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Modèls Garch à la mémoire longue: application aux taux de change tunisiens
[GARCH models : evidence from Tunisian Exchange market]

  • Lahiani, Amine
  • Yousfi, Ouidad

This paper deals with statistics�and econometrics�properties of fractionally integra- ted GARCH (FIGARCH). We compare these characteristics with those of traditional models. We insist on the GARCH exponential/IGARCH in�nite decrease of volatility impact. Then, we apply it on three Tunisian exchange rate series between 1994 and 2006. As Beine, Laurent and Lecourt (2002), the contributions of the FIGARCH model are extended by accounting for the observed kurtosis through a student-t based maximum likelihood estimation. This estimation improves the goodness of �t properties of this model and may lead to di¤erent interest parameters estimates.

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File URL: http://mpra.ub.uni-muenchen.de/28702/1/MPRA_paper_28702.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 28702.

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Date of creation: Dec 2007
Date of revision: 2008
Publication status: Published in Euro-Mediterranean Economics and Finance Review 4.3(2008): pp. 106-122
Handle: RePEc:pra:mprapa:28702
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  1. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
  2. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus, 2000. "Stationary Arch Models: Dependence Structure And Central Limit Theorem," Econometric Theory, Cambridge University Press, vol. 16(01), pages 3-22, February.
  3. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
  4. 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.
  5. Nelson, Daniel B., 1990. "ARCH models as diffusion approximations," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 7-38.
  6. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
  7. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  8. Bougerol, Philippe & Picard, Nico, 1992. "Stationarity of Garch processes and of some nonnegative time series," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 115-127.
  9. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
  10. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
  11. Sandrine LARDIC & Valérie MIGNON, 1999. "Prévision ARFIMA des taux de change : les modélisateurs doivent-ils encore exhorter à la naïveté des prévisions ?," Annales d'Economie et de Statistique, ENSAE, issue 54, pages 47-68.
  12. Thomas Mikosch & Catalin Starica, 2004. "Long range dependence effects and ARCH modelling," Econometrics 0412004, EconWPA.
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