Maximum likelihood estimation of the Markov-switching GARCH model

Maciej Augustyniak

Computational Statistics & Data Analysis, 2014, vol. 76, issue C, 61-75

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 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:76:y:2014:i:c:p:61-75

DOI: 10.1016/j.csda.2013.01.026

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