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Nonstationarities in Stock Returns

  • Cătălin Stărică

    (Chalmers University of Technology and Göteborg University)

  • Clive Granger

    (University of California, San Diego)

The paper outlines a methodology for analyzing daily stock returns that relinquishes the assumption of global stationarity. Giving up this common working hypothesis reflects our belief that fundamental features of the financial markets are continuously and significantly changing. Our approach approximates the nonstationary data locally by stationary models. The methodology is applied to the S&P 500 series of returns covering a period of over seventy years of market activity. We find most of the dynamics of this time series to be concentrated in shifts of the unconditional variance. The forecasts based on our nonstationary unconditional modeling were found to be superior to those obtained in a stationary long-memory framework and to those based on a stationary Garch(1, 1) data-generating process. © 2005 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 87 (2005)
Issue (Month): 3 (August)
Pages: 503-522

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Handle: RePEc:tpr:restat:v:87:y:2005:i:3:p:503-522
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  1. Hidalgo, Javier & Robinson, Peter M., 1996. "Testing for structural change in a long-memory environment," Journal of Econometrics, Elsevier, vol. 70(1), pages 159-174, January.
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