usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of …

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

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the … management. Evaluation of volatility models is then considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for …

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 …

world application that considering crude oil forecasts disagreement when forecasting volatility has important implications … conducted by the European Central Bank contain information for the Brent crude oil return volatility predictions. With a variety … forecasters disagreement into the GARCH-MIDAS significantly improves oil return volatility prediction. We demonstrate in the real …