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Identifying Volatility Clusters Using the PPM: A Sensitivity Analysis

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  • Rosangela Loschi
  • Leonardo Bastos
  • Pilar Iglesias

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

Suggested Citation

  • Rosangela Loschi & Leonardo Bastos & Pilar Iglesias, 2005. "Identifying Volatility Clusters Using the PPM: A Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 24(4), pages 305-319, June.
    • Handle: RePEc:kap:compec:v:24:y:2005:i:4:p:305-319
    DOI: 10.1007/s10614-005-5169-0
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    References listed on IDEAS

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    1. Ulrich Menzefricke, 1981. "A Bayesian Analysis of a Change in the Precision of a Sequence of Independent Normal Random Variables at an Unknown Time Point," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(2), pages 141-146, June.
    2. 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.
    3. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    4. 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.
    5. 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.
    6. Fernando A. Quintana & Pilar L. Iglesias, 2003. "Bayesian clustering and product partition models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 557-574, May.
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