Identifying Volatility Clusters Using the PPM: A Sensitivity Analysis
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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Volume (Year): 24 (2005)
Issue (Month): 4 (June)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jushan Bai & Pierre Perron, 1998.
"Estimating and Testing Linear Models with Multiple Structural Changes,"
Econometric Society, vol. 66(1), pages 47-78, January.
- 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.
- Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Universite de Montreal, Departement de sciences economiques.
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- 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.
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