Bayesian analysis of multivariate stochastic volatility with skew distribution

TL;DR

This paper introduces a Bayesian multivariate stochastic volatility model with skew distributions, leveraging Cholesky decomposition to handle high-dimensional data, and demonstrates improved prediction and risk forecasting on stock returns.

Contribution

It develops a scalable Bayesian framework for multivariate stochastic volatility with skew distributions, incorporating time-varying correlations and sparse skew structures.

Findings

Enhanced prediction accuracy for stock returns.

Improved Value-at-Risk (VaR) forecasts.

Effective modeling of high-dimensional financial data.

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.