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On the relationship of persistence and number of breaks in volatility: new evidence for three CEE countries

  • Výrost, Tomáš
  • Baumöhl, Eduard
  • Lyócsa, Štefan

In this article, we contribute to the discussion of volatility persistence in the presence of sudden changes. We follow previous research, particularly Wang and Moore (2009), who analysed stock market returns in five Central and Eastern European countries using the Iterated Cumulative Sum of Squares (ICSS) algorithm for detecting multiple breaks and the test (IT) proposed by Inclán and Tiao (1994). We complement this analysis by using the κ1 and κ2 statistic introduced by Sansó et al. (2004), which lead us to the hypothesis that the estimated persistence in volatility depends inversely on the number of breakpoints in volatility. We explored this claim through a simulation study, where by randomizing an increasing number of breakpoints over the sample, we estimated kernel density of the persistence measure. The results confirmed the relationship between persistence and the number of breakpoints. It also showed that the use of break detection algorithms leads to lower persistence estimates, even within the class of models with an equal number of breaks. Therefore, the overall decrease in persistence can be attributed both to the number of breaks and their position, as suggested by the chosen break detection tests.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 27927.

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Date of creation: 06 Jan 2011
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Handle: RePEc:pra:mprapa:27927
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  1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  2. Newey, Whitney K & West, Kenneth D, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Wiley Blackwell, vol. 61(4), pages 631-53, October.
  3. Konstantin Kholodilin & Vincent Wenxiong Yao, 2006. "Modelling the structural break in volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 13(7), pages 417-422.
  4. Wang, Ping & Moore, Tomoe, 2009. "Sudden changes in volatility: The case of five central European stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(1), pages 33-46, February.
  5. James M. Poterba & Lawrence H. Summers, 1984. "The Persistence of Volatility and Stock Market Fluctuations," NBER Working Papers 1462, National Bureau of Economic Research, Inc.
  6. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  8. Farooq Malik & Bradley Ewing & James Payne, 2005. "Measuring volatility persistence in the presence of sudden changes in the variance of Canadian stock returns," Canadian Journal of Economics, Canadian Economics Association, vol. 38(3), pages 1037-1056, August.
  9. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
  10. J. D. Byers & D. A. Peel, 2001. "Volatility persistence in asset markets: long memory in high/low prices," Applied Financial Economics, Taylor & Francis Journals, vol. 11(3), pages 253-260.
  11. Aggarwal, Reena & Inclan, Carla & Leal, Ricardo, 1999. "Volatility in Emerging Stock Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(01), pages 33-55, March.
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