bond_returns.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56b3c988",
   "metadata": {},
   "source": [
    "# Bond Returns and Risks\n",
    "\n",
    "_The essence of investment management is the management of risks, not the management of returns_ - Benjamin Graham \n",
    "\n",
    "Concepts\n",
    "- Term structure changes\n",
    "- Bond risk factors\n",
    "\n",
    "References:\n",
    "- FRM Part I Exam Book Valuation and Risk Models Ch12-13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01bdc8d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "import statsmodels.formula.api as smf\n",
    "from finds.readers import Alfred\n",
    "from finds.recipes import bond_price\n",
    "from secret import credentials\n",
    "# %matplotlib qt\n",
    "VERBOSE = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5774ca0c",
   "metadata": {},
   "source": [
    "## Term structure changes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30644f28",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "source": [
    "### Duration\n",
    "\n",
    "Duration, or Macaulay Durection, measures the change in bond price P with\n",
    "a instataneous change in its yield y. \n",
    "\n",
    "Consider a bond with price $P$ and yield $y$. Because it leads to a simpler duration formula, we first measure the yield with continuous compounding. The present value relationship between $P$ and $y$ is $P = \\sum_{t=1}^{n} c_t e^{-yt}$.  Then after differentiating: $\\Delta P = -\\sum_{t=1}^{n} c_t t e^{-yt} \\Delta y$\n",
    "\n",
    "We define the yield-based duration as the proportional change in the\n",
    "bond price for a small change in the yield: \n",
    "\n",
    "$$D = \\dfrac{\\Delta P}{P \\Delta y} = \\sum_{t=1}^{n} t \\dfrac{c_t e^{-yt}}{P}$$\n",
    "\n",
    "This formulation gives us another interpretation of duration. It\n",
    "can be calculated by taking an average of the times when cash flows\n",
    "are received weighted by the proportion of the bond’s value received\n",
    "at each time. Duration is therefore a measure of how long an\n",
    "investor has to wait to receive cash flows. (This explains why the\n",
    "word “duration” was chosen to describe the sensitivity of proportional\n",
    "changes to yield).\n",
    "\n",
    "Modified duration: If the yield on the bond is measured with\n",
    "semi-annual compounding rather than continuous compounding, the\n",
    "expression for (Macaulay) duration must be divided by $(1 + y_2/2)$ \n",
    "(or $1 + y_m/m$ for m'thly compounding).\n",
    "\n",
    "Effective duration describes the percentage change in the price of a\n",
    "bond with embedded options due to a small change in all rates.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32452a57",
   "metadata": {},
   "source": [
    "### DV01\n",
    "\n",
    "DV01 describes the impact of a one-basis-point change in interest\n",
    "rates on the value of a portfolio. \n",
    "\n",
    "$$DV01 = -\\dfrac{\\Delta P}{\\Delta r}$$\n",
    "\n",
    "where $\\Delta r$ is the size of a small parallel shift\n",
    "in the interest rate term structure (measured in basis points).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bec54f",
   "metadata": {},
   "source": [
    "### Convexity\n",
    "\n",
    "Bond duration and convexity are in the first two terms of the\n",
    "derivative of a bond's price with respect to interest rates in a Taylor\n",
    "expansion. Convexity measures the curvature of\n",
    "the bond price-interest rate relationship. It indicates how much the\n",
    "bond's duration changes as interest rates change. While duration helps\n",
    "estimate bond price changes for small interest rate movements,\n",
    "convexity accounts for larger changes, providing a more accurate\n",
    "prediction of bond price behavior\n",
    "\n",
    "Yield-based convexity when yields are expressed with continuous compounding is\n",
    "\n",
    "$$C = \\frac{1}{P}\\frac{1}{(1+r/m)^2}\\sum_{t=1}^{T}(t/2m +(t/m)^2)\\frac{C_t}{(1+r/m)^t}$$\n",
    "\n",
    "It is a weighted average of the squared time to maturity. When yields\n",
    "are expressed with semi-annual compounding, these expressions must be\n",
    "divided by $(1 + y_2/2)^2$ (or $(1 + y_m/2)^2$ with m'thly compounding) \n",
    "and the result is referred to as modified convexity.\n",
    "Effective convexity measures the sensitivity of the duration measure\n",
    "to changes in interest rates. \n",
    "\n",
    "$$C = \\dfrac{1}{P}[\\dfrac{P^+ + P^- - 2P}{(\\Delta r)^2}]$$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8893fd44",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "# Helpers to calculate duration and convexity\n",
    "def macaulay_duration(coupon: float, n: int, m: int, price: float, \n",
    "                      yields: float | List[float], par: float = 1, **kwargs) -> float:\n",
    "    \"\"\"Compute macaulay duration of a bond given spot rates or yield-to-maturity\n",
    "\n",
    "    Args:\n",
    "      coupon : Annual coupon rate\n",
    "      n : Number of remaining coupons\n",
    "      m : Number of compounding periods per year\n",
    "      price : current market price of bond\n",
    "      yields : Simple annual yield-to-maturity or spot rates each period\n",
    "      par : face or par value of bond\n",
    "\n",
    "    Returns:\n",
    "      Macaulay duration\n",
    "    \"\"\"\n",
    "    if not pd.api.types.is_list_like(yields):\n",
    "        yields = [yields] * n        # same spot rate every period\n",
    "    assert len(yields) == n, \"Number of spot rates must equal number of couponds\"\n",
    "    pv = [(1 + yields[t-1]/m)**(-t) * (t/m) * (coupon/m + par*(t == n))\n",
    "          for t in range(1, n+1)]    # discount every period's time-weighted payment\n",
    "    return np.sum(pv) / price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68cea862",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "def modified_duration(coupon: float, n: int, m: int, price: float, \n",
    "                      yields: float | List[float], par: float = 1, **kwargs) -> float:\n",
    "    \"\"\"Compute modified duration of a bond given spot rates or yield-to-maturity\n",
    "\n",
    "    Args:\n",
    "      coupon : Annual coupon rate\n",
    "      n : Number of remaining coupons\n",
    "      m : Number of compounding periods per year\n",
    "      price : current market price of bond\n",
    "      yields : Simple annual yield-to-maturity or spot rates each period\n",
    "      par : face or par value of bond\n",
    "\n",
    "    Returns:\n",
    "      Modified duration\n",
    "    \"\"\"\n",
    "    assert not pd.api.types.is_list_like(yields), \"Not Implemented\"\n",
    "    ytm = yields\n",
    "    return (macaulay_duration(coupon=coupon, n=n, m=m, price=price, yields=yields, par=par)\n",
    "            / (1 + ytm/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12bfcec8",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "def modified_convexity(coupon: float, n: int, m: int, price: float, \n",
    "                       yields: float | List[float], par: float = 1, **kwargs) -> float:\n",
    "    \"\"\"Compute mocified convexity of a bond given spot rates or yield-to-maturity\n",
    "\n",
    "    Args:\n",
    "      coupon : Annual coupon rate\n",
    "      n : Number of remaining coupons\n",
    "      m : Number of compounding periods per year\n",
    "      price : current market price of bond\n",
    "      yields : Simple annual yield-to-maturity or spot rates each period\n",
    "      par : face or par value of bond\n",
    "\n",
    "    Returns:\n",
    "      Modified convexity\n",
    "    \"\"\"\n",
    "    assert not pd.api.types.is_list_like(yields), \"Not Implemented\"\n",
    "    ytm = yields\n",
    "    if not pd.api.types.is_list_like(yields):\n",
    "        yields = [yields] * n        # same spot rate every period\n",
    "    assert len(yields) == n, \"Number of spot rates must equal number of coupons\"\n",
    "    pv = [(1 + yields[t-1]/m)**(-t) * ((t/m)**2 + t/(2*m)) * (coupon/m + par*(t == n))\n",
    "          for t in range(1, n+1)]    # discount every period's time-weighted payment\n",
    "    return np.sum(pv) / (price * (1 + ytm/m)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda98e58",
   "metadata": {},
   "source": [
    "### Barbells and bullets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "117eee4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute prices, duration and convexity for 3 bonds (FRM Valuation and Risk Models 12.7)\n",
    "bond5Y = dict(coupon=2, m=2, n=2*5, par=100, yields=0.04)\n",
    "bond10Y = dict(coupon=4, m=2, n=2*10, par=100, yields=0.04)\n",
    "bond20Y = dict(coupon=6, m=2, n=2*20, par=100, yields=0.04)\n",
    "for bond in [bond5Y, bond10Y, bond20Y]:\n",
    "    bond |= dict(price=bond_price(**bond))\n",
    "    bond |= dict(duration=modified_duration(**bond))\n",
    "    bond |= dict(convexity=modified_convexity(**bond))\n",
    "bonds = DataFrame.from_dict(\n",
    "    {f\"{b['n']/b['m']:.0f}-year, {b['coupon']}% coupon\": [\n",
    "        b['price'], b['duration'], b['convexity']]\n",
    "     for b in [bond5Y, bond10Y, bond20Y]},\n",
    "    orient='index',\n",
    "    columns=['Value', 'Effective Duration', 'Effective Convexity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3644c43f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table 12.4 Effective Durations and Convexities of Three Bonds\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\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>Value</th>\n",
       "      <th>Effective Duration</th>\n",
       "      <th>Effective Convexity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5-year, 2% coupon</th>\n",
       "      <td>91.0174</td>\n",
       "      <td>4.6764</td>\n",
       "      <td>24.8208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-year, 4% coupon</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>8.1757</td>\n",
       "      <td>78.8979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-year, 6% coupon</th>\n",
       "      <td>127.3555</td>\n",
       "      <td>12.6233</td>\n",
       "      <td>212.4587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Value  Effective Duration  Effective Convexity\n",
       "5-year, 2% coupon    91.0174              4.6764              24.8208\n",
       "10-year, 4% coupon  100.0000              8.1757              78.8979\n",
       "20-year, 6% coupon  127.3555             12.6233             212.4587"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Table 12.4 Effective Durations and Convexities of Three Bonds\")\n",
    "bonds.round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "096cd76d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barbell: weight in 5Y = 0.5597, weight in 20Y = 0.4403\n"
     ]
    }
   ],
   "source": [
    "# Compute 5Y and 20Y weights of barbell portfolio, with same duration as bullet 10Y\n",
    "barbell = ((bond10Y['duration'] - bond20Y['duration']) /\n",
    "           (bond5Y['duration'] - bond20Y['duration']))\n",
    "print(f\"Barbell: weight in 5Y = {barbell:.4f}, weight in 20Y = {1-barbell:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d6dada3",
   "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>Effective Duration</th>\n",
       "      <th>Effective Convexity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bullet</th>\n",
       "      <td>8.175717</td>\n",
       "      <td>78.897925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbell</th>\n",
       "      <td>8.175717</td>\n",
       "      <td>107.444902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Effective Duration  Effective Convexity\n",
       "Bullet             8.175717            78.897925\n",
       "Barbell            8.175717           107.444902"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare durations and convexities\n",
    "DataFrame.from_dict(dict(Bullet=np.array([0,1,0]).dot(bonds),\n",
    "                         Barbell=np.array([barbell, 0, 1-barbell]).dot(bonds)),\n",
    "                    orient='index', columns=bonds.columns)\\\n",
    "                    [['Effective Duration', 'Effective Convexity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f3aeb1",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "A __positive convexity__ improves the bond holder’s position when there is\n",
    "a parallel shift in interest rates. While both strategies provide a yield of 4%\n",
    "and a duration of 8.1758, the barbell strategy always produces a\n",
    "better result when there is a parallel shift in the yield curve. \n",
    "An opportunity for arbitrageurs:\n",
    "- Invest a certain USD amount in the barbell, and\n",
    "- Short the same USD amount of the bullet\n",
    "\n",
    "This would be profitable if shifts in the term structure were always parallel.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74eb544",
   "metadata": {},
   "source": [
    "### Key rate shifts "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8038517c",
   "metadata": {},
   "source": [
    "\n",
    "Suppose we consider three spot rates: the two-year rate, the five-year\n",
    "rate, and the ten-year rate. Each of these can affect spot rates in\n",
    "their neighborhoods, and the combined effect of all three key rate\n",
    "shifts is a one-basis-point shift in all rates. Shifts in each of\n",
    "those spot rates can be defined as shown belo.  These shift are\n",
    "sometimes referred to as __key rate shifts__, and providea way of\n",
    "splitting the DV01 measure into three separate measures.\n",
    "\n",
    "The impact of the shifts are called __partial 01s__ or __key rate 01s__ (KR01s), e.g.:\n",
    "- KR01$_1$: The reduction in a portfolio’s value from a one-basis-point increase \n",
    "in the two-year spot rate\n",
    "- KR01$_2$: The reduction in a portfolio’s value from a one-basis-point increase \n",
    "in the five-year spot rate\n",
    "- KR01$_3$: The reduction in a portfolio’s value from a one-basis-point increase \n",
    "in the ten-year spot rate\n",
    "\n",
    "It follows that: $DV01 = KR01_1 + KR01_2 + KR01_3$.  A bond\n",
    "investor can estimate portfolio values under, or hedge against, a wider\n",
    "range of (small) term structure movements.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e36c7540",
   "metadata": {},
   "outputs": [],
   "source": [
    "KR = [2, 5, 10]\n",
    "DV01 = dict()\n",
    "for maturity in np.arange(0, 15, 0.5):\n",
    "    if maturity <= KR[0]:\n",
    "        changes = [1, 0, 0]\n",
    "    elif maturity >= KR[2]:\n",
    "        changes = [0, 0, 1]\n",
    "    elif maturity < KR[1]:\n",
    "        diff = (maturity - KR[0])/(KR[1] - KR[0])\n",
    "        changes = [1 - diff, diff, 0]\n",
    "    else:\n",
    "        diff = (KR[2] - maturity)/(KR[2] - KR[1])\n",
    "        changes = [0, diff, 1-diff]\n",
    "    DV01[maturity] = changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee1a1e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "DataFrame.from_dict(DV01, orient='index', columns=[f\"{y}Y-rate\" for y in KR])\\\n",
    "         .plot(ax=ax)\n",
    "ax.legend()\n",
    "ax.set_title('Changes in all rates when key rate is increased by one basis point')\n",
    "ax.set_ylabel('Rate increase (bp)')\n",
    "ax.set_xlabel('Maturity (years)')\n",
    "ax.set_ylim(top=2)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47db2145",
   "metadata": {},
   "source": [
    "## Bond risk factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbc2cdba",
   "metadata": {},
   "outputs": [],
   "source": [
    "alf = Alfred(api_key=credentials['fred']['api_key'], verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "638598a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32413 BofA Merrill Lynch Total Bond Return Index Values\n"
     ]
    }
   ],
   "source": [
    "# get Merrill Lynch bond indexes\n",
    "freq = 'D'    # periodicity 'M' or 'D'\n",
    "cat = alf.get_category(32413)\n",
    "print(cat['id'], cat['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1675b335",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get bond index returns\n",
    "bonds = []   # to accumulate bond returns\n",
    "for s in cat['series']:\n",
    "    bonds.append(alf(s['id'], start=19961231, freq=freq) )\n",
    "bonds_df = pd.concat(bonds, axis=1).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e47ed96d",
   "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></th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>notna</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>15</th>\n",
       "      <td>19961231</td>\n",
       "      <td>19971230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>16</th>\n",
       "      <td>19971231</td>\n",
       "      <td>19981230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>33</th>\n",
       "      <td>19981231</td>\n",
       "      <td>20031230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>48</th>\n",
       "      <td>20031231</td>\n",
       "      <td>20181108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>46</th>\n",
       "      <td>20181109</td>\n",
       "      <td>20181109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>48</th>\n",
       "      <td>20181112</td>\n",
       "      <td>20240418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                first      last\n",
       "notna count                    \n",
       "1     15     19961231  19971230\n",
       "2     16     19971231  19981230\n",
       "3     33     19981231  20031230\n",
       "4     48     20031231  20181108\n",
       "5     46     20181109  20181109\n",
       "6     48     20181112  20240418"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show blocks of data availability\n",
    "counts = bonds_df.notna().sum(axis=1).rename('count')\n",
    "counts = pd.concat([counts, (counts != counts.shift()).cumsum().rename('notna')], axis=1)\n",
    "counts = counts.reset_index().groupby(['notna', 'count'])['date'].agg(['first','last'])\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "22f30c3f",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>BAMLCC0A0CMTRIV</th>\n",
       "      <th>BAMLCC0A1AAATRIV</th>\n",
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       "      <th>BAMLCC4A0710YTRIV</th>\n",
       "      <th>BAMLCC7A01015YTRIV</th>\n",
       "      <th>...</th>\n",
       "      <th>BAMLEMPTPRVICRPITRIV</th>\n",
       "      <th>BAMLEMRACRPIASIATRIV</th>\n",
       "      <th>BAMLEMRECRPIEMEATRIV</th>\n",
       "      <th>BAMLEMRLCRPILATRIV</th>\n",
       "      <th>BAMLEMUBCRPIUSTRIV</th>\n",
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       "      <th>BAMLHYH0A3CMTRIV</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",
       "      <th></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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19990104</th>\n",
       "      <td>-0.001053</td>\n",
       "      <td>-0.001421</td>\n",
       "      <td>-0.000660</td>\n",
       "      <td>-0.001166</td>\n",
       "      <td>-0.000995</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>-0.000519</td>\n",
       "      <td>-0.001209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001599</td>\n",
       "      <td>-0.000700</td>\n",
       "      <td>-0.005616</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.001490</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.001999</td>\n",
       "      <td>0.003234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19990105</th>\n",
       "      <td>-0.003846</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>-0.003500</td>\n",
       "      <td>-0.003857</td>\n",
       "      <td>-0.003952</td>\n",
       "      <td>-0.000844</td>\n",
       "      <td>-0.002452</td>\n",
       "      <td>-0.003343</td>\n",
       "      <td>-0.004276</td>\n",
       "      <td>-0.004990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.008113</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.002394</td>\n",
       "      <td>0.012287</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>-0.000834</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>0.005428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19990106</th>\n",
       "      <td>0.002388</td>\n",
       "      <td>0.002099</td>\n",
       "      <td>0.002102</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>0.001967</td>\n",
       "      <td>0.002537</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007144</td>\n",
       "      <td>0.003395</td>\n",
       "      <td>0.009135</td>\n",
       "      <td>0.006251</td>\n",
       "      <td>0.006060</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.001487</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.001041</td>\n",
       "      <td>0.003480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19990107</th>\n",
       "      <td>-0.002694</td>\n",
       "      <td>-0.002813</td>\n",
       "      <td>-0.002413</td>\n",
       "      <td>-0.002573</td>\n",
       "      <td>-0.002996</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>-0.000853</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>-0.002662</td>\n",
       "      <td>-0.003371</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001484</td>\n",
       "      <td>-0.003095</td>\n",
       "      <td>0.007386</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>-0.000694</td>\n",
       "      <td>0.003932</td>\n",
       "      <td>-0.000112</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.001005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19990108</th>\n",
       "      <td>-0.002583</td>\n",
       "      <td>-0.002941</td>\n",
       "      <td>-0.002419</td>\n",
       "      <td>-0.002658</td>\n",
       "      <td>-0.002504</td>\n",
       "      <td>-0.000920</td>\n",
       "      <td>-0.002006</td>\n",
       "      <td>-0.003309</td>\n",
       "      <td>-0.003773</td>\n",
       "      <td>-0.004368</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000594</td>\n",
       "      <td>0.001998</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>-0.002079</td>\n",
       "      <td>-0.000892</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001737</td>\n",
       "      <td>-0.000918</td>\n",
       "      <td>0.002509</td>\n",
       "      <td>0.007643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240411</th>\n",
       "      <td>-0.001330</td>\n",
       "      <td>-0.001659</td>\n",
       "      <td>-0.001234</td>\n",
       "      <td>-0.001240</td>\n",
       "      <td>-0.001418</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>-0.000040</td>\n",
       "      <td>-0.000588</td>\n",
       "      <td>-0.001859</td>\n",
       "      <td>-0.001963</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>-0.000714</td>\n",
       "      <td>-0.003072</td>\n",
       "      <td>-0.003734</td>\n",
       "      <td>-0.002164</td>\n",
       "      <td>-0.001999</td>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.001767</td>\n",
       "      <td>-0.001925</td>\n",
       "      <td>-0.002184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240412</th>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.001828</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.001592</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.001509</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.001789</td>\n",
       "      <td>0.002657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001016</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>-0.000522</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>-0.000229</td>\n",
       "      <td>-0.001196</td>\n",
       "      <td>-0.000885</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>-0.002668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240415</th>\n",
       "      <td>-0.006152</td>\n",
       "      <td>-0.008150</td>\n",
       "      <td>-0.006125</td>\n",
       "      <td>-0.006174</td>\n",
       "      <td>-0.006110</td>\n",
       "      <td>-0.000410</td>\n",
       "      <td>-0.002345</td>\n",
       "      <td>-0.004446</td>\n",
       "      <td>-0.006793</td>\n",
       "      <td>-0.008662</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002430</td>\n",
       "      <td>-0.002237</td>\n",
       "      <td>-0.003116</td>\n",
       "      <td>-0.004952</td>\n",
       "      <td>-0.003309</td>\n",
       "      <td>-0.000858</td>\n",
       "      <td>-0.002717</td>\n",
       "      <td>-0.002836</td>\n",
       "      <td>-0.002138</td>\n",
       "      <td>-0.003969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240416</th>\n",
       "      <td>-0.004341</td>\n",
       "      <td>-0.004776</td>\n",
       "      <td>-0.003966</td>\n",
       "      <td>-0.004142</td>\n",
       "      <td>-0.004571</td>\n",
       "      <td>-0.001117</td>\n",
       "      <td>-0.002740</td>\n",
       "      <td>-0.003974</td>\n",
       "      <td>-0.004730</td>\n",
       "      <td>-0.005714</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002959</td>\n",
       "      <td>-0.002457</td>\n",
       "      <td>-0.002725</td>\n",
       "      <td>-0.004515</td>\n",
       "      <td>-0.003117</td>\n",
       "      <td>-0.004533</td>\n",
       "      <td>-0.004080</td>\n",
       "      <td>-0.003503</td>\n",
       "      <td>-0.004105</td>\n",
       "      <td>-0.006513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240417</th>\n",
       "      <td>0.004802</td>\n",
       "      <td>0.006168</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.004873</td>\n",
       "      <td>0.004725</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.004275</td>\n",
       "      <td>0.005568</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>0.002147</td>\n",
       "      <td>0.001095</td>\n",
       "      <td>0.001602</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>-0.000130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6603 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          BAMLCC0A0CMTRIV  BAMLCC0A1AAATRIV  BAMLCC0A2AATRIV  BAMLCC0A3ATRIV  \\\n",
       "date                                                                           \n",
       "19990104        -0.001053         -0.001421        -0.000660       -0.001166   \n",
       "19990105        -0.003846         -0.004194        -0.003500       -0.003857   \n",
       "19990106         0.002388          0.002099         0.002102        0.002495   \n",
       "19990107        -0.002694         -0.002813        -0.002413       -0.002573   \n",
       "19990108        -0.002583         -0.002941        -0.002419       -0.002658   \n",
       "...                   ...               ...              ...             ...   \n",
       "20240411        -0.001330         -0.001659        -0.001234       -0.001240   \n",
       "20240412         0.001673          0.001828         0.001822        0.001739   \n",
       "20240415        -0.006152         -0.008150        -0.006125       -0.006174   \n",
       "20240416        -0.004341         -0.004776        -0.003966       -0.004142   \n",
       "20240417         0.004802          0.006168         0.004689        0.004873   \n",
       "\n",
       "          BAMLCC0A4BBBTRIV  BAMLCC1A013YTRIV  BAMLCC2A035YTRIV  \\\n",
       "date                                                             \n",
       "19990104         -0.000995          0.000076          0.000473   \n",
       "19990105         -0.003952         -0.000844         -0.002452   \n",
       "19990106          0.002419          0.000466          0.000901   \n",
       "19990107         -0.002996          0.000101         -0.000853   \n",
       "19990108         -0.002504         -0.000920         -0.002006   \n",
       "...                    ...               ...               ...   \n",
       "20240411         -0.001418          0.000582         -0.000040   \n",
       "20240412          0.001592          0.000727          0.001509   \n",
       "20240415         -0.006110         -0.000410         -0.002345   \n",
       "20240416         -0.004571         -0.001117         -0.002740   \n",
       "20240417          0.004725          0.001034          0.002748   \n",
       "\n",
       "          BAMLCC3A057YTRIV  BAMLCC4A0710YTRIV  BAMLCC7A01015YTRIV  ...  \\\n",
       "date                                                               ...   \n",
       "19990104          0.000534          -0.000519           -0.001209  ...   \n",
       "19990105         -0.003343          -0.004276           -0.004990  ...   \n",
       "19990106          0.001695           0.001967            0.002537  ...   \n",
       "19990107         -0.001561          -0.002662           -0.003371  ...   \n",
       "19990108         -0.003309          -0.003773           -0.004368  ...   \n",
       "...                    ...                ...                 ...  ...   \n",
       "20240411         -0.000588          -0.001859           -0.001963  ...   \n",
       "20240412          0.002151           0.001789            0.002657  ...   \n",
       "20240415         -0.004446          -0.006793           -0.008662  ...   \n",
       "20240416         -0.003974          -0.004730           -0.005714  ...   \n",
       "20240417          0.004275           0.005568            0.006826  ...   \n",
       "\n",
       "          BAMLEMPTPRVICRPITRIV  BAMLEMRACRPIASIATRIV  BAMLEMRECRPIEMEATRIV  \\\n",
       "date                                                                         \n",
       "19990104              0.001599             -0.000700             -0.005616   \n",
       "19990105              0.002692              0.000400              0.008113   \n",
       "19990106              0.007144              0.003395              0.009135   \n",
       "19990107             -0.001484             -0.003095              0.007386   \n",
       "19990108             -0.000594              0.001998              0.000392   \n",
       "...                        ...                   ...                   ...   \n",
       "20240411             -0.001561             -0.000714             -0.003072   \n",
       "20240412              0.001016              0.001856              0.000311   \n",
       "20240415             -0.002430             -0.002237             -0.003116   \n",
       "20240416             -0.002959             -0.002457             -0.002725   \n",
       "20240417              0.001567              0.002147              0.001095   \n",
       "\n",
       "          BAMLEMRLCRPILATRIV  BAMLEMUBCRPIUSTRIV  BAMLHE00EHYITRIV  \\\n",
       "date                                                                 \n",
       "19990104            0.002297            0.001099          0.000195   \n",
       "19990105            0.002392            0.002394          0.012287   \n",
       "19990106            0.006251            0.006060          0.000577   \n",
       "19990107            0.000198           -0.000694          0.003932   \n",
       "19990108           -0.002079           -0.000892          0.000000   \n",
       "...                      ...                 ...               ...   \n",
       "20240411           -0.003734           -0.002164         -0.001999   \n",
       "20240412           -0.000522            0.000781         -0.000229   \n",
       "20240415           -0.004952           -0.003309         -0.000858   \n",
       "20240416           -0.004515           -0.003117         -0.004533   \n",
       "20240417            0.001602            0.001795          0.001092   \n",
       "\n",
       "          BAMLHYH0A0HYM2TRIV  BAMLHYH0A1BBTRIV  BAMLHYH0A2BTRIV  \\\n",
       "date                                                              \n",
       "19990104            0.001490          0.000250         0.001999   \n",
       "19990105            0.000674         -0.000834         0.000781   \n",
       "19990106            0.001487          0.001583         0.001041   \n",
       "19990107           -0.000112         -0.001250         0.000520   \n",
       "19990108            0.001737         -0.000918         0.002509   \n",
       "...                      ...               ...              ...   \n",
       "20240411           -0.001882         -0.001767        -0.001925   \n",
       "20240412           -0.001196         -0.000885        -0.001148   \n",
       "20240415           -0.002717         -0.002836        -0.002138   \n",
       "20240416           -0.004080         -0.003503        -0.004105   \n",
       "20240417            0.000397          0.000712         0.000162   \n",
       "\n",
       "          BAMLHYH0A3CMTRIV  \n",
       "date                        \n",
       "19990104          0.003234  \n",
       "19990105          0.005428  \n",
       "19990106          0.003480  \n",
       "19990107          0.001005  \n",
       "19990108          0.007643  \n",
       "...                    ...  \n",
       "20240411         -0.002184  \n",
       "20240412         -0.002668  \n",
       "20240415         -0.003969  \n",
       "20240416         -0.006513  \n",
       "20240417         -0.000130  \n",
       "\n",
       "[6603 rows x 33 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose start date with good data availability\n",
    "start_date = 19981231\n",
    "rets = bonds_df.loc[bonds_df.index >= start_date,\n",
    "                    bonds_df.loc[start_date].notna().values].iloc[:-1]\n",
    "rets = pd.concat([alf.transform(rets[col], log=1, diff=1)\n",
    "                  for col in rets.columns], axis=1).dropna()\n",
    "rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "307c6a24",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bond Index Total Returns\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>series</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAMLCC0A0CMTRIV</th>\n",
       "      <td>ICE BofA US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC0A1AAATRIV</th>\n",
       "      <td>ICE BofA AAA US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC0A2AATRIV</th>\n",
       "      <td>ICE BofA AA US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC0A3ATRIV</th>\n",
       "      <td>ICE BofA Single-A US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC0A4BBBTRIV</th>\n",
       "      <td>ICE BofA BBB US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC1A013YTRIV</th>\n",
       "      <td>ICE BofA 1-3 Year US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC2A035YTRIV</th>\n",
       "      <td>ICE BofA 3-5 Year US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC3A057YTRIV</th>\n",
       "      <td>ICE BofA 5-7 Year US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC4A0710YTRIV</th>\n",
       "      <td>ICE BofA 7-10 Year US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC7A01015YTRIV</th>\n",
       "      <td>ICE BofA 10-15 Year US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLCC8A015PYTRIV</th>\n",
       "      <td>ICE BofA 15+ Year US Corporate Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEM1BRRAAA2ACRPITRIV</th>\n",
       "      <td>ICE BofA AAA-A Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEM2BRRBBBCRPITRIV</th>\n",
       "      <td>ICE BofA BBB Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEM3BRRBBCRPITRIV</th>\n",
       "      <td>ICE BofA BB Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEM4BRRBLCRPITRIV</th>\n",
       "      <td>ICE BofA B &amp; Lower Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEM5BCOCRPITRIV</th>\n",
       "      <td>ICE BofA Crossover Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMCBPITRIV</th>\n",
       "      <td>ICE BofA Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMEBCRPIETRIV</th>\n",
       "      <td>ICE BofA Euro Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMFSFCRPITRIV</th>\n",
       "      <td>ICE BofA Private Sector Financial Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMHBHYCRPITRIV</th>\n",
       "      <td>ICE BofA High Yield Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMIBHGCRPITRIV</th>\n",
       "      <td>ICE BofA High Grade Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMNSNFCRPITRIV</th>\n",
       "      <td>ICE BofA Non-Financial Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMPBPUBSICRPITRIV</th>\n",
       "      <td>ICE BofA Public Sector Issuers Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMPTPRVICRPITRIV</th>\n",
       "      <td>ICE BofA Private Sector Issuers Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMRACRPIASIATRIV</th>\n",
       "      <td>ICE BofA Asia Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMRECRPIEMEATRIV</th>\n",
       "      <td>ICE BofA EMEA Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMRLCRPILATRIV</th>\n",
       "      <td>ICE BofA Latin America Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLEMUBCRPIUSTRIV</th>\n",
       "      <td>ICE BofA US Emerging Markets Corporate Plus Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLHE00EHYITRIV</th>\n",
       "      <td>ICE BofA Euro High Yield Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLHYH0A0HYM2TRIV</th>\n",
       "      <td>ICE BofA US High Yield Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLHYH0A1BBTRIV</th>\n",
       "      <td>ICE BofA BB US High Yield Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLHYH0A2BTRIV</th>\n",
       "      <td>ICE BofA Single-B US High Yield Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLHYH0A3CMTRIV</th>\n",
       "      <td>ICE BofA CCC &amp; Lower US High Yield Index Total Return Index Value</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                    title\n",
       "series                                                                                                                   \n",
       "BAMLCC0A0CMTRIV                                                      ICE BofA US Corporate Index Total Return Index Value\n",
       "BAMLCC0A1AAATRIV                                                 ICE BofA AAA US Corporate Index Total Return Index Value\n",
       "BAMLCC0A2AATRIV                                                   ICE BofA AA US Corporate Index Total Return Index Value\n",
       "BAMLCC0A3ATRIV                                              ICE BofA Single-A US Corporate Index Total Return Index Value\n",
       "BAMLCC0A4BBBTRIV                                                 ICE BofA BBB US Corporate Index Total Return Index Value\n",
       "BAMLCC1A013YTRIV                                            ICE BofA 1-3 Year US Corporate Index Total Return Index Value\n",
       "BAMLCC2A035YTRIV                                            ICE BofA 3-5 Year US Corporate Index Total Return Index Value\n",
       "BAMLCC3A057YTRIV                                            ICE BofA 5-7 Year US Corporate Index Total Return Index Value\n",
       "BAMLCC4A0710YTRIV                                          ICE BofA 7-10 Year US Corporate Index Total Return Index Value\n",
       "BAMLCC7A01015YTRIV                                        ICE BofA 10-15 Year US Corporate Index Total Return Index Value\n",
       "BAMLCC8A015PYTRIV                                           ICE BofA 15+ Year US Corporate Index Total Return Index Value\n",
       "BAMLEM1BRRAAA2ACRPITRIV                     ICE BofA AAA-A Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEM2BRRBBBCRPITRIV                         ICE BofA BBB Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEM3BRRBBCRPITRIV                           ICE BofA BB Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEM4BRRBLCRPITRIV                    ICE BofA B & Lower Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEM5BCOCRPITRIV                      ICE BofA Crossover Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMCBPITRIV                                    ICE BofA Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMEBCRPIETRIV                            ICE BofA Euro Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMFSFCRPITRIV        ICE BofA Private Sector Financial Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMHBHYCRPITRIV                     ICE BofA High Yield Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMIBHGCRPITRIV                     ICE BofA High Grade Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMNSNFCRPITRIV                  ICE BofA Non-Financial Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMPBPUBSICRPITRIV       ICE BofA Public Sector Issuers Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMPTPRVICRPITRIV       ICE BofA Private Sector Issuers Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMRACRPIASIATRIV                         ICE BofA Asia Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMRECRPIEMEATRIV                         ICE BofA EMEA Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMRLCRPILATRIV                  ICE BofA Latin America Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLEMUBCRPIUSTRIV                             ICE BofA US Emerging Markets Corporate Plus Index Total Return Index Value\n",
       "BAMLHE00EHYITRIV                                                  ICE BofA Euro High Yield Index Total Return Index Value\n",
       "BAMLHYH0A0HYM2TRIV                                                  ICE BofA US High Yield Index Total Return Index Value\n",
       "BAMLHYH0A1BBTRIV                                                 ICE BofA BB US High Yield Index Total Return Index Value\n",
       "BAMLHYH0A2BTRIV                                            ICE BofA Single-B US High Yield Index Total Return Index Value\n",
       "BAMLHYH0A3CMTRIV                                        ICE BofA CCC & Lower US High Yield Index Total Return Index Value"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', None)\n",
    "print(\"Bond Index Total Returns\")\n",
    "Series(alf.header(rets.columns), index=rets.columns, name='title')\\\n",
    "    .to_frame().rename_axis('series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cabe4b06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.90633015e-01 1.84670310e-01 7.66846462e-02 2.31249861e-02\n",
      " 2.08709778e-02 1.73997768e-02 1.59048070e-02 1.11428201e-02\n",
      " 1.08065948e-02 9.53333795e-03 7.17195056e-03 5.80339671e-03\n",
      " 5.13102872e-03 4.61504627e-03 3.95216602e-03 2.55170452e-03\n",
      " 2.42215195e-03 2.22998793e-03 1.31893510e-03 9.51987980e-04\n",
      " 7.84773755e-04 6.15732469e-04 3.99797350e-04 3.69901192e-04\n",
      " 3.07591420e-04 2.74143218e-04 9.27920937e-05 8.50594135e-05\n",
      " 5.40645899e-05 3.96089404e-05 3.55645916e-05 1.55810718e-05\n",
      " 5.76269423e-06]\n"
     ]
    },
    {
     "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>Cumulative Variance Ratio Explained</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.590633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.775303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.851988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.875113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.895984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.913384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.929289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.940431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.951238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.960771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cumulative Variance Ratio Explained\n",
       "1                              0.590633\n",
       "2                              0.775303\n",
       "3                              0.851988\n",
       "4                              0.875113\n",
       "5                              0.895984\n",
       "6                              0.913384\n",
       "7                              0.929289\n",
       "8                              0.940431\n",
       "9                              0.951238\n",
       "10                             0.960771"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract principal components of bond index returns\n",
    "pipe = Pipeline([('scaler', StandardScaler()), ('pca', PCA())])\n",
    "pipe.fit(rets)\n",
    "print(pipe.named_steps['pca'].explained_variance_ratio_)  # sanity check\n",
    "scree = Series(pipe.named_steps['pca'].explained_variance_ratio_,\n",
    "               index=np.arange(1, rets.shape[1]+1))\n",
    "DataFrame(scree.cumsum().rename('Cumulative Variance Ratio Explained')).iloc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4629dff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scree plot\n",
    "fig, ax = plt.subplots(num=1, clear=True, figsize=(10, 6))\n",
    "scree.plot(kind='bar', rot=0, width=.8, ax=ax)\n",
    "ax.set_title('Scree Plot: PCA of FRED BofA Bond Return Indexes', fontsize=16)\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.set_ylabel(\"Percent Variance Explained\", fontsize=14)\n",
    "ax.set_xlabel(\"Principal Component\", fontsize=14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c6ee701",
   "metadata": {},
   "source": [
    "### Explainability of Statistical Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7428dedc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract factor returns from bond indexes with PCA projection\n",
    "K = 4\n",
    "factors = DataFrame(pipe.transform(rets)[:, :K],\n",
    "                    columns=[f\"PC{c+1}\" for c in range(K)],\n",
    "                    index=pd.DatetimeIndex(rets.index.astype(str), freq='infer'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6f8cec",
   "metadata": {},
   "source": [
    "Construct changes in interest rate spreads:\n",
    "- level: average of 2-year and 10-year treasury rates\n",
    "- slope: 10-year minus 2-year rates\n",
    "- twist: (10-year minus 5-year) minus (5-year minus 2-year)\n",
    "- credit: BAA minus 10-year treasury rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9bf8e3aa",
   "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>level</th>\n",
       "      <th>credit</th>\n",
       "      <th>slope</th>\n",
       "      <th>twist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-01-03</th>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>1.776357e-15</td>\n",
       "      <td>1.776357e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-06</th>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>-1.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-07</th>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-6.000000e-02</td>\n",
       "      <td>-8.881784e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-08</th>\n",
       "      <td>0.155</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>7.000000e-02</td>\n",
       "      <td>3.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-09</th>\n",
       "      <td>0.160</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-4.000000e-02</td>\n",
       "      <td>-8.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-12</th>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-1.000000e-02</td>\n",
       "      <td>3.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-15</th>\n",
       "      <td>0.090</td>\n",
       "      <td>0.01</td>\n",
       "      <td>8.000000e-02</td>\n",
       "      <td>-4.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-16</th>\n",
       "      <td>0.040</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-17</th>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-4.000000e-02</td>\n",
       "      <td>2.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-18</th>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-8.881784e-16</td>\n",
       "      <td>-2.000000e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9580 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            level  credit         slope         twist\n",
       "date                                                 \n",
       "1986-01-03  0.010   -0.04  1.776357e-15  1.776357e-15\n",
       "1986-01-06  0.015   -0.01  1.000000e-02 -1.000000e-02\n",
       "1986-01-07 -0.100    0.06 -6.000000e-02 -8.881784e-16\n",
       "1986-01-08  0.155   -0.14  7.000000e-02  3.000000e-02\n",
       "1986-01-09  0.160   -0.05 -4.000000e-02 -8.000000e-02\n",
       "...           ...     ...           ...           ...\n",
       "2024-04-12 -0.055    0.04 -1.000000e-02  3.000000e-02\n",
       "2024-04-15  0.090    0.01  8.000000e-02 -4.000000e-02\n",
       "2024-04-16  0.040    0.00  0.000000e+00  0.000000e+00\n",
       "2024-04-17 -0.060    0.02 -4.000000e-02  2.000000e-02\n",
       "2024-04-18  0.050   -0.01 -8.881784e-16 -2.000000e-02\n",
       "\n",
       "[9580 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct interest rate spread changes\n",
    "spreads = pd.concat([alf(s, freq=freq) for s in ['BAA10Y', 'DGS10', 'DGS5', 'DGS2']],\n",
    "                     axis=1)\\\n",
    "            .sort_index()\n",
    "spreads.index = pd.DatetimeIndex(spreads.index.astype(str), freq='infer')\n",
    "spreads['level'] = 0.5 * (spreads['DGS2'] + spreads['DGS10'])\n",
    "spreads['credit'] = spreads['BAA10Y']\n",
    "spreads['slope'] = spreads['DGS10'] - spreads['DGS2']\n",
    "spreads['twist'] = ((spreads['DGS10'] - spreads['DGS5']) -\n",
    "                    (spreads['DGS5'] - spreads['DGS2']))\n",
    "spreads = spreads.drop(columns=['BAA10Y', 'DGS10', 'DGS5', 'DGS2'])\\\n",
    "                 .ffill()\\\n",
    "                 .diff()\\\n",
    "                 .dropna()\n",
    "spreads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f5af148a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation of bond factors and interest rate spread changes')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show correlations between bond factor returns and spread changes\n",
    "data = pd.concat([spreads, factors], axis=1, join='inner')\n",
    "corr = data.corr()\n",
    "#plt.imshow(corr**2, vmin=0, vmax=1, cmap='Purples')\n",
    "plt.imshow(corr, vmin=-1, vmax=1, cmap='seismic')\n",
    "plt.xticks(range(len(corr)), corr.index)\n",
    "plt.yticks(range(len(corr)), corr.index)\n",
    "plt.colorbar()\n",
    "plt.title('Correlation of bond factors and interest rate spread changes')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7a5c09d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    PC1   R-squared:                       0.540\n",
      "Model:                            OLS   Adj. R-squared:                  0.540\n",
      "Method:                 Least Squares   F-statistic:                     422.9\n",
      "Date:                Sun, 21 Apr 2024   Prob (F-statistic):          2.47e-323\n",
      "Time:                        13:15:12   Log-Likelihood:                -16027.\n",
      "No. Observations:                6318   AIC:                         3.206e+04\n",
      "Df Residuals:                    6313   BIC:                         3.210e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0222      0.091      0.245      0.807      -0.156       0.200\n",
      "credit        66.9094     10.707      6.249      0.000      45.923      87.895\n",
      "level         66.9367      3.158     21.195      0.000      60.747      73.127\n",
      "slope          9.1005      2.491      3.653      0.000       4.218      13.983\n",
      "twist        -13.9458      2.207     -6.319      0.000     -18.271      -9.620\n",
      "==============================================================================\n",
      "Omnibus:                     4119.817   Durbin-Watson:                   1.162\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           216965.568\n",
      "Skew:                           2.469   Prob(JB):                         0.00\n",
      "Kurtosis:                      31.281   Cond. No.                         40.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 63 lags and without small sample correction\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    PC2   R-squared:                       0.492\n",
      "Model:                            OLS   Adj. R-squared:                  0.492\n",
      "Method:                 Least Squares   F-statistic:                     273.9\n",
      "Date:                Sun, 21 Apr 2024   Prob (F-statistic):          1.99e-217\n",
      "Time:                        13:15:12   Log-Likelihood:                -12670.\n",
      "No. Observations:                6318   AIC:                         2.535e+04\n",
      "Df Residuals:                    6313   BIC:                         2.538e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0038      0.049      0.079      0.937      -0.092       0.099\n",
      "credit         7.9067      3.362      2.352      0.019       1.318      14.496\n",
      "level        -28.0466      1.680    -16.695      0.000     -31.339     -24.754\n",
      "slope        -14.4437      1.191    -12.131      0.000     -16.777     -12.110\n",
      "twist          0.1590      1.291      0.123      0.902      -2.372       2.690\n",
      "==============================================================================\n",
      "Omnibus:                     4046.973   Durbin-Watson:                   1.259\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           201753.832\n",
      "Skew:                           2.419   Prob(JB):                         0.00\n",
      "Kurtosis:                      30.258   Cond. No.                         40.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 63 lags and without small sample correction\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    PC3   R-squared:                       0.049\n",
      "Model:                            OLS   Adj. R-squared:                  0.048\n",
      "Method:                 Least Squares   F-statistic:                     9.627\n",
      "Date:                Sun, 21 Apr 2024   Prob (F-statistic):           9.28e-08\n",
      "Time:                        13:15:12   Log-Likelihood:                -11869.\n",
      "No. Observations:                6318   AIC:                         2.375e+04\n",
      "Df Residuals:                    6313   BIC:                         2.378e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0046      0.025     -0.181      0.857      -0.054       0.045\n",
      "credit       -12.9168      2.346     -5.507      0.000     -17.514      -8.319\n",
      "level         -2.0945      1.339     -1.564      0.118      -4.719       0.530\n",
      "slope          1.7102      1.241      1.378      0.168      -0.722       4.143\n",
      "twist          0.3878      1.103      0.352      0.725      -1.773       2.549\n",
      "==============================================================================\n",
      "Omnibus:                     3422.015   Durbin-Watson:                   1.565\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           498792.470\n",
      "Skew:                           1.565   Prob(JB):                         0.00\n",
      "Kurtosis:                      46.416   Cond. No.                         40.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 63 lags and without small sample correction\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    PC4   R-squared:                       0.024\n",
      "Model:                            OLS   Adj. R-squared:                  0.023\n",
      "Method:                 Least Squares   F-statistic:                     12.35\n",
      "Date:                Sun, 21 Apr 2024   Prob (F-statistic):           5.27e-10\n",
      "Time:                        13:15:12   Log-Likelihood:                -8150.9\n",
      "No. Observations:                6318   AIC:                         1.631e+04\n",
      "Df Residuals:                    6313   BIC:                         1.635e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0015      0.013     -0.111      0.911      -0.027       0.024\n",
      "credit         2.6211      1.654      1.585      0.113      -0.620       5.862\n",
      "level          0.6178      0.766      0.807      0.420      -0.883       2.119\n",
      "slope          2.7265      0.431      6.329      0.000       1.882       3.571\n",
      "twist          1.6352      0.454      3.606      0.000       0.746       2.524\n",
      "==============================================================================\n",
      "Omnibus:                     2609.560   Durbin-Watson:                   1.953\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           152469.248\n",
      "Skew:                           1.171   Prob(JB):                         0.00\n",
      "Kurtosis:                      26.952   Cond. No.                         40.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 63 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "# Show regression fits\n",
    "for pc in range(K):\n",
    "    print(smf.ols(f\"PC{pc+1} ~ credit + level + slope + twist\", data=data)\\\n",
    "          .fit(cov_type='HAC', cov_kwds={'maxlags': 63})\\\n",
    "          .summary())"
   ]
  }
 ],
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