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Nonlinear modelling of the Finnish Banking and Finance branch index

Author

Listed:
  • Ralf Ostermark
  • Jaana Aaltonen
  • Henrik Saxen
  • Kenneth Soderlund

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.

Suggested Citation

  • Ralf Ostermark & Jaana Aaltonen & Henrik Saxen & Kenneth Soderlund, 2004. "Nonlinear modelling of the Finnish Banking and Finance branch index," The European Journal of Finance, Taylor & Francis Journals, vol. 10(4), pages 277-289.
  • Handle: RePEc:taf:eurjfi:v:10:y:2004:i:4:p:277-289
    DOI: 10.1080/13518470210124641
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    References listed on IDEAS

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    1. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    2. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-337, July.
    3. Brock, W.A. & Dechert, W.D. & LeBaron, B. & Scheinkman, J.A., 1995. "A Test for Independence Based on the Correlation Dimension," Working papers 9520, Wisconsin Madison - Social Systems.
    4. 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.
    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. 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 239-252.
    7. 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 213-228, August.
    8. 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.
    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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    Cited by:

    1. Melike Bildirici & Özgür Ömer Ersin, 2014. "Nonlinearity, Volatility and Fractional Integration in Daily Oil Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 108-135, October.

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