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Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series

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Author Info
Catherine Kyrtsou
Michel Terraza

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Abstract

Most recent empirical works that apply sophisticated statistical proceduressuch as a correlation-dimension method have shown that stock returns arehighly complex. The estimated correlation dimension is high and there islittle evidence of low-dimensional deterministic chaos. Taking the complexbehaviour in stock markets into account, we think it is more robust than thetraditional stochastic approach to model the observed data by a nonlinearchaotic model disturbed by dynamic noise. In fact, we construct a model havingnegligible or even zero autocorrelations in the conditional mean, but a richstructure in the conditional variance. The model is a noisy Mackey–Glassequation with errors that follow a GARCH(p,q) process. This model permits usto capture volatility-clustering phenomena. Its characteristic is thatvolatility clustering is interpreted as an endogenous phenomenon. The mainobjective of this article is the identification of the underlying process ofthe Paris Stock Exchange returns series CAC40. To this end, we apply severaldifferent tests to detect longmemory components and chaotic structures.Forecasting results for the CAC40 returns series, will conclude this paper. Copyright Kluwer Academic Publishers 2003

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Publisher Info
Article provided by Springer in its journal Computational Economics.

Volume (Year): 21 (2003)
Issue (Month): 3 (June)
Pages: 257-276
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Handle: RePEc:kap:compec:v:21:y:2003:i:3:p:257-276

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Web page: http://www.springerlink.com/link.asp?id=100248

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Related research
Keywords: Mackey–Glass equation; noisy chaos; volatility clustering; correlation dimension; Lyapunov exponents; GARCH effects; forecasting;

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References listed on IDEAS
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  1. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August. [Downloadable!] (restricted)
  2. Xue-Zhong He & Carl Chiarella, 1999. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset-Pricing Model," Computing in Economics and Finance 1999 223, Society for Computational Economics. [Downloadable!]
    Other versions:
  3. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," Journal of Business, University of Chicago Press, vol. 62(3), pages 339-68, July. [Downloadable!] (restricted)
  4. Kyrtsou, Catherine & Terraza, Michel, 2002. "Stochastic chaos or ARCH effects in stock series?: A comparative study," International Review of Financial Analysis, Elsevier, vol. 11(4), pages 407-431. [Downloadable!] (restricted)
  5. repec:att:wimass:199520 is not listed on IDEAS
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  11. Carl Chiarella & Roberto Dieci & Laura Gardini, 2001. "Speculative Behaviour and Complex Asset Price Dynamics," Research Paper Series 49, Quantitative Finance Research Centre, University of Technology, Sydney.
  12. Bruce Mizrach, 1996. "Learning and Conditional Heteroscedasticity in Asset Returns," Departmental Working Papers 199526, Rutgers University, Department of Economics.
  13. Shu-Heng Chen & Thomas Lux & Michele Marchesi, 1999. "Testing for Non-Linear Structure in an Artificial Financial Market," Discussion Paper Serie B 447, University of Bonn, Germany.
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  14. Chung-Ming Kuan & Halbert White, 1994. "Artificial neural networks: an econometric perspective," Econometric Reviews, Taylor and Francis Journals, vol. 13(1), pages 1-91. [Downloadable!] (restricted)
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  15. 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. [Downloadable!] (restricted)
  16. Malliaris, A. G. & Stein, Jerome L., 1999. "Methodological issues in asset pricing: Random walk or chaotic dynamics," Journal of Banking & Finance, Elsevier, vol. 23(11), pages 1605-1635, November. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Catherine Kyrtsou & Michel Terraza, 2008. "Seasonal Mackey-Glass-GARCH process and short-term dynamics," Discussion Paper Series 2008_09, Department of Economics, University of Macedonia, revised Sep 2008. [Downloadable!]
  2. Constantinos VORLOW & Antonios ANTONIOU & Catherine KYRTSOU, 2004. "Surrogate Data Analysis and Stochastic Chaotic Modelling: Application to Stock Exchange Returns Series," Computing in Economics and Finance 2004 27, Society for Computational Economics. [Downloadable!]
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