Predicting extreme VaR nonparametric quantile regression with refinements from extreme value theory
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%) condit...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Berlin
SFB 649, Economic Risk
2010
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| Schriftenreihe: | SFB 649 discussion paper
2010,009 |
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Tag hinzufügen | Zusammenfassung: | 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. -- Value at Risk ; nonparametric quantile regression ; risk management ; extreme value theory ; monotonization ; CAViaR |
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| Beschreibung: | 25 S. graph. Darst. |