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Detecting Level Shifts In The Presence Of Conditional Heteroscedasticity

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Author Info

  • M. Angeles Carnero

    ()
    (Universidad de Alicante)

  • Daniel Peña

    ()
    (Universidad Carlos III de Madrid)

  • Esther Ruiz

    ()
    (Universidad Carlos III de Madrid)

Abstract

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series. We show that the behavior of the likelihood ratio test is not appropriate in this context whereas if the test statistic is appropriately standardized, it works better. We also compare two alternative procedures for testing for several level shifts. The results are illustrated by analyzing daily returns of exchange rates.

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File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2004-06.pdf
File Function: Fisrt version / Primera version, 2004
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Bibliographic Info

Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2004-06.

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Length: 35 pages
Date of creation: Feb 2004
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2004-06

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Related research

Keywords: EGARCH; GARCH; Likelihood Ratio; Stochastic Volatility.;

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References

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  1. Kramer, Walter & Ploberger, Werner & Alt, Raimund, 1988. "Testing for Structural Change in Dynamic Models," Econometrica, Econometric Society, vol. 56(6), pages 1355-69, November.
  2. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2001. "Outliers And Conditional Autoregressive Heteroscedasticity In Time Series," Statistics and Econometrics Working Papers ws010704, Universidad Carlos III, Departamento de Estadística y Econometría.
  3. Pena D. & Rodriguez J., 2002. "A Powerful Portmanteau Test of Lack of Fit for Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 601-610, June.
  4. Donald W.K. Andrews, 1990. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Cowles Foundation Discussion Papers 943, Cowles Foundation for Research in Economics, Yale University.
  5. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
  6. Ghysels, E. & Harvey, A. & Renault, E., 1996. "Stochastic Volatility," Cahiers de recherche 9613, Universite de Montreal, Departement de sciences economiques.
  7. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
  8. Filippo Altissimo & Valentina Corradi, 2000. "Strong Rules for Detecting the Number of Breaks in a Time Series," Econometric Society World Congress 2000 Contributed Papers 0574, Econometric Society.
  9. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier.
  10. Balke, Nathan S, 1993. "Detecting Level Shifts in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 81-92, January.
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Cited by:
  1. Pedro Galeano, 2004. "Use Of Cumulative Sums For Detection Of Changepoints In The Rate Parameter Of A Poisson Process," Statistics and Econometrics Working Papers ws046816, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. Pedro Galeano & Daniel Peña & Ruey S. Tsay, 2004. "Outlier Detection In Multivariate Time Series Via Projection Pursuit," Statistics and Econometrics Working Papers ws044211, Universidad Carlos III, Departamento de Estadística y Econometría.

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