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Breaks and outliers when modelling the volatility of the U.S. stock market

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  • Vasiliki Chatzikonstanti

Abstract

This study analyses volatility persistence of the U.S. stock market, after taking into account the role of breaks and outliers. By employing a wavelet-based algorithm, it identifies several outliers which are comfortably associated with major events such as the ‘Black Monday’ and the Asian crisis. There is also evidence of clustering of breaks and a substantial variation in the properties of the identified segments.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:46:p:4704-4717
    DOI: 10.1080/00036846.2017.1293785
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Lorraine Muguto & Paul-Francois Muzindutsi, 2022. "A Comparative Analysis of the Nature of Stock Return Volatility in BRICS and G7 Markets," JRFM, MDPI, vol. 15(2), pages 1-27, February.
    2. Alanya-Beltran, Willy, 2022. "Unit roots in lower-bounded series with outliers," Economic Modelling, Elsevier, vol. 115(C).
    3. Yaqoob, Tanzeela & Maqsood, Arfa, 2024. "The potency of time series outliers in volatile models: An empirical analysis of fintech, and mineral resources," Resources Policy, Elsevier, vol. 89(C).
    4. Somayeh Kokabisaghi & Eric J. Pauwels & Katrien Van Meulder & André B. Dorsman, 2018. "Are These Shocks for Real? Sensitivity Analysis of the Significance of the Wavelet Response to Some CKLS Processes," IJFS, MDPI, vol. 6(3), pages 1-12, September.

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