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An evaluation framework for alternative VaR-models


  • Bams, Dennis
  • Lehnert, Thorsten
  • Wolff, Christian C.P.


In 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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  • 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.
  • Handle: RePEc:eee:jimfin:v:24:y:2005:i:6:p:944-958

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    References listed on IDEAS

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    6. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2000. "Diagnosing and treating the fat tails in financial returns data," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 389-416, November.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    Cited by:

    1. Kulp-Tåg, Sofie, 2007. "An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions," Working Papers 526, Hanken School of Economics.
    2. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    3. Silvia Stanescu & Radu Tunaru, 2013. "Quantifying the uncertainty in VaR and expected shortfall estimates," Chapters,in: Handbook of Research Methods and Applications in Empirical Finance, chapter 15, pages 357-372 Edward Elgar Publishing.
    4. George Kouretas & Leonidas Zarangas, 2005. "Conditional autoregressive valu at risk by regression quantile: Estimatingmarket risk for major stock markets," Working Papers 0521, University of Crete, Department of Economics.
    5. Thorsten Lehnert & Gildas Blanchard & Dennis Bams, 2014. "Evaluating Option Pricing Model Performance Using Model Uncertainty," LSF Research Working Paper Series 14-06, Luxembourg School of Finance, University of Luxembourg.
    6. Bams, Dennis & Lehnert, Thorsten & Wolff, Christian C, 2005. "Loss Functions in Option Valuation: A Framework for Model Selection," CEPR Discussion Papers 4960, C.E.P.R. Discussion Papers.
    7. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    8. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    9. Wolfgang Aussenegg & Tatiana Miazhynskaia, 2006. "Uncertainty in Value-at-risk Estimates under Parametric and Non-parametric Modeling," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 243-264, September.
    10. Samia Omrane, 2012. "An Analysis of Exchange Rate Risk Exposure Related to the Public Debt Portfolio of Tunisia: Beyond VaR Approach," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 59(1), pages 59-87, March.
    11. Amira Akl Ahmed & Doaa Akl Ahmed, 2016. "Modelling Conditional Volatility and Downside Risk for Istanbul Stock Exchange," Working Papers 1028, Economic Research Forum, revised Jul 2016.
    12. repec:bbz:fcpbbr:v:8:y:2011:i:2:p:22-41 is not listed on IDEAS
    13. Lehnert, Thorsten & Wolff, Christian C. P., 2004. "Scale-consistent Value-at-Risk," Finance Research Letters, Elsevier, vol. 1(2), pages 127-134, June.
    14. Basu, Sanjay, 2011. "Comparing simulation models for market risk stress testing," European Journal of Operational Research, Elsevier, vol. 213(1), pages 329-339, August.
    15. Spierdijk, Laura, 2016. "Confidence intervals for ARMA–GARCH Value-at-Risk: The case of heavy tails and skewness," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 545-559.
    16. Angelidis, Timotheos & Degiannakis, Stavros, 2007. "Backtesting VaR Models: A Τwo-Stage Procedure," MPRA Paper 80418, University Library of Munich, Germany.
    17. Dennis Bams & Thorsten Lehnert & Christian C. P. Wolff, 2009. "Loss Functions in Option Valuation: A Framework for Selection," Management Science, INFORMS, vol. 55(5), pages 853-862, May.
    18. Nieto, María Rosa & Ruiz, Esther, 2010. "Bootstrap prediction intervals for VaR and ES in the context of GARCH models," DES - Working Papers. Statistics and Econometrics. WS ws102814, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Kostas Andriosopoulos & Nikos Nomikos, 2012. "Risk management in the energy markets and Value-at-Risk modelling: a Hybrid approach," RSCAS Working Papers 2012/47, European University Institute.
    20. repec:eee:intfor:v:33:y:2017:i:4:p:848-863 is not listed on IDEAS
    21. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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