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A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns

A multivariate regime-switching GARCH model with an application to global stock market and real... AbstractWe consider a multivariate Markov-switching GARCH model which allows for regime-specific volatility dynamics, leverage effects, and correlation structures. Conditions for stationarity and expressions for the moments of the process are derived. A Lagrange Multiplier test against misspecification of the within-regime correlation dynamics is proposed, and a simple recursion for multi-step-ahead conditional covariance matrices is deduced. We use this methodology to model the dynamics of the joint distribution of global stock market and real estate equity returns. The empirical analysis highlights the importance of the conditional distribution in Markov-switching time series models. Specifications with Student’s t innovations dominate their Gaussian counterparts both in- and out-of-sample. The dominating specification appears to be a two-regime Student’s t process with correlations which are higher in the turbulent (high-volatility) regime. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns

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References (76)

Publisher
de Gruyter
Copyright
©2018 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1558-3708
eISSN
1558-3708
DOI
10.1515/snde-2016-0019
Publisher site
See Article on Publisher Site

Abstract

AbstractWe consider a multivariate Markov-switching GARCH model which allows for regime-specific volatility dynamics, leverage effects, and correlation structures. Conditions for stationarity and expressions for the moments of the process are derived. A Lagrange Multiplier test against misspecification of the within-regime correlation dynamics is proposed, and a simple recursion for multi-step-ahead conditional covariance matrices is deduced. We use this methodology to model the dynamics of the joint distribution of global stock market and real estate equity returns. The empirical analysis highlights the importance of the conditional distribution in Markov-switching time series models. Specifications with Student’s t innovations dominate their Gaussian counterparts both in- and out-of-sample. The dominating specification appears to be a two-regime Student’s t process with correlations which are higher in the turbulent (high-volatility) regime.

Journal

Studies in Nonlinear Dynamics & Econometricsde Gruyter

Published: Jun 26, 2018

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