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The Search for Chaos and Nonlinearities in Swedish Stock Index Returns

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  • Amilon, Henrik

    ()
    (Department of Economics, Lund University)

  • Byström, Hans

    ()
    (Department of Economics, Lund University)

Abstract

Numerous empirical studies have shown evidence of nonlinearities in financial time series, which can be of both a deterministic and a stochastic nature. Chaos is an example of the former, and heteroscedasticity in the conditional variance an example of the latter. We apply a test, the BDS test, to Swedish Stock Index returns and detect large deviations from the IID-hypothesis. There is no evidence of chaos, and most of the nonlinearities are due to conditionally heteroscedastic error terms. We look at monthly, daily, and 15-minute return series, and find no sensitivity in the results to choice of sampling frequency. Different GARCH models often seem to explain the nonlinearities detected by the BDS test, which is particularly the case for GARCH models with t-distributed errors fitted to monthly and daily returns.

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Bibliographic Info

Paper provided by Lund University, Department of Economics in its series Working Papers with number 1998:6.

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Length: 25 pages
Date of creation: 01 Dec 1998
Date of revision:
Handle: RePEc:hhs:lunewp:1998_006

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Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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Keywords: BDS test; neural networks; heteroscedasticity; deterministic systems;

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  1. repec:att:wimass:9520 is not listed on IDEAS
  2. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-68, July.
  3. Bjorn Hansson & Peter Hordahl, 2005. "Forecasting variance using stochastic volatility and GARCH," The European Journal of Finance, Taylor & Francis Journals, vol. 11(1), pages 33-57.
  4. Poon, Ser-Huang & Taylor, Stephen J., 1992. "Stock returns and volatility: An empirical study of the UK stock market," Journal of Banking & Finance, Elsevier, vol. 16(1), pages 37-59, February.
  5. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  6. Paula L. Varson & Paul Doran, 1995. "The search for evidence of chaos in FTSE-100 daily returns," European Financial Management, European Financial Management Association, vol. 1(2), pages 201-210.
  7. Baillie, R.T. & Degennaro, R.P., 1988. "Stock Returns And Volatility," Papers 8803, Michigan State - Econometrics and Economic Theory.
  8. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July.
  9. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
  10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  11. Hsieh, David A, 1991. " Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-77, December.
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