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Estimation of tail thickness parameters from GJR-GARCH models

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Author Info
Emma M. Iglesias ()
Oliver Linton ()

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Abstract

We propose a method of estimating the Pareto tail thickness parameter of the unconditional distribution of a financial time series by exploiting the implications of a GJR-GARCH volatility model. The method is based on some recent work on the extremes of GARCH-type processes and extends the method proposed by Berkes, Horváth and Kokoszka (2003). We show that the estimator of tail thickness is consistent and converges at rate ?T to a normal distribution (where T is the sample size), provided the model for conditional variance is correctly specified as a GJR-GARCH. This is much faster than the convergence rate of the Hill estimator, since that procedure only uses a vanishing fraction of the sample. We also develop new specification tests based on this method and propose new alternative estimates of unconditional value at risk. We show in Monte Carlo simulations the advantages of our procedure in finite samples; and finally an application concludes the paper

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Paper provided by Universidad Carlos III, Departamento de Economía in its series Economics Working Papers with number we094726.

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Date of creation: Jun 2009
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Handle: RePEc:cte:werepe:we094726

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Related research
Keywords: Pareto tail thickness parameter; GARCH-type models; Value-at-Risk; Extreme value theory; Heavy tails;

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Capital and Ownership Structure

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  1. Markowitz, Harry M, 1991. " Foundations of Portfolio Theory," Journal of Finance, American Finance Association, vol. 46(2), pages 469-77, June. [Downloadable!] (restricted)
  2. Linton, Oliver, 1993. "Adaptive Estimation in ARCH Models," Econometric Theory, Cambridge University Press, vol. 9(04), pages 539-569, August. [Downloadable!]
    Other versions:
  3. Phillip Kearns & Adrian Pagan, 1997. "Estimating The Density Tail Index For Financial Time Series," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 171-175, May. [Downloadable!] (restricted)
  4. Wagner, Niklas & Marsh, Terry A., 2005. "Measuring tail thickness under GARCH and an application to extreme exchange rate changes," Journal of Empirical Finance, Elsevier, vol. 12(1), pages 165-185, January. [Downloadable!] (restricted)
  5. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May. [Downloadable!] (restricted)
    Other versions:
  6. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-16, April.
  7. Groenendijk, Patrick A. & Lucas, Andre & de Vries, Casper G., 1995. "A note on the relationship between GARCH and symmetric stable processes," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 253-264, September. [Downloadable!] (restricted)
  8. Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," Journal of Business, University of Chicago Press, vol. 36, pages 394. [Downloadable!]
  9. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(02), pages 280-310, April. [Downloadable!]
    Other versions:
  10. Berkes, Istv n & Horv th, Lajos & Kokoszka, Piotr, 2003. "Estimation Of The Maximal Moment Exponent Of A Garch(1,1) Sequence," Econometric Theory, Cambridge University Press, vol. 19(04), pages 565-586, August. [Downloadable!]
  11. Oliver Linton & Enno Mammen, 2003. "Estimating Semiparametric ARCH (8) Models by Kernel Smoothing Methods," STICERD - Econometrics Paper Series /2003/453, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
    Other versions:
  12. Lee, Sang-Won & Hansen, Bruce E., 1994. "Asymptotic Theory for the Garch(1,1) Quasi-Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 10(01), pages 29-52, March. [Downloadable!]
  13. Fan, Yanqin & Ullah, Aman, 1999. "Asymptotic Normality of a Combined Regression Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 191-240, November. [Downloadable!] (restricted)
  14. Jansen, Dennis W & de Vries, Casper G, 1991. "On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 18-24, February. [Downloadable!] (restricted)
    Other versions:
  15. Liang Peng, 2003. "Least absolute deviations estimation for ARCH and GARCH models," Biometrika, Oxford University Press for Biometrika Trust, vol. 90(4), pages 967-975, December.
  16. Joris Pinkse & Margaret E. Slade & Craig Brett, 2002. "Spatial Price Competition: A Semiparametric Approach," Econometrica, Econometric Society, vol. 70(3), pages 1111-1153, May. [Downloadable!] (restricted)
  17. Jonathan B. Hill, 2005. "On Tail Index Estimation for Dependent, Heterogenous Data," Econometrics 0505005, EconWPA, revised 27 May 2005. [Downloadable!]
  18. Slade, Margaret E, 1986. "Exogeneity Tests of Market Boundaries Applied to Petroleum Products," Journal of Industrial Economics, Blackwell Publishing, vol. 34(3), pages 291-303, March. [Downloadable!] (restricted)
  19. Niklas Wagner & Terry A. Marsh, 2004. "Measuring Tail Thickness under GARCH and an Application to Extreme Exchange Rate Changes," Econometrics 0401008, EconWPA. [Downloadable!]
  20. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(03), pages 318-334, September. [Downloadable!]
  21. 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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