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Estimation of k-Factor Gigarch Process: A Monte Carlo Study

Author

Listed:
  • Diongue Abdou Ka

    (UGB - Université Gaston Berger de Saint-Louis Sénégal)

  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

In this paper, we discuss the parameter estimation for a k-factor generalized long memory processwith conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of Whittle's estimation approach. For comparison purposes, Monte Carlo simulations are used to evaluate the finite sample performance of these estimation techniques, using four different conditional distribution functions.

Suggested Citation

  • Diongue Abdou Ka & Dominique Guegan, 2008. "Estimation of k-Factor Gigarch Process: A Monte Carlo Study," PSE-Ecole d'économie de Paris (Postprint) halshs-00375758, HAL.
  • Handle: RePEc:hal:pseptp:halshs-00375758
    DOI: 10.1080/03610910802304994
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00375758
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    References listed on IDEAS

    as
    1. Abdou Kâ Diongue & Dominique Guegan, 2004. "Estimating parameters for a k-GIGARCH process," Post-Print halshs-00188531, HAL.
    2. Dominique Guegan & Abdou Kâ Diongue & Bertrand Vignal, 2004. "A k- factor GIGARCH process : estimation and application to electricity market spot prices," Post-Print halshs-00188533, HAL.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Giraitis, Liudas & Robinson, Peter M., 2001. "Whittle Estimation Of Arch Models," Econometric Theory, Cambridge University Press, vol. 17(3), pages 608-631, June.
    5. Fernández, C. & Steel, M.F.J., 1996. "On Bayesian Modelling of Fat Tails and Skewness," Other publications TiSEM 0991c197-c9e8-4904-8119-3, Tilburg University, School of Economics and Management.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    8. M. C. Jones & M. J. Faddy, 2003. "A skew extension of the t‐distribution, with applications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 159-174, February.
    9. Dominique Guegan, 2000. "A New Model: The k-Factor GIGARCH Process," Post-Print halshs-00199207, HAL.
    10. Dominique Guegan, 2003. "A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates," Post-Print halshs-00201314, HAL.
    11. Laurent Ferrara & Dominique Guegan, 2001. "Comparison of parameter estimation methods in cyclical long memory time series," Post-Print halshs-00196426, HAL.
    12. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    13. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    14. Peiro, Amado, 1999. "Skewness in financial returns," Journal of Banking & Finance, Elsevier, vol. 23(6), pages 847-862, June.
    15. Henry L. Gray & Nien‐Fan Zhang & Wayne A. Woodward, 1989. "On Generalized Fractional Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 233-257, May.
    16. Giraitis, Liudas & Robinson, Peter M., 2001. "Whittle estimation of ARCH models," LSE Research Online Documents on Economics 316, London School of Economics and Political Science, LSE Library.
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    Cited by:

    1. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    2. Heni Boubaker, 2015. "Wavelet Estimation of Gegenbauer Processes: Simulation and Empirical Application," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 551-574, December.

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    More about this item

    Keywords

    Long memory; Gegenbauer polynomial; heteroskedasticity; Conditional Sum of Squares; Whittle estimation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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