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Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory

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  • Julia Schaumburg

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.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2010-009.pdf
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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2010-009.

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Length: 28 pages
Date of creation: Feb 2010
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2010-009

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Related research

Keywords: Value at Risk; nonparametric quantile regression; risk management; extreme value theory; monotonization; CAViaR;

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Cited by:
  1. Ioan Trenca & Simona Mutu & Nicolae Petria, 2011. "Econometric Models Used For Managing The Market Risk In The Romanian Banking System," Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - Stiinte Economice, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 2011, pages 115-123, july.
  2. Mauro Bernardi & Ghislaine Gayraud & Lea Petrella, 2013. "Bayesian inference for CoVaR," Papers 1306.2834, arXiv.org, revised Nov 2013.
  3. Shih-Kang Chao & Wolfgang Karl Härdle & Weining Wang, 2012. "Quantile Regression in Risk Calibration," SFB 649 Discussion Papers SFB649DP2012-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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