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Forecasting volatility

Author

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  • Thavaneswaran, A.
  • Appadoo, S.S.
  • Peiris, S.

Abstract

This paper studies the problem of volatility forecasting for some financial time series models. We consider several stochastic volatility models including GARCH, Power GARCH and non-stationary GARCH for illustration. In particular, a martingale representation is used to obtain the l-steps-ahead forecast error variance for the class of GARCH models. Some closed-form expressions for the variance of l-steps-ahead forecasts errors are given in terms of [psi] weights and the kurtosis of the error distribution.

Suggested Citation

  • Thavaneswaran, A. & Appadoo, S.S. & Peiris, S., 2005. "Forecasting volatility," Statistics & Probability Letters, Elsevier, vol. 75(1), pages 1-10, November.
  • Handle: RePEc:eee:stapro:v:75:y:2005:i:1:p:1-10
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    1. Bovas Abraham & A. Thavaneswaran, 1991. "A nonlinear time series model and estimation of missing observations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(3), pages 493-504, September.
    2. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
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    6. He, Changli & Teräsvirta, Timo, 1999. "FOURTH MOMENT STRUCTURE OF THE GARCH(p,q) PROCESS," Econometric Theory, Cambridge University Press, vol. 15(6), pages 824-846, December.
    7. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-359, October.
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    Cited by:

    1. Shelton Peiris & Tim Swartz, 2020. "Revisiting the Kurtosis of Stationary Processes with Applications to Volatility Models," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(2), pages 1-1.
    2. Guidi, Francesco, 2010. "Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models," MPRA Paper 19851, University Library of Munich, Germany.
    3. Ghahramani, M. & Thavaneswaran, A., 2009. "On some properties of Autoregressive Conditional Poisson (ACP) models," Economics Letters, Elsevier, vol. 105(3), pages 273-275, December.
    4. Liang, Y. & Thavaneswaran, A. & Ravishanker, N., 2013. "RCA models: Joint prediction of mean and volatility," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 527-533.
    5. Thavaneswaran, A. & Peiris, S. & Appadoo, S., 2008. "Random coefficient volatility models," Statistics & Probability Letters, Elsevier, vol. 78(6), pages 582-593, April.

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