forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve …

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the … management. Evaluation of volatility models is then considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for …

heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 … indicate that none of our volatility models can uniformly outperform other models across all six different loss functions …

This paper investigates the role of volatility risk on stock return predictability specified on two global financial … volatility forecasting measures on future stock returns in four different periods (bear and bull markets). First we find clear … and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub …

price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the … jump component is persistent when forecasting the oil futures market volatility. Specifically, we propose a strategy that … according to their recent past forecasting performance. The volatility data are based on the intraday prices of West Texas …

degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which … forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation … distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility …