forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve …

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 …

-day and intra-day volatility models by estimating the AR(1)-GARCH(1,1)-skT and the AR(1)-HAR-RV-skT frameworks, respectively … intra-day volatility model is not as appropriate as it was expected to be for each of the different asset classes; stock … performance of the inter-day and intra-day volatility models across various markets. The inter-day specification predicts and …

Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more … accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, I estimate an … leptokurtic conditional distribution of innovations, produces the most accurate one-day-ahead volatility forecasts. The study …

paper investigates the impact of EPU on the crude oil return volatility and which EPU index has the most forecasting power … the crude oil return volatility, but the effect is short-lived and the decay period is about one year. Particularly, our … results show that the US EPU index has the best forecasting power for crude oil return volatility over the long-term, whereas …

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

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