Estimating crude oil price 'Value at Risk' using the Bayesian-SV-SGT approach

Based on the analysis of the demographic characteristics of the crude oil price, to better characterized the kurtosis, thick tail, skewness, volatility clustering and durative characteristics of the return of crude oil spot market, and so introduced the SGT distribution to describe the distribution characteristics of crude oil price. At the same time, based on Bayesian theory and MCMC methods to solve the difficult SV model parameter estimation problems, and then used the Bayesian-SV-SGT model to estimate and analyze the crude oil spot price VaR (Value at Risk). The results show that the Bayesian-SV-SGT model can better describes the characteristics of crude oil spot market and can give a more precise "Value at Risk" estimation compare with the category of GARCH-GED model.