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Nonlinear modelling of the Finnish Banking and Finance branch index

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  • Ralf Ostermark
  • Jaana Aaltonen
  • Henrik Saxen
  • Kenneth Soderlund
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    Abstract

    It is well documented that daily returns of several financial assets cannot be modelled by pure linear processes. It seems to be generally accepted that many economic variables follow nonlinear processes. The sources of nonlinearity can be divided in two classes: those where nonlinearities stem from the conditional variance and those where non-linearities enter through the conditional mean. Efforts in modelling the former have resulted in development of the ARCH-family models. There is, however, less evidence on nonlinearity in the mean of financial time series. One family of models that is applied in finance is the STAR. In this paper some nonlinear modelling techniques are applied to a Finnish financial time series, the daily Banking and Finance branch index on the Helsinki Stock Exchange. The techniques include a variance-nonlinear model from the ARCH family, a mean-nonlinear model, namely Smooth Transition Autoregression (STAR)-model and a neural network. Linearity is tested for by standard autocorrelation tests, LM-tests against the specific nonlinear models and the BDS-test. The study provides supplements to a range of earlier research. It demonstrates that the stock series is both linearly and nonlinearly dependent. Adapting an ARCH(3) eliminates the dependencies most satisfactorily. The ARCH-models and STAR-models were estimated using the SHAZAM-package.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

    Volume (Year): 10 (2004)
    Issue (Month): 4 ()
    Pages: 277-289

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    Handle: RePEc:taf:eurjfi:v:10:y:2004:i:4:p:277-289

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    Related research

    Keywords: nonlinear time series; variance-nonlinearity; mean-nonlinearity;

    References

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    1. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    2. 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.
    3. Granger, Clive W J, 1986. "Developments in the Study of Cointegrated Economic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 213-28, August.
    4. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    5. Booth, G. Geoffrey & Martikainen, Teppo & Tse, Yiuman, 1997. "Price and volatility spillovers in Scandinavian stock markets," Journal of Banking & Finance, Elsevier, vol. 21(6), pages 811-823, June.
    6. Koutmos, Gregory & Booth, G Geoffrey, 1995. "Asymmetric volatility transmission in international stock markets," Journal of International Money and Finance, Elsevier, vol. 14(6), pages 747-762, December.
    7. Stengos, Thanasis & Panas, E, 1992. "Testing the Efficiency of the Athens Stock Exchange: Some Results from the Banking Sector," Empirical Economics, Springer, vol. 17(2), pages 239-52.
    8. repec:att:wimass:9520 is not listed on IDEAS
    9. Geoffrey Booth, G. & Hatem, John & Virtanen, Ilkka & Yli-Olli, Paavo, 1992. "Stochastic modeling of security returns: Evidence from the Helsinki Stock Exchange," European Journal of Operational Research, Elsevier, vol. 56(1), pages 98-106, January.
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