In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean …(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable … in the mean equation. The same extension isdeveloped elsewhere for Autoregressive ConditionalHeteroskedastic (ARCH …

markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance …-at-Risk forecasts, which appears to stem from several econometric properties of the return distributions. With stochastic volatility … models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across …

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been … employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which … can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics …

The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to ten years of … daily data for FTSE. As a benchmark, we use the realized volatility (RV) of FTSE sampled at 5-minute intervals, taken from … we need either of the two standard volatility models, if the simple expedient of using lagged squared demeaned daily …

The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus … years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and … by adding a regression matrix including the first and second moments of the demeaned return series. Similarly, the GARCH …