Bayesian analysis of multivariate stochastic volatility with skew distribution
Methodology
2012-12-21 v1 Applications
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 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.
Cite
@article{arxiv.1212.5090,
title = {Bayesian analysis of multivariate stochastic volatility with skew distribution},
author = {Jouchi Nakajima},
journal= {arXiv preprint arXiv:1212.5090},
year = {2012}
}