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Bayesian analysis of multivariate stochastic volatility with skew return distribution

Jouchi Nakajima

Econometric Reviews, 2017, vol. 36, issue 5, 546-562

Abstract: Multivariate stochastic volatility models with skew distributions are proposed. Exploiting Cholesky stochastic volatility modeling, univariate stochastic volatility processes with leverage effect and generalized hyperbolic skew t-distributions are embedded to multivariate analysis with time-varying correlations. Bayesian modeling allows this approach to provide parsimonious skew structure and to easily scale up for high-dimensional problem. Analyses of daily stock returns are illustrated. Empirical results show that the time-varying correlations and the sparse skew structure contribute to improved prediction performance and Value-at-Risk forecasts.

Date: 2017
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Handle: RePEc:taf:emetrv:v:36:y:2017:i:5:p:546-562