most important factor in reducing portfolio variance is the use of a flexible model for time varying volatility, rather … good on the present set of measures as the stochastic volatility models, with or without dynamic correlation. …

volatility of individual stock returns and exchange rate returns. …

tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization …

-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP …

forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that …, 60 and 300 seconds), forecast horizons (1, 5, 22 and 66 days) and the use of standard and robust-to-noise volatility and …-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility measures at the …

series of 1, 200 to 4, 400 daily price observations. Apart from persistence, heteroskedasticity and extreme observations in …

The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic … Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks - Brent in Europe … and WTI in America are compared. OVX index is able to provide the optimal forecast for the volatility of Brent's future …

Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit … volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based … on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility …