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EGARCH models with fat tails, skewness and leverage

Author Info

  • Harvey, A.
  • Sucarrat, G.

Abstract

An EGARCH model in which the conditional distribution is heavy-tailed and skewed is proposed. The properties of the model, including unconditional moments, autocorrelations and the asymptotic distribution of the maximum likelihood estimator, are obtained. Evidence for skewness in conditional t-distribution is found for a range of returns series and the model is shown to give a better .t than the corresponding skewed-t GARCH model.

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File URL: http://www.econ.cam.ac.uk/research/repec/cam/pdf/cwpe1236.pdf
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Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 1236.

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Date of creation: 17 Aug 2012
Date of revision:
Handle: RePEc:cam:camdae:1236
Contact details of provider: Web page: http://www.econ.cam.ac.uk/index.htm

Keywords: General error distribution; heteroskedasticity; leverage; score; Student?s t; two components.;

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Find related papers by JEL classification:
  • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
  • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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  1. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
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  7. John Galbraith & Dongming Zhu, 2009. "A Generalized Asymmetric Student-T Distribution With Application To Financial Econometrics," Departmental Working Papers 2009-02, McGill University, Department of Economics.
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  9. Drew Creal & Siem Jan Koopman & Andre Lucas, 2009. "A General Framework for Observation Driven Time-Varying Parameter Models," Global COE Hi-Stat Discussion Paper Series gd08-038, Institute of Economic Research, Hitotsubashi University.
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  17. Jensen, S ren Tolver & Rahbek, Anders, 2004. "Asymptotic Inference For Nonstationary Garch," Econometric Theory, Cambridge University Press, vol. 20(06), pages 1203-1226, December.
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  19. repec:dgr:uvatin:2011078 is not listed on IDEAS
  20. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, 08.
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  22. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, 06.
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Cited by:

  1. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
  2. Harvey, A. & Luati, A., 2012. "Filtering with heavy tails," Cambridge Working Papers in Economics 1255, Faculty of Economics, University of Cambridge.
  3. Krenar Avdulaj & Jozef Barunik, 2013. "Are benefits from oil - stocks diversification gone? A new evidence from a dynamic copulas and high frequency data," Papers 1307.5981, arXiv.org.
  4. Michele Caivano & Andrew Harvey, 2014. "Time series models with an EGB2 conditional distribution," Temi di discussione (Economic working papers) 947, Bank of Italy, Economic Research and International Relations Area.
  5. Michele Caivano & Andrew Harvey, 2014. "Two EGARCH models and one fat tail," Temi di discussione (Economic working papers) 954, Bank of Italy, Economic Research and International Relations Area.

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