Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/39337 
Autor:innen: 
Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2010,009
Verlag: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
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.
Schlagwörter: 
Value at Risk
nonparametric quantile regression
risk management
extreme value theory
monotonization
CAViaR
JEL: 
C14
C22
C52
C53
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
292.13 kB





Publikationen in EconStor sind urheberrechtlich geschützt.