Nonparametric estimation of value-at-risk

This paper develops a fully nonparametric method for estimating value-at-risk based on the adaptive volatility estimation and the nonparametric quantile estimation. The proposed method is simple, fast and easy to implement. We evaluated its numerical performance on the basis of Monte Carlo study for numerous models. We also provided an empirical application to KOrean Stock Price Index data, which turned out to be successful by backtesting.