Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model

We develop a flexible modeling and estimation framework for a high-dimensional factor stochastic volatility (SV) model. Our specification allows for leverage effects, asymmetry and heavy tails across all systematic and idiosyncratic components of the model. This framework accounts for well-documented features of univariate financial time series, while introducing a flexible dependence structure that incorporates tail dependence and asymmetries such as stronger correlations following downturns. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior simulation based on the particle Gibbs, ancestor sampling, and particle efficient importance sampling methods. We build computationally efficient model selection into our estimation framework to obtain parsimonious specifications in practice. We validate the performance of our proposed estimation method via extensive simulation studies for univariate and multivariate simulated datasets. An empirical study shows that the model outperforms other multivariate models in terms of value-at-risk evaluation and portfolio selection performance for a sample of US and Australian stocks.