Bayesian analysis of multivariate stochastic volatility with skew return distribution
提出一种多变量随机波动率模型，结合偏态分布和时变相关性，通过贝叶斯方法高效处理高维问题，实证表明能提升股票收益预测和风险价值预测。
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.