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Title:Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory PDF Logo
Authors:Schaumburg, Julia
Issue Date:2010
Series/Report no.:SFB 649 discussion paper 2010,009
Abstract:This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models.
Subjects:Value at Risk
nonparametric quantile regression
risk management
extreme value theory
monotonization
CAViaR
JEL:C14
C22
C52
C53
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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