Bayesian analysis of multivariate stochastic volatility with skew distribution

Jouchi Nakajima
Duke University

Jan 8 2013

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 prior works allow 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 VaR forecasts.


Generalized hyperbolic skew t-distribution, Multivariate stochastic volatility, Portfolio allocation, Skew selection, Stock returns, Value at Risk


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