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Non-stationarities in stock returns

  • Catalin Starica

    (Dept. Mathematical Statistics, Chalmers University of Technology)

  • Clive Granger

    (Dept. Economics, UCSD)

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 locally the non-stationary data 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 non-stationary unconditional modeling were found to be superior to those obtained in a stationary long memory framework or to those based on a stationary Garch(1,1) data generating process.

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File URL: http://econwpa.repec.org/eps/em/papers/0411/0411016.pdf
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Paper provided by EconWPA in its series Econometrics with number 0411016.

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Length: 67 pages
Date of creation: 22 Nov 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0411016
Note: Type of Document - pdf; pages: 67
Contact details of provider: Web page: http://econwpa.repec.org

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  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. repec:att:wimass:9417 is not listed on IDEAS
  3. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
  4. Granger, Clive W. J. & Terasvirta, Timo, 1999. "A simple nonlinear time series model with misleading linear properties," Economics Letters, Elsevier, vol. 62(2), pages 161-165, February.
  5. 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 225-34, April.
  6. Clive W.J. Granger & Namwon Hyung, 2013. "Occasional Structural Breaks and Long Memory," Annals of Economics and Finance, Society for AEF, vol. 14(2), pages 739-764, November.
  7. I.N. Lobato & N.E. Savin, 1996. "Real and Spurious Long Memory Properties of Stock Market Data," Econometrics 9605004, EconWPA, revised 26 Sep 1996.
  8. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-83, July.
  9. Simonato, Jean-Guy, 1992. "Estimation of GARCH process in the presence of structural change," Economics Letters, Elsevier, vol. 40(2), pages 155-158, October.
  10. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  11. Catalin Starica & Stefano Herzel & Tomas Nord, 2005. "Why does the GARCH(1,1) model fail to provide sensible longer- horizon volatility forecasts?," Econometrics 0508003, EconWPA.
  12. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  13. James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
  14. Kenneth D. West, 1994. "Asymptotic Inference About Predictive Ability," Macroeconomics 9410002, EconWPA.
  15. 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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