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Testing for Changes in the Unconditional Variance of Financial Time Series

  • Andreu Sansó

    ()

  • Vicent Aragó
  • Josep Lluís Carrion

Inclan and Tiao (1994) proposed a test for the detection of changes of the unconditional variance which has been used in financial time series analysis. In this article we show some serious drawbacks for using this test with this type of data. Specifically, it su.ers important size distortions for leptokurtic and platykurtic innovations. Moreover, the size distortions are more extreme for heteroskedastic conditional variance processes. These results invalidate in practice the use of the test for financial time series. To overcome these problems we propose new tests that explicitly consider the fourth moment properties of the disturbances and the conditional heteroskedasticity. Monte Carlo experiments show the good performance of these tests. The application of the new tests to the same series in Aggarwal, Inclan and Leal (1999) reveal that the changes in variance they detect are spurious.

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Paper provided by Universitat de les Illes Balears, Departament d'Economía Aplicada in its series DEA Working Papers with number 5.

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Date of creation: Nov 2003
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Handle: RePEc:ubi:deawps:5
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  1. Shiqing Ling & Michael McAleer, 2001. "Stationarity and the Existence of Moments of a Family of GARCH Processes," ISER Discussion Paper 0535, Institute of Social and Economic Research, Osaka University.
  2. Perron, P., 1989. "Testing For A Unit Root In A Time Series With A Changing Mean," Papers 347, Princeton, Department of Economics - Econometric Research Program.
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  5. Kenneth D. West & Whitney K. Newey, 1995. "Automatic Lag Selection in Covariance Matrix Estimation," NBER Technical Working Papers 0144, National Bureau of Economic Research, Inc.
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  7. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  9. Bwo-Nung Huang & Chin-Wei Yang, 2001. "The impact of settlement time on the volatility of stock market revisited: an application of the iterated cumulative sums of squares detection method for changes of variance," Applied Economics Letters, Taylor & Francis Journals, vol. 8(10), pages 665-668.
  10. 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.
  11. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(03), pages 318-334, September.
  12. 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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  14. Bera, Anil K & Higgins, Matthew L, 1993. " ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-66, December.
  15. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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