Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory
Julia Schaumburg ()
No SFB649DP2010-009, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649
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.
Keywords: Value at Risk; nonparametric quantile regression; risk management; extreme value theory; monotonization; CAViaR (search for similar items in EconPapers)
JEL-codes: C14 C22 C52 C53 (search for similar items in EconPapers)
Pages: 28 pages
New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:hum:wpaper:sfb649dp2010-009
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