Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/39337
Authors: 
Schaumburg, Julia
Year of Publication: 
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

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