Nonparametric estimation of value-at-risk

Seok-Oh Jeong and Kee-Hoon Kang

Journal of Applied Statistics, 2009, vol. 36, issue 11, 1225-1238

Abstract: 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.

Keywords: value-at-risk; volatility; local homogeneity; quantile estimation; risk management; KOSPI (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1080/02664760802607517

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