Stochastic volatility models with heavy-tailed distributions: a Bayesian analysis. Unpublished manuscript

This paper examines two asymmetric stochastic volatility mod-els used to describe the heavy tails and volatility dependencies found in most financial returns. The first is the autoregressive stochastic volatility model with Student’s t-distribution (ARSV-t), and the sec-ond is the multifactor stochastic volatility (MFSV) model. In order to estimate these models, the analysis employs the Monte Carlo like-lihood (MCL) method proposed by Sandmann and Koopman (1998). To guarantee the positive definiteness of the sampling distribution of the MCL, the nearest covariance matrix in the Frobenius norm is used. The empirical results using returns on the S&P 500 Composite and Tokyo stock price indexes and the Japan–US exchange rate indicate that the ARSV-t model provides a better fit than the MFSV model on the basis of Akaike information criterion (AIC) and the Bayes in-formation criterion (BIC)