An evaluation framework for alternative VaR-models
AbstractIn this Paper we investigate the ability of different models to produce useful VaR-estimates for exchange rate positions. We make a distinction between models that include sophisticated tail properties and models that do not. The former type of models often leads to too extreme VaR-estimates, whereas the latter type underestimates the risk in case of extreme events. Our analysis shows that it is important to take into account parameter uncertainty, since this leads to uncertainty in the reported VaR. We make this uncertainty in the VaR explicit by means of simulation. Our empirical results suggest that more sophisticated tail-modeling approaches come at the cost of more uncertainty about the VaR estimate itself. In the case of the GARCH(1,1)-Student-t model the average VaR may be adjusted for parameter uncertainty to arrive at levels which are adequate according to out-of-sample tests.
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Bibliographic InfoPaper provided by Maastricht University in its series Open Access publications from Maastricht University with number urn:nbn:nl:ui:27-13930.
Date of creation: 2005
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Publication status: Published in Journal of international money and finance (2005) v.24, p.944-958
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Other versions of this item:
- Bams, Dennis & Lehnert, Thorsten & Wolff, Christian C.P., 2005. "An evaluation framework for alternative VaR-models," Journal of International Money and Finance, Elsevier, vol. 24(6), pages 944-958, October.
- Bams, Dennis & Lehnert, Thorsten & Wolff, Christian C, 2002. "An Evaluation Framework for Alternative VaR Models," CEPR Discussion Papers 3403, C.E.P.R. Discussion Papers.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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