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Whittle Estimation Of Arch Models

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  • Giraitis, Liudas
  • Robinson, Peter M.

Abstract

For a class of parametric ARCH models, Whittle estimation based on squared observations is shown to be [square root of n]-consistent and asymptotically normal. Our conditions require the squares to have short memory autocorrelation, by comparison with the work of Zaffaroni (1999, Gaussian Inference on Certain Long-Range Dependent Volatility Models, Preprint), who established the same properties on the basis of an alternative class of models with martingale difference levels and long memory autocorrelated squares.

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Bibliographic Info

Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 17 (2001)
Issue (Month): 03 (June)
Pages: 608-631

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Handle: RePEc:cup:etheor:v:17:y:2001:i:03:p:608-631_17

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Cited by:
  1. repec:hal:journl:halshs-00235179 is not listed on IDEAS
  2. Liudas Giraitis, 2004. "LARCH, Leverage, and Long Memory," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(2), pages 177-210.
  3. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
  4. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
  5. repec:hal:cesptp:halshs-00235179 is not listed on IDEAS
  6. Tomohito Naito & Kohei Asai & Tomoyuki Amano & Masanobu Taniguchi, 2010. "Local Whittle likelihood estimators and tests for non-Gaussian stationary processes," Statistical Inference for Stochastic Processes, Springer, vol. 13(3), pages 163-174, October.
  7. Abdou Kâ Diongue & Dominique Guegan, 2008. "Estimation of k-factor GIGARCH process : a Monte Carlo study," Documents de travail du Centre d'Economie de la Sorbonne b08004, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  8. Liudas Giraitis & Remigijus Leipus & Peter M. Robinson & Donatas Surgailis, 2004. "LARCH, leverage, and long memory," LSE Research Online Documents on Economics 294, London School of Economics and Political Science, LSE Library.
  9. Peter M Robinson & Paolo Zaffaroni, 2005. "Pseudo-Maximum Likelihood Estimation of ARCH(8) Models," STICERD - Econometrics Paper Series /2005/495, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  10. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2010. "Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5759-5768.
  11. Peter M. Robinson & Paolo Zafaroni, 2005. "Pseudo-maximum likelihood estimation of ARCH models," LSE Research Online Documents on Economics 4544, London School of Economics and Political Science, LSE Library.
  12. Mikosch, Thomas & Straumann, Daniel, 0. "Whittle estimation in a heavy-tailed GARCH(1,1) model," Stochastic Processes and their Applications, Elsevier, vol. 100(1-2), pages 187-222, July.
  13. Han, Heejoon & Park, Joon Y., 2012. "ARCH/GARCH with persistent covariate: Asymptotic theory of MLE," Journal of Econometrics, Elsevier, vol. 167(1), pages 95-112.
  14. Jörg Polzehl & Vladimir Spokoiny, 2006. "Varying coefficient GARCH versus local constant volatility modeling. Comparison of the predictive power," SFB 649 Discussion Papers SFB649DP2006-033, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  15. repec:hal:journl:halshs-00375758 is not listed on IDEAS

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