    # Print the selected metrics for each ticker\n",
    "    print(f\"Statistics for {strategy.__name__} on {ticker}:\")\n",
    "    for stat, value in stats.items():\n",
    "        print(f\"{stat}: {value}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for VolatilityCrossover on CL=F:\n",
      "Return [%]: 53.21421398818966\n",
      "Sharpe Ratio: 0.6613061839990659\n",
      "Sortino Ratio: 1.2866037609708525\n",
      "# Trades: 25\n",
      "Win Rate [%]: 52.0\n",
      "Best Trade [%]: 20.09939369526822\n",
      "Worst Trade [%]: -13.033443636106657\n",
      "Avg. Trade [%]: 1.7300774940580466\n",
      "\n",
      "Statistics for VolatilityCrossover on BZ=F:\n",
      "Return [%]: 36.66112078903196\n",
      "Sharpe Ratio: 0.5376687603467989\n",
      "Sortino Ratio: 0.9834969678648605\n",
      "# Trades: 25\n",
      "Win Rate [%]: 52.0\n",
      "Best Trade [%]: 16.451104210304745\n",
      "Worst Trade [%]: -9.539829781118758\n",
      "Avg. Trade [%]: 1.2619325313579122\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the executor then specify the strategy and desired statistics\n",
    "executor = StrategyExecutor(crude_epu_data, brent_epu_data)\n",
    "strategy = VolatilityCrossover\n",
    "desired_stats = ['Return [%]', 'Sharpe Ratio', 'Sortino Ratio', '# Trades', \n",
    "                 'Win Rate [%]', 'Best Trade [%]', 'Worst Trade [%]', 'Avg. Trade [%]']\n",
    "\n",
    "for ticker in tickers:\n",
    "    # Run the strategy for each ticker and specify the parameters\n",
    "    stats = executor.run_strategy_for_ticker(\n",
    "        strategy,\n",
    "        ticker,\n",
    "        {\n",
    "            'short_window': 40,\n",
    "            'long_window': 100\n",
    "        }, \n",
    "        display_stats=desired_stats\n",
    "    )\n",
    "\n",
    "    # Print the selected metrics for each ticker\n",
    "    print(f\"Statistics for {strategy.__name__} on {ticker}:\")\n",
    "    for stat, value in stats.items():\n",
    "        print(f\"{stat}: {value}\")\n",
    "    print()"
   ]
  }
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