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Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity

Author

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  • Liudas Giraitis
  • Piotr Kokoszka
  • Remigijus Leipus
  • Gilles Teyssière

Abstract

The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis, Robinson and Surgailis (1999). We consider methods based on the partial sums of the squared observations which are similar in spirit to the classicla R/S analysis as well as spectral domain approximate maximum likelihood estimators. The finite sample performance of the estimators is examined by means of a Monte Carlo study.
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Suggested Citation

  • Liudas Giraitis & Piotr Kokoszka & Remigijus Leipus & Gilles Teyssière, 2000. "Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity," Statistical Inference for Stochastic Processes, Springer, vol. 3(1), pages 113-128, January.
    • Handle: RePEc:spr:sistpr:v:3:y:2000:i:1:p:113-128
    DOI: 10.1023/A:1009951213271
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    References listed on IDEAS

    as
    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    3. 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.
    4. 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.
    5. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-283, July.
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    Citations

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    Cited by:

    1. Dominique Guegan, 2005. "How can we Define the Concept of Long Memory? An Econometric Survey," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 113-149.
    2. Jonathan Dark, 2004. "Long memory in the volatility of the Australian All Ordinaries Index and the Share Price Index futures," Monash Econometrics and Business Statistics Working Papers 5/04, Monash University, Department of Econometrics and Business Statistics.
    3. Härdle, Wolfgang Karl & Mungo, Julius, 2008. "Value-at-risk and expected shortfall when there is long range dependence," SFB 649 Discussion Papers 2008-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus & Teyssiere, Gilles, 2003. "Rescaled variance and related tests for long memory in volatility and levels," Journal of Econometrics, Elsevier, vol. 112(2), pages 265-294, February.
    5. repec:hum:wpaper:sfb649dp2008-006 is not listed on IDEAS
    6. Kirman, Alan & Teyssiere, Gilles, 2005. "Testing for bubbles and change-points," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 765-799, April.
    7. Jonathan Dark, 2004. "Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures," Monash Econometrics and Business Statistics Working Papers 4/04, Monash University, Department of Econometrics and Business Statistics.
    8. Dominique Guegan, 2005. "How can we Define the Concept of Long Memory? An Econometric Survey," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 113-149.

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    More about this item

    Keywords

    long memory; ARCH models; semiparametric estimation; modified R / S ; KPSS and V / S statistics; periodogram;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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