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An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions

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Author Info
Kulp-Tåg, Sofie () (Swedish School of Economics and Business Administration)
Abstract

This paper uses the Value-at-Risk approach to define the risk in both long and short trading positions. The investigation is done on some major market indices(Japanese, UK, German and US). The performance of models that takes into account skewness and fat-tails are compared to symmetric models in relation to both the specific model for estimating the variance, and the distribution of the variance estimate used as input in the VaR estimation. The results indicate that more flexible models not necessarily perform better in predicting the VaR forecast; the reason for this is most probably the complexity of these models. A general result is that different methods for estimating the variance are needed for different confidence levels of the VaR, and for the different indices. Also, different models are to be used for the left respectively the right tail of the distribution.

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Publisher Info
Paper provided by Swedish School of Economics and Business Administration in its series Working Papers with number 526.

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Length: 37 pages
Date of creation: 13 Apr 2007
Date of revision:
Handle: RePEc:hhb:hanken:0526

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Postal: Swedish School of Economics and Business Administration, Arkadiankatu 22, P.O.B. 479; FIN 00101 Helsinki, Finland
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Related research
Keywords: Value-at-Risk asymmetry Exponential GARCH Asymmetric Power ARCH long-trading short-trading

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  1. Guermat, Cherif & Harris, Richard D. F., 2002. "Forecasting value at risk allowing for time variation in the variance and kurtosis of portfolio returns," International Journal of Forecasting, Elsevier, vol. 18(3), pages 409-419. [Downloadable!] (restricted)
  2. So, Mike K.P. & Yu, Philip L.H., 2006. "Empirical analysis of GARCH models in value at risk estimation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(2), pages 180-197, April. [Downloadable!] (restricted)
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  5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March. [Downloadable!] (restricted)
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  7. Sean D. Campbell, 2005. "A review of backtesting and backtesting procedures," Finance and Economics Discussion Series 2005-21, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  8. 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. [Downloadable!] (restricted)
  9. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June. [Downloadable!] (restricted)
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  10. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89. [Downloadable!] (restricted)
  11. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  12. Giot, P. & Laurent, S., 2001. "Value-at-risk for Long and Short Trading Positions," Papers 0122, Catholique de Louvain - Center for Operations Research and Economics.
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  13. Balaban, Ercan, 2004. "Comparative forecasting performance of symmetric and asymmetric conditional volatility models of an exchange rate," Economics Letters, Elsevier, vol. 83(1), pages 99-105, April. [Downloadable!] (restricted)
  14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. [Downloadable!] (restricted)
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