Bayesian Estimation of a Skew-t Stochastic Volatility Model

In this paper we present a stochastic volatility (SV) model assuming that the return shock has a skew-Student-t distribution. This allows a parsimonious, flexible treatment of asymmetry and heavy tails in the conditional distribution of returns. An efficient Markov chain Monte Carlo estimation method is described that exploits a skew-normal mixture representation of the error distribution with a gamma distribution as the mixing distribution. We apply the methodology to the NASDAQ daily index returns.