characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in … market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models … that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long …

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. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized …

This paper examines the ability of different GARCH models to forecast stock return volatility under a range of forecast …-based threshold limit. Our key results demonstrate that de-noising returns improves the accuracy of volatility forecasts regardless of … whether we use statistical metrics, tests of equal predictive accuracy or a VaR procedure. In terms of a particular volatility …

influential for Value at Risk (VaR) performance than the conditional volatility specification. We also show that some recently … proposed asymmetric probability distributions and the APARCH and FGARCH volatility specifications beat more standard … parameter in conditional volatility in the APARCH and FGARCH models explains their better performance. Indeed, our estimates …