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 …

We develop a LSTM neural network for the joint prediction of volatility, realized volatility and Value …

forest. In contrast to conventional random forests that approximate the volatility nonparametrically using local averaging … significantly higher utility for volatility managed portfolios. Superior forecast performance is especially pronounced for firms …

This study utilized both single-regime GARCH and double-regime GARCH models to investigate oil price volatility … volatility on these factors was examined. The empirical results confirmed the presence of the leverage effect and identified … multiple volatility switches associated with remarkable events like the GFC, the European debt crisis, the COVID-19 pandemic …

This paper analyses the attributes and the significance of the roughness of oil market volatility. We employ unspanned … stochastic volatility models driven by rough Brownian motions that yield semi-analytical prices for futures options entailing … futures and provide empirical evidence of the roughness in oil volatility. Introducing just one additional parameter, the …

through a new adaptation of the Monte Carlo simulation approach for forecasting multi-period volatility to a fractionally …

Expected Shortfall, for a given volatility model. We obtain precise forecasts of the tail of the distribution of returns not …