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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