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 …

relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the …

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international … augmented Empirical Similarity model that combines three volatility components, defined over different time horizons, using the … similarity measure between lagged Google search queries and volatility. Results show that investor attention positively affects …

This paper proposes the sample path generation method for the stochastic volatility version of the CGMY process. We …

The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus … years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and … volatility models, if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at …

volatility, which has not been studied earlier. The study examines squared stock index returns of equity in 35 markets, including … of volatility. Results from the conditional heteroskedasticity long memory model show the evidence of long memory in the …