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 …

predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression … superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual …

. We find that our implied volatility (IV) sentiment measure, jointly derived from index and single stock options, explains …

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …

improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …

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

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … momentum strategy. The estimation of this modeling and strategy approach can be done using an extended and modified version of … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …