Volatility forecasting with double Markov switching GARCH models

Cathy W. S. Chen (), Mike K. P. So and Edward Lin ()
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Mike K. P. So: Hong Kong University of Science and Technology, Hong Kong, Postal: Hong Kong University of Science and Technology, Hong Kong

Journal of Forecasting, 2009, vol. 28, issue 8, 681-697

Abstract: This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first-order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value-at-risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:28:y:2009:i:8:p:681-697

DOI: 10.1002/for.1119

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