Uncertainty in Value-at-Risk Estimates Under Parametric and Non-Parametric Modeling

31 Pages Posted: 26 Feb 2005

See all articles by Wolfgang Aussenegg

Wolfgang Aussenegg

Vienna University of Technology

Tatiana Miazhynskaia

Vienna University of Technology - Department of Finance and Corporate Control

Multiple version iconThere are 2 versions of this paper

Date Written: April 2006

Abstract

This study evaluates a set of parametric and non-parametric Value-at-Risk (VaR) models that quantify the uncertainty in VaR estimates in form of a VaR distribution. We propose a new VaR approach based on Bayesian statistics in a GARCH volatility modeling environment. This Bayesian approach is compared with other parametric VaR methods (quasi-maximum likelihood and bootstrap resampling on the basis of GARCH models) as well as with non-parametric historical simulation approaches (classical and volatility adjusted). All these methods are evaluated based on the frequency of failures and the uncertainty in VaR estimates.

Within the parametric methods, the Bayesian approach is better able to produce adequate VaR estimates, and results mostly in a smaller VaR variability. The non-parametric methods imply more uncertain 99%-VaR estimates, but show good performance with respect to 95%-VaRs.

Keywords: Value-at-Risk, Bayesian analysis, GARCH, Historical Simulation, Bootstrap resampling

JEL Classification: C11, C50, G10

Suggested Citation

Aussenegg, Wolfgang and Miazhynskaia, Tatiana, Uncertainty in Value-at-Risk Estimates Under Parametric and Non-Parametric Modeling (April 2006). Available at SSRN: https://ssrn.com/abstract=673662 or http://dx.doi.org/10.2139/ssrn.673662

Wolfgang Aussenegg (Contact Author)

Vienna University of Technology ( email )

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HOME PAGE: http://www.imw.tuwien.ac.at/fc/people/wolfgang_aussenegg/

Tatiana Miazhynskaia

Vienna University of Technology - Department of Finance and Corporate Control ( email )

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