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Testing for changes in volatility in heteroskedastic time series - a further examination

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  • de Pooter, M.D.
  • van Dijk, D.J.C.

Abstract

We consider tests for sudden changes in the unconditional volatility of conditionally heteroskedastic time series based on cumulative sums of squares. When applied to the original series these tests suffer from severe size distortions, where the correct null hypothesis of no volatility change is rejected much too frequently. Applying the tests to standardized residuals from an estimated GARCH model results in good size and reasonable power properties when testing for a single break in the variance. The tests also appear to be robust to different types of misspecification. An iterative algorithm is designed to test sequentially for the presence of multiple changes in volatility. An application to emerging markets stock returns clearly illustrates the properties of the different test statistics.

Suggested Citation

  • de Pooter, M.D. & van Dijk, D.J.C., 2004. "Testing for changes in volatility in heteroskedastic time series - a further examination," Econometric Institute Research Papers EI 2004-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:1627
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    References listed on IDEAS

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    Cited by:

    1. Taewook Lee & Moosup Kim & Changryong Baek, 2015. "Tests for Volatility Shifts in Garch Against Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 127-153, March.
    2. David McMillan & Mark Wohar, 2011. "Structural breaks in volatility: the case of UK sector returns," Applied Financial Economics, Taylor & Francis Journals, vol. 21(15), pages 1079-1093.
    3. Marco Barassi & Lajos Horváth & Yuqian Zhao, 2020. "Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 340-349, April.
    4. Gebka, Bartosz & Wohar, Mark E., 2013. "Causality between trading volume and returns: Evidence from quantile regressions," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 144-159.
    5. Xu, Ke-Li & Phillips, Peter C.B., 2008. "Adaptive estimation of autoregressive models with time-varying variances," Journal of Econometrics, Elsevier, vol. 142(1), pages 265-280, January.
    6. Babikir, Ali & Gupta, Rangan & Mwabutwa, Chance & Owusu-Sekyere, Emmanuel, 2012. "Structural breaks and GARCH models of stock return volatility: The case of South Africa," Economic Modelling, Elsevier, vol. 29(6), pages 2435-2443.
    7. Vivian, Andrew & Wohar, Mark E., 2012. "Commodity volatility breaks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(2), pages 395-422.
    8. Efe Çağlar Çağli & Pinar Evrim Mandaci & Pinar Hakan Kahyaoğlu, 2011. "Volatility Shifts and Persistence in Variance: Evidence from the Sector Indices of Istanbul Stock Exchange," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 4(3), pages 119-140, December.
    9. Vasiliki Chatzikonstanti, 2017. "Breaks and outliers when modelling the volatility of the U.S. stock market," Applied Economics, Taylor & Francis Journals, vol. 49(46), pages 4704-4717, October.
    10. Haejune Oh & Sangyeol Lee, 2019. "Modified residual CUSUM test for location-scale time series models with heteroscedasticity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1059-1091, October.
    11. Bala A. Dahiru & Pam W. Jim & Kalu N. Nwonyuku, 2017. "Equity markets volatility dynamics in developed and newly emerging economies: EGARCH-with-skewed-t density approach," Economics Bulletin, AccessEcon, vol. 37(4), pages 2394-2412.
    12. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Reboredo, Juan Carlos & Wen, Xiaoqian, 2014. "Dependence of stock and commodity futures markets in China: Implications for portfolio investment," Emerging Markets Review, Elsevier, vol. 21(C), pages 183-200.

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    More about this item

    Keywords

    CUSUM; GARCH models; change-point tests; emerging markets; structural breaks;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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