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Testing for a Unit Root in the Volatility of Asset Returns

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  • Wright, Jonathan H

Abstract

It is now well established that the volatility of asset returns is time varying and highly persistent. One leading model that is used to represent these features of the data is the stochastic volatility model. The researcher may test for non-stationarity of the volatility process by testing for a unit root in the log-squared time series. This strategy for inference has many advantages, but is not followed in practice because these unit root tests are known to have very poor size properties. In this paper I show that new tests that are robust to negative MA roots allow a reliable test for a unit root in the volatility process to be conducted. In applying these tests to exchange rate and stock returns, strong rejections of non-stationarity in volatility are obtained.

Suggested Citation

  • Wright, Jonathan H, 1999. "Testing for a Unit Root in the Volatility of Asset Returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(3), pages 309-318, May-June.
    • Handle: RePEc:jae:japmet:v:14:y:1999:i:3:p:309-18
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    Cited by:

    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-059, New York University, Leonard N. Stern School of Business-.
    2. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    3. Luis Gil-Alana, 2003. "Stochastic behavior of nominal exchange rates," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 31(2), pages 159-173, June.
    4. Kevin B. Grier & Aaron D. Smallwood, 2007. "Uncertainty and Export Performance: Evidence from 18 Countries," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(4), pages 965-979, June.
    5. Hansen, Peter R. & Lunde, Asger, 2014. "Estimating The Persistence And The Autocorrelation Function Of A Time Series That Is Measured With Error," Econometric Theory, Cambridge University Press, vol. 30(1), pages 60-93, February.
    6. Benth, Fred Espen & Paraschiv, Florentina, 2018. "A space-time random field model for electricity forward prices," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 203-216.
    7. Yong Li & Jun Yu, 2010. "A New Bayesian Unit Root Test in Stochastic Volatility Models," Working Papers 21-2010, Singapore Management University, School of Economics, revised Oct 2010.
    8. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    9. Yong Li & Jun Yu, 2019. "An Improved Bayesian Unit Root Test in Stochastic Volatility Models," Annals of Economics and Finance, Society for AEF, vol. 20(1), pages 103-122, May.

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