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Nonlinearities and Nonstationarities in Stock Returns

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  • de Lima, Pedro J F

Abstract

This article addresses the question of whether recent findings of nonlinearities in high-frequency financial time series have been contaminated by possible shifts in the distribution of the data. It applies a recursive version of the Brock-Dechert-Scheinkman statistic to daily data on two stock-market indexes between January 1980 and December 1990. It is shown that October 1987 is highly influential in the characterization of the stock-market dynamics and appears to correspond to a shift in the distribution of stock returns. Sampling experiments show that simple linear processes with shifts in variance can replicate the behavior of the tests but autoregressive conditional heteroscedastic filters are unable to do so.

Suggested Citation

  • de Lima, Pedro J F, 1998. "Nonlinearities and Nonstationarities in Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 227-236, April.
  • Handle: RePEc:bes:jnlbes:v:16:y:1998:i:2:p:227-36
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    Cited by:

    1. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(2), pages 290-318.
    2. Rosser, J. Jr. & Ahmed, Ehsan & Hartmann, Georg C., 2003. "Volatility via social flaring," Journal of Economic Behavior & Organization, Elsevier, vol. 50(1), pages 77-87, January.
    3. Pedro J. F. de Lima & Michelle L. Barnes, 2000. "Modeling Financial Volatility: Extreme Observations, Nonlinearities and Nonstationarities," School of Economics Working Papers 2000-05, University of Adelaide, School of Economics.
    4. Evzen Kocenda, 2001. "An Alternative To The Bds Test: Integration Across The Correlation Integral," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 337-351.
    5. McKenzie, Michael D., 2001. "Chaotic behavior in national stock market indices: New evidence from the close returns test," Global Finance Journal, Elsevier, vol. 12(1), pages 35-53.
    6. Evzen Kocenda & Lubos Briatka, 2004. "Advancing the iid Test Based on Integration across the Correlation Integral: Ranges, Competition, and Power," CERGE-EI Working Papers wp235, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    7. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    8. Korkie, Bob & Sivakumar, Ranjini & Turtle, Harry, 2002. "The dual contributions of information instruments in return models: magnitude and direction predictability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 511-523, December.
    9. Thomas Lux, 2008. "Stochastic Behavioral Asset Pricing Models and the Stylized Facts," Working Papers wp08-03, Warwick Business School, Finance Group.
    10. Evzen Kocenda & Lubos Briatka, 2005. "Optimal Range for the iid Test Based on Integration Across the Correlation Integral," Econometric Reviews, Taylor & Francis Journals, vol. 24(3), pages 265-296.

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