VaR estimation based on quantile regression forest and risk factors analysis

Quantile regression forests as a nonparametric and ensemble method were built to estimate the VaR of Shanghai Composite Index and the S&P 500 Index at different confidence levels. Meanwhile,other methods were built for comparison, including historic simulation,GARCH, elastic net,threshold quantile regression model and CAViaR, and the superiority of the proposed method was verified. Further, a new measurement method of variable importance based on the quantile regression forest was defined to judge the importance of various factors on the risk value, and it was discovered that the past one day yield has the greatest influence on the risk value of the Shanghai Composite Index, and that the volatility has the greatest influence on S&P 500 Index risk value. At the same time, the risk conduction between China and US is weak. Further, by dynamically analyzing the partial dependence between the factors and risk value, the “black box” problem of machine learning used in financial applications has been remedied to some extent.