The international crude oil market is complicated in itself and with the rapid development of China in recent years, the dramatic changes of the international crude oil market have brought some risk to the security of Chinas oil market and the economic development of China. Value at risk (VaR), an effective measurement of financial risk, can be used to assess the risk of refined oil retail sales as well. However, VaR, as a model that can be applied to complicated nonlinear data, has not yet been widely researched. Therefore, an improved Historical Simulation Approach, historical stimulation of genetic algorithm to parameters selection of support vector machine, HSGA-SVMF, in this paper, is proposed, which is based on an approach the historical simulation with ARMA forecasts, HSAF. By comparing it with the HSAF and HSGA-SVMF approach, this paper gives evidence to show that HSGA-SVMF has a more effective forecasting power in the field of amount of refined oil.
Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data