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Sonderforschungsbereich 649: Ökonomisches Risiko, Humboldt-Universität Berlin >
SFB 649 Discussion Papers, HU Berlin >
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http://hdl.handle.net/10419/39337
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| Title: | | Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory  |
| 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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