Mean-correction and Higher Order Moments for a Stochastic Volatility Model with Correlated Errors

TLDR: In this paper, a mean-correction for such an SVM for discrete-time returns with non-zero correlation is proposed, and the performance of the proposed and classical SVMs on S&P 500 index returns obtained from NYSE.

Abstract: In an efficient stock market, the log-returns and their time-dependent variances are often jointly modelled by  stochastic volatility models (SVMs). Many SVMs assume that errors in log-return and latent volatility process are uncorrelated, which is unrealistic. It turns out that if a non-zero correlation is included in the SVM (e.g., \cite{Shephard05}), then the expected log-return at time $t$ conditional on the past returns is non-zero, which is not a desirable feature of an efficient stock market. In this paper, we propose a mean-correction for such an SVM for discrete-time returns with non-zero correlation. We also find closed form analytical expressions for higher moments of log-return and its lead-lag correlations with the volatility process. We compare the performance of the proposed and classical SVMs on S\&P 500 index returns obtained from NYSE.