A Flexible Regime Switching Model for Asset Returns
52 Pages Posted: 16 May 2019 Last revised: 25 May 2019
Date Written: May 16, 2019
A non-Gaussian multivariate regime switching dynamic correlation model for financial asset returns is proposed. It incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns. All model parameters are estimated consistently using a new two-stage expectation-maximization algorithm that also allows for incorporation of shrinkage estimation via quasi-Bayesian priors. It is shown that use of Markov switching correlation dynamics not only leads to highly accurate risk forecasts, but also potentially reduces the regulatory capital requirements during periods of distress. In terms of portfolio performance, the new regime switching model delivers consistently higher Sharpe ratios and smaller losses than the equally weighted portfolio and all competing models. Finally, the regime forecasts are employed in a dynamic risk control strategy that avoids most losses during the financial crisis and vastly improves risk-adjusted returns.
Keywords: GARCH; Markov Switching; Multivariate Generalized Hyperbolic Distribution; Portfolio Optimization; Value-at-Risk
JEL Classification: C32, C51, C53, G11, G17, G32
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