Predicting tail-related risk measures: The consequences of using GARCH filters for non-GARCH data
AbstractWe investigate the consequences for Value-at-Risk and expected shortfall purposes of using a GARCH filter on various mis-specified processes. In general, we find that the McNeil and Frey (McNeil, A.J. and R. Frey, 2000, Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach, Journal of Empirical Finance 7, 271-300.) two step procedure has very good forecasting properties. Using an unconditional non-filtered tail estimate also appears to perform satisfactorily for expected shortfall measurements but less so for VaR computations. Methods assuming specific densities such as the Gaussian or Student-t may yield wrong predictions. Thus, the use of an adequacy test for filtered data to given densities appears relevant. The paper builds on recent techniques to obtain thresholds for extreme value computations. Statistical tests for the expected shortfall, based on the circular bootstrap, are developed.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Empirical Finance.
Volume (Year): 15 (2008)
Issue (Month): 5 (December)
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Web page: http://www.elsevier.com/locate/jempfin
Extreme value theory Value-at-Risk (VaR) Expected shortfall GARCH Markov switching Jump diffusion Backtesting;
Other versions of this item:
- Amine JALAL & Michael ROCKINGER, 2004. "Predicting Tail-related Risk Measures: The Consequences of Using GARCH Filters for non-GARCH Data," FAME Research Paper Series rp115, International Center for Financial Asset Management and Engineering.
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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