GIT_MSM

1.) The first part of this code will refer to Calvet and Fisher as the relying theory and the process of estimating 4 parameters of the model. I only convert the code from MATLAB into Python3.

% ------------------------------------------------------------------------- % Markov Switching Multifractal (MSM) % Maximum likelihood estimation % v1.0 % Copyright ? 2010 Multifractal-finance.com % ------------------------------------------------------------------------- % % USAGE % [PARAMETERS] = MSM(DATA, K) % [PARAMETERS, LL, LLs, DIAGNOSTICS] = MSM(DATA, K, STARTING_VALUES, OPTIONS) % % INPUTS: % DATA - A column (or row) of mean zero data % KBAR - The number of frequency components % STARTINGVALS - [OPTIONAL] Starting values for optimization % [b, m0, gamma_k, sigma] % b - (1,inf) % m0 - (1,2] % gamma_k - (0,1) % sigma - [0,inf) % OPTIONS - {OPTIONAL} User provided options structure % % OUTPUTS: % PARAMETERS - A 4x1 row vector of parameters % [b, m0, gamma_k, sigma] % LL - The log-likelihood at the optimum % LLs - Individual daily log-likelihoods at optimum % diagnostics - Structure of optimization output information. % Useful for checking convergence problems % % ASSOCIATED FILES: % MSM_likelihood.m, MSM_parameter_check.m, MSM_starting_values.m % % REFERENCES: % [1] Calvet, L., Adlai Fisher (2004). "How to Forecast long-run % volatility: regime-switching and the estimation of multifractal processes". Journal of Financial Econometrics 2: 49?83. % [2] Calvet, L., Adlai Fisher (2008). "Multifractal Volatility: Theory, Forecasting and Pricing". Elsevier - Academic Press.. % -------------------------------------------------------------------------

2.) After that, I will try to forecast by using those parameters and do a model valuation both in-sample and out-of sample test. Which is the part of my Independent Study (IS) plan for Master degree In Finance at Thammasat University, Thailand.