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Predicting extreme value at risk: Nonparametric quantile regression with refinements from extreme value theory

Julia Schaumburg ()

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4081-4096

Abstract: A framework is introduced allowing us to apply nonparametric quantile regression to Value at Risk (VaR) prediction at any probability level of interest. A monotonized double kernel local linear estimator is used to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, nonparametric quantile regression is combined with extreme value theory. The abilities of the proposed estimators to capture market risk are investigated in a VaR prediction study with empirical and simulated data. Possibly due to its flexibility, the out-of-sample forecasting performance of the new model turns out to be superior to competing models.

Keywords: Value at risk; Nonparametric quantile regression; Risk management; Extreme value statistical applications; Monotonization (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1016/j.csda.2012.03.016

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