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An evaluation framework for alternative VaR-models

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

Abstract

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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Bibliographic Info

Article provided by Elsevier in its journal Journal of International Money and Finance.

Volume (Year): 24 (2005)
Issue (Month): 6 (October)
Pages: 944-958

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

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Web page: http://www.elsevier.com/locate/inca/30443

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Citations

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Cited by:
  1. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
  2. Wolfgang Aussenegg & Tatiana Miazhynskaia, 2006. "Uncertainty in Value-at-risk Estimates under Parametric and Non-parametric Modeling," Financial Markets and Portfolio Management, Springer, vol. 20(3), pages 243-264, September.
  3. Christian Wolff & Dennis Bams & Thorsten Lehnert, 2008. "Loss Functions in Option Valuation: A Framework for Selection," LSF Research Working Paper Series 08-11, Luxembourg School of Finance, University of Luxembourg.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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).
  9. Kulp-Tåg, Sofie, 2007. "An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions," Working Papers 526, Hanken School of Economics.
  10. Dany Rogers Silva & Karem Cristina de Sousa Ribeiro & Hsia Hua Sheng, 2011. "Trade credit profitability measurement: application in a wholesalerdistributor case," Brazilian Business Review, Fucape Business School, vol. 8(2), pages 22-41, April.
  11. 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.
  12. Basu, Sanjay, 2011. "Comparing simulation models for market risk stress testing," European Journal of Operational Research, Elsevier, vol. 213(1), pages 329-339, August.
  13. 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.
  14. María Rosa Nieto & Esther Ruiz, 2010. "Bootstrap prediction intervals for VaR and ES in the context of GARCH models," Statistics and Econometrics Working Papers ws102814, Universidad Carlos III, Departamento de Estadística y Econometría.

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