期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 76, 期 -, 页码 61-75出版社
ELSEVIER
DOI: 10.1016/j.csda.2013.01.026
关键词
Markov-switching; GARCH; EM algorithm; Importance sampling
资金
- Natural Sciences and Engineering Research Council of Canada
- Society of Actuaries
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. (C) 2013 Elsevier B.V. All rights reserved.
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