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A New Approach for Using Lévy Processes for Determining High‐Frequency Value‐at‐Risk Predictions

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  • Wei Sun
  • Svetlozar Rachev
  • Frank J. Fabozzi

Abstract

A new approach for using Lévy processes to compute value‐at‐risk (VaR) using high‐frequency data is presented in this paper. The approach is a parametric model using an ARMA(1,1)‐GARCH(1,1) model where the tail events are modelled using fractional Lévy stable noise and Lévy stable distribution. Using high‐frequency data for the German DAX Index, the VaR estimates from this approach are compared to those of a standard nonparametric estimation method that captures the empirical distribution function, and with models where tail events are modelled using Gaussian distribution and fractional Gaussian noise. The results suggest that the proposed parametric approach yields superior predictive performance.

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  • Wei Sun & Svetlozar Rachev & Frank J. Fabozzi, 2009. "A New Approach for Using Lévy Processes for Determining High‐Frequency Value‐at‐Risk Predictions," European Financial Management, European Financial Management Association, vol. 15(2), pages 340-361, March.
  • Handle: RePEc:bla:eufman:v:15:y:2009:i:2:p:340-361
    DOI: 10.1111/j.1468-036X.2008.00467.x
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    1. Jeremy Berkowitz & James O'Brien, 2002. "How Accurate Are Value‐at‐Risk Models at Commercial Banks?," Journal of Finance, American Finance Association, vol. 57(3), pages 1093-1111, June.
    2. Brooks, C. & Clare, A.D. & Dalle Molle, J.W. & Persand, G., 2005. "A comparison of extreme value theory approaches for determining value at risk," Journal of Empirical Finance, Elsevier, vol. 12(2), pages 339-352, March.
    3. Wasserfallen, Walter & Zimmermann, Heinz, 1985. "The behavior of intra-daily exchange rates," Journal of Banking & Finance, Elsevier, vol. 9(1), pages 55-72, March.
    4. 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.
    5. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    6. Winfried G. Hallerbach & Albert J. Menkveld, 2004. "Analysing Perceived Downside Risk: the Component Value‐at‐Risk Framework," European Financial Management, European Financial Management Association, vol. 10(4), pages 567-591, December.
    7. Benoit Mandelbrot, 1963. "New Methods in Statistical Economics," Journal of Political Economy, University of Chicago Press, vol. 71(5), pages 421-421.
    8. 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 841-862, November.
    9. Arjan Berkelaar & Phornchanok Cumperayot & Roy Kouwenberg, 2002. "The Effect of VaR Based Risk Management on Asset Prices and the Volatility Smile," European Financial Management, European Financial Management Association, vol. 8(2), pages 139-164, June.
    10. Stefano Gatti & Alvaro Rigamonti & Francesco Saita & Mauro Senati, 2007. "Measuring Value‐at‐Risk in Project Finance Transactions," European Financial Management, European Financial Management Association, vol. 13(1), pages 135-158, January.
    11. Beltratti, Andrea & Morana, Claudio, 1999. "Computing value at risk with high frequency data," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 431-455, December.
    12. Svetlozar T. Rachev & Chufang Wu & Frank J. Fabozzi, 2007. "Empirical Analyses of Industry Stock Index Return Distributions for the Taiwan Stock Exchange," Annals of Economics and Finance, Society for AEF, vol. 8(1), pages 21-31, May.
    13. repec:nys:sunysb:93-02 is not listed on IDEAS
    14. T. Rachev, Svetlozar & Samorodnitsky, Gennady, 2001. "Long strange segments in a long-range-dependent moving average," Stochastic Processes and their Applications, Elsevier, vol. 93(1), pages 119-148, May.
    15. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    16. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    17. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    18. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    19. Samorodnitsky, Gennady, 1994. "Possible sample paths of self-similar [alpha]-stable processes," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 233-237, February.
    20. Eugene F. Fama, 1963. "Mandelbrot and the Stable Paretian Hypothesis," The Journal of Business, University of Chicago Press, vol. 36, pages 420-420.
    21. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    2. Batten, Jonathan A. & Kinateder, Harald & Wagner, Niklas, 2014. "Multifractality and value-at-risk forecasting of exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 71-81.
    3. Edward W. Sun & Yu-Jen Wang & Min-Teh Yu, 2018. "Integrated Portfolio Risk Measure: Estimation and Asymptotics of Multivariate Geometric Quantiles," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 627-652, August.
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    5. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.
    6. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    7. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2019. "Coherent quality management for big data systems: a dynamic approach for stochastic time consistency," Annals of Operations Research, Springer, vol. 277(1), pages 3-32, June.
    8. Andrés García Mirantes & Javier Población & Gregorio Serna, 2012. "The Stochastic Seasonal Behaviour of Natural Gas Prices," European Financial Management, European Financial Management Association, vol. 18(3), pages 410-443, June.
    9. Bertrand Groslambert & Devraj Basu & Wan Ni Lai, 2019. "Is tail risk the missing link between institutions and risk?," Economics Bulletin, AccessEcon, vol. 39(2), pages 1435-1448.

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