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Identifying Volatility Clusters Using the PPM: A Sensitivity Analysis

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  • Rosangela Loschi

    ()

  • Leonardo Bastos

    ()

  • Pilar Iglesias

    ()

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    Abstract

    Several previous works show that, in general, financial time series are characterized by periods of large volatility followed by periods of relative quitness. In this paper we consider the product partition model (PPM) to identify changes in the volatility extending it to identify multiple change points in normal variances assuming known means. Yao’s prior cohesions and a conjugate prior distribution for the variance – which in this case is a Inverted-Gamma distribution – are assumed. The ultimate goal is to provide a sensitivity analysis to the product estimates assuming different prior specifications for the parameter which indexes the Yao’s cohesions and also for the variance. We analyze a Chilean stock market return series and conclude that the product estimates for the volatility of this series are strongly influenced by the prior specifications of both parameters. Copyright Springer Science + Business Media, Inc. 2005

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    File URL: http://hdl.handle.net/10.1007/s10614-005-5169-0
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    Bibliographic Info

    Article provided by Society for Computational Economics in its journal Computational Economics.

    Volume (Year): 24 (2005)
    Issue (Month): 4 (June)
    Pages: 305-319

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    Handle: RePEc:kap:compec:v:24:y:2005:i:4:p:305-319

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    Related research

    Keywords: Inverted-Gamma distribution; Student-t distribution; Yao’s cohesions;

    References

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    1. Loschi, R. H. & Cruz, F. R. B., 2002. "An analysis of the influence of some prior specifications in the identification of change points via product partition model," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 477-501, June.
    2. Arellano-Valle, Reinaldo B. & Bolfarine, Heleno, 1995. "On some characterizations of the t-distribution," Statistics & Probability Letters, Elsevier, vol. 25(1), pages 79-85, October.
    3. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    4. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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