Value at risk estimation based on generalized quantile regression

The paper proposes a novel value-at-risk measurement method based on kernel quantile regression. The method can build linear quantile regression in a reproduced Hilbert kernel space. It makes no assumption on the dependence between quantile functions and the predictors and achieves nonlinear capabilities. In the experiment on daily returns of crude oil, we compare its capability with other four conventional methods: simple moving average, exponential weighted moving average, GARCH and linear quantile regression. The out-of-sample results clearly show that the new method has superiority over other four methods.