Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit … volatility models are compared in terms of their VaR forecasting performances through a Monte carlo study and an analysis based … on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility …

normality. These include volatility clustering, heavy-tailedness and serial dependence. A voluminous literature on different … variety of highly flexible stochastic volatility models, and introduce some efficient algorithms based on recent advances in …

metric for comparing stochastic volatility models is the DIC based on the conditional likelihood—obtained by conditioning on … well. The main challenge for obtaining the latter DIC for stochastic volatility models is that the observed …-data likelihoods for a variety of stochastic volatility models using importance sampling. We demonstrate the methodology with an …

This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (2002) to allow for … time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears … inflation. The estimation results show substantial time-variation in the coefficient associated with the volatility, high …