Maximum Likelihood Estimation of the Markov-Switching GARCH Model
Computational Statistics & Data Analysis (DOI: 10.1016/j.csda.2013.01.026)
32 Pages Posted: 29 Dec 2016
Date Written: April 20, 2013
Abstract
The Markov-switching GARCH model offers rich dynamics to model financial data. Estimating this path dependent model is a challenging task because exact computation of the likelihood is infeasible in practice. This difficulty led to estimation procedures either based on a simplification of the model or not dependent on the likelihood. There is no method available to obtain the maximum likelihood estimator without resorting to a modification of the model. A novel approach is developed based on both the Monte Carlo expectation-maximization algorithm and importance sampling to calculate the maximum likelihood estimator and asymptotic variance-covariance matrix of the Markov-switching GARCH model. Practical implementation of the proposed algorithm is discussed and its effectiveness is demonstrated in simulation and empirical studies.
Keywords: Markov-switching, GARCH, EM algorithm, importance sampling
JEL Classification: C13, C51, C63
Suggested Citation: Suggested Citation