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Spurious persistence in stochastic volatility

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  • Messow, Philip
  • Krämer, Walter

Abstract

We show that structural changes in stochastic volatility models induce spurious persistence. Other than in GARCH-type models, implied persistence does not tend to unity with given size of the structural change and increasing sample size.

Suggested Citation

  • Messow, Philip & Krämer, Walter, 2013. "Spurious persistence in stochastic volatility," Economics Letters, Elsevier, vol. 121(2), pages 221-223.
    • Handle: RePEc:eee:ecolet:v:121:y:2013:i:2:p:221-223
    DOI: 10.1016/j.econlet.2013.08.008
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    References listed on IDEAS

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    1. Kramer, Walter & Azamo, Baudouin Tameze, 2007. "Structural change and estimated persistence in the GARCH(1,1)-model," Economics Letters, Elsevier, vol. 97(1), pages 17-23, October.
    2. PREMINGER, Arie & HAFNER, Christian, 2006. "Deciding between GARCH and stochastic volatility via strong decision rules," LIDAM Discussion Papers CORE 2006042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    4. Thomas Mikosch & Cătălin Stărică, 2004. "Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 378-390, February.
    5. Zacharias Psaradakis & Elias Tzavalis, 1999. "On regression-based tests for persistence in logarithmic volatility models," Econometric Reviews, Taylor & Francis Journals, vol. 18(4), pages 441-448.
    6. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
    7. 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 225-234, April.
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    Cited by:

    1. Laurini, Márcio Poletti & Mauad, Roberto Baltieri, 2015. "A common jump factor stochastic volatility model," Finance Research Letters, Elsevier, vol. 12(C), pages 2-10.
    2. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Mutual information and persistence in the stochastic volatility of market returns: An emergent market example," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 36-59.

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    More about this item

    Keywords

    Persistence; Stochastic volatility; Structural change;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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