univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and …

forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures …

The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the … definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from … the co-jumps of two assets have a significant impact on future co-volatility, but that the impact is negligible for …

time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out … empirical findings, based on the S&P 500 stock index, indicate that almost all realized and implied volatility measures can … produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being …

This paper evaluates and compares the ability of alternative option-implied volatility measures to forecast the monthly … realized volatility of crude-oil returns. We find that a corridor implied volatility measure that aggregates information from a …-free volatility expectations, as well as those generated by a high-frequency realized volatility model. In particular, this measure …

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility … realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that … machine learning methods can capture the stylized facts about volatility without relying on any assumption about the …

of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much …