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Modelling Scale-Consistent VaR with the Truncated Lévy Flight

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Author Info
Lehnert, Thorsten
Wolff, Christian C

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Abstract

Returns in financial assets show consistent excess kurtosis, indicating the presence of large fluctuations not predicted by Gaussian models. Mandelbrot (1963) first proposed the idea that price changes distributed according to a Lévy stable law. The unique feature of Lévy-stable distributions in general is that they are stable under addition. However, these distributions have power law tails that decay too slowly from the point of view of financial modelling. In recent studies the truncated Lévy Flight has been shown to eliminate this problem and to be very promising for the modelling of financial dynamics. An exponential decay in the tails ensures that all moments are finite and the distribution is fat-tailed for short time scales and converges in a Gaussian process for increasing time scales, a feature observed in financial data. We propose a model with time varying scale parameter (GARCH process) with error terms that are truncated Lévy distributed. We determine the appropriate GARCH specification for each data set by conducting a specification test based on a generalization of the augmented GARCH process of Duan (1997). The Lévy flight includes a method for scaling up a single-day volatility to a multi-day volatility, precisely a ?-root-of-time rule, where ? is the characteristic parameter of the process. We use this rule to forecast future volatility and as a result estimate Value-at-Risk (VaR) several days ahead and compare it to the RiskMetricsTM (1996) approach, which is a special case of our method. We compare the models in in-sample- and out-of-sample analyses for a sample of stock index returns.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 2711.

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Date of creation: Feb 2001
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Handle: RePEc:cpr:ceprdp:2711

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Related research
Keywords: Augmented GARCH Process; In- And Out-Of-Sample Analysis; Scale Consistency; Truncated Lévy Flight; Value-At-Risk;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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  1. Duan, Jin-Chuan, 1997. "Augmented GARCH (p,q) process and its diffusion limit," Journal of Econometrics, Elsevier, vol. 79(1), pages 97-127, July. [Downloadable!] (restricted)
  2. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-. [Downloadable!]
    Other versions:
  3. Andrew Matacz, 1997. "Financial modeling and option theory with the truncated Lévy process," Science & Finance (CFM) working paper archive 500035, Science & Finance, Capital Fund Management. [Downloadable!]
  4. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-27, July. [Downloadable!] (restricted)
    Other versions:
  5. Pownall, Rachel A. J. & Koedijk, Kees G., 1999. "Capturing downside risk in financial markets: the case of the Asian Crisis," Journal of International Money and Finance, Elsevier, vol. 18(6), pages 853-870, December. [Downloadable!] (restricted)
  6. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-78, December. [Downloadable!] (restricted)
    Other versions:
  7. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June. [Downloadable!] (restricted)
  8. Phillipe Lambert & J. K. Lindsey, 1999. "Analysing Financial Returns by Using Regression Models Based on Non-Symmetric Stable Distributions," Journal Of The Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 409-424. [Downloadable!] (restricted)
  9. Hentschel, Ludger, 1995. "All in the family Nesting symmetric and asymmetric GARCH models," Journal of Financial Economics, Elsevier, vol. 39(1), pages 71-104, September. [Downloadable!] (restricted)
  10. Rama Cont & Marc Potters & Jean-Philippe Bouchaud, 1997. "Scaling in stock market data: stable laws and beyond," Science & Finance (CFM) working paper archive 9705087, Science & Finance, Capital Fund Management. [Downloadable!]
  11. Liu, Shi-Miin & Brorsen, B Wade, 1995. "Maximum Likelihood Estimation of a Garch-Stable Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(3), pages 273-85, July-Sept. [Downloadable!] (restricted)
  12. Ghose, Devajyoti & Kroner, Kenneth F., 1995. "The relationship between GARCH and symmetric stable processes: Finding the source of fat tails in financial data," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 225-251, September. [Downloadable!] (restricted)
  13. 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)
    Other versions:
  14. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December. [Downloadable!] (restricted)
  15. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
  16. Rama Cont & Marc Potters & Jean-Philippe Bouchaud, 1997. "Scaling in stock market data: stable laws and beyond," Quantitative Finance Papers cond-mat/9705087, arXiv.org. [Downloadable!]
  17. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-53, December. [Downloadable!] (restricted)
    Other versions:
  18. Jón Daníelsson & Casper G. de Vries, 1998. "Value-at-Risk and Extreme Returns," Tinbergen Institute Discussion Papers 98-017/2, Tinbergen Institute. [Downloadable!]
    Other versions:
  19. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  20. Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania. [Downloadable!]
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