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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.

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File URL: http://qed.econ.queensu.ca:80/jae/1999-v14.3/
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 14 (1999)
Issue (Month): 3 (May-June)
Pages: 309-18

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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. Yong Li & Jun Yu, 2012. "A New Bayesian Unit Root Test in Stochastic Volatility Models," Working Papers 14-2012, Singapore Management University, School of Economics.
  3. Luis Gil-Alana, 2003. "Stochastic behavior of nominal exchange rates," Atlantic Economic Journal, International Atlantic Economic Society, vol. 31(2), pages 159-173, June.
  4. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2006. "Modelling stochastic volatility in asset returns using fractionally integrated semiparametric techniques," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 2(1), pages 9-12, January.
  5. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.

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