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Surrogate Data Analysis and Stochastic Chaotic Modelling: Application to Stock Exchange Returns Series

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Author Info
Constantinos VORLOW
Antonios ANTONIOU
Catherine KYRTSOU

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Abstract

We investigate for evidence of complex-deterministic dynamics in financial returns time series. By combining the Surrogate Data Analysis inferential framework with the MG-GARCH (Kyrtsou and Terraza, 2003) modelling approach, we examine whether the sequences are characterized by aperiodic and nonlinear deterministic cycles or pure randomness. Our results support the hypothesis of complex nonlinear and non-stochastic dynamics in the data generating processes. According to our approach, markets can be assumed to be highly complex, high-dimensional, open and dissipative dynamical systems that need feedback as well as other kinds of inputs in order to operate. These inputs may come in the guise of noise or news. The inputs may also control the evolution of the system dynamics and the knowledge of their nature may allow us to forecast the future states of the market with greater accuracy. To this extent the MG-GARCH model provides a valuable insight on how a feedback mechanism can operate within the structure of stock returns processes and explain stylized facts.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 27.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:27

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Related research
Keywords: MG-GARCH; Surrogate Data Analysis; Chaos; Complexity;

Find related papers by JEL classification:
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies
D40 - Microeconomics - - Market Structure and Pricing - - - General
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General

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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, 2003. "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," Computational Economics, Springer, vol. 21(3), pages 257-276, June. [Downloadable!] (restricted)
  2. Gaunersdorfer, A. & Hommes, C.H., 2000. "A Nonlinear Structural Model for Volatility Clustering," CeNDEF Working Papers 00-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
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  3. 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)
  4. repec:att:wimass:192017 is not listed on IDEAS
  5. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2001. "Evolutionary Dynamics in Financial Markets With Many Trader Types," CeNDEF Working Papers 01-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
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  6. Gaunersdorfer, Andrea, 2000. "Endogenous fluctuations in a simple asset pricing model with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 799-831, June. [Downloadable!] (restricted)
  7. Giulia Iori, 1999. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Finance 9905005, EconWPA. [Downloadable!]
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  8. 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.
  9. Andrea Gaunersdorfer & Cars Hommes & Florian Wagener, 2001. "Adaptive Beliefs and the volatility of asset prices," CeNDEF Workshop Papers, January 2001 5A.1, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. [Downloadable!]
  10. Hommes, C.H., . "Financial Markets as Nonlinear Adaptive Evolutionary Systems," CeNDEF Working Papers 00-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
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  11. Chung-Ming Kuan & Halbert White, 1992. "Artificial Neural Networks: An Econometric Perspective," University of California at San Diego, Economics Working Paper Series 92-11, Department of Economics, UC San Diego.
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  12. Chen, Shu-Heng & Lux, Thomas & Marchesi, Michele, 2001. "Testing for non-linear structure in an artificial financial market," Journal of Economic Behavior & Organization, Elsevier, vol. 46(3), pages 327-342, November. [Downloadable!] (restricted)
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  13. Catherine Kyrtsou & Walter C. Labys & Michel Terraza, 2004. "Noisy chaotic dynamics in commodity markets," Empirical Economics, Springer, vol. 29(3), pages 489-502, 09. [Downloadable!] (restricted)
  14. 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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