volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and more …

choose the thresholds to maximize the out-ofsample forecast performance of time series models based on realized partial (co …

We propose new asymmetric multivariate volatility models. The models exploit estimates of variances and covariances …

most noteworthy empirical findings to date pertaining to volatility forecasting and asset pricing …

volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically … forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility …

volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically … forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility …

and implied volatilities. First, we show that whereas the volatility feedback effect as measured by the sign of the … correlation between contemporaneous return and realized volatility depends importantly on the underlying structural model … parameters, the correlation between return and implied volatility is unambiguously positive for all reasonable parameter …

Stock market volatility clusters in time, appears fractionally integrated, carries a risk premium, and exhibits … asymmetric leverage effects relative to returns. At the same time, the volatility risk premium, defined by the difference between … the risk-neutral and objective expectations of the volatility, is distinctly less persistent and appears short …