This paper presents the first formal comparison of Value at risk(VaR)forecasting perfor-mance across various high-frequency volatility models and conventional benchmarks using daily data in the crude oil futures marke...This paper presents the first formal comparison of Value at risk(VaR)forecasting perfor-mance across various high-frequency volatility models and conventional benchmarks using daily data in the crude oil futures market.Our analysis reveals the following key findings:(1)High-frequency data significantly enhance the accuracy of VaR forecasts.Specifically,the realized-GARCH(generalized autoregressive conditional hetero-skedasticity)model that incorporates 5-s realized bipower variation(BPV)outperforms all other models.(2)Not all realized measures are equally effective for VaR forecasting.The 5-s BPV model consistently outperforms other realized measures in forecasting VaR.(3)The choice of sampling frequency plays a crucial role in the performance of realized measures when forecasting VaR.(4)Many more sophisticated realized measures fail to surpass the simple 5-min realized variance(RV)model in forecasting VaR in the crude oil futures market.展开更多
文摘This paper presents the first formal comparison of Value at risk(VaR)forecasting perfor-mance across various high-frequency volatility models and conventional benchmarks using daily data in the crude oil futures market.Our analysis reveals the following key findings:(1)High-frequency data significantly enhance the accuracy of VaR forecasts.Specifically,the realized-GARCH(generalized autoregressive conditional hetero-skedasticity)model that incorporates 5-s realized bipower variation(BPV)outperforms all other models.(2)Not all realized measures are equally effective for VaR forecasting.The 5-s BPV model consistently outperforms other realized measures in forecasting VaR.(3)The choice of sampling frequency plays a crucial role in the performance of realized measures when forecasting VaR.(4)Many more sophisticated realized measures fail to surpass the simple 5-min realized variance(RV)model in forecasting VaR in the crude oil futures market.
