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Testing for Non-Linear Structure in an Artificial Financial Market

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Author Info
Shu-Heng Chen
Thomas Lux
Michele Marchesi

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Abstract

We present a stochastic simulation model of a prototype financial market. Our market is populated by both noise traders and fundamentalist speculators. The dynamics covers switches in the prevailing mood among noise traders (optimistic or pessimistic) as well as switches of agents between the noise traders and fundamentalist group in response to observed differences in profits. The particular behavioral variant adopted by an agent also determines her decision to enter on the long or the short side of the market. Short-run imbalances between demand and supply lead to price adjustments by a market maker or auctioneer in the usual Walrasian manner. Our interest in this paper is in exploring the behavior of the model when testing for the presence of chaos or non-linearity in the simulated data. First, attempts to determine the fractal dimension of the underlying process give unsatisfactory results in that we experience a lack of convergence of the estimate. Explicit tests for non-linearity and dependence (the BDS and Kaplan tests) also give very unstable results in that both acceptance and strong rejection of IIDness can be found in different realizations of our model. All in all, this behavior is very similar to experience collected with empirical data and our results may point towards an explanation of why robustness of inference in this area is low. However, when testing for dependence in second moments and estimating GARCH models, the results appear much more robust and the chosen GARCH specification closely resembles the typical outcome of empirical studies.

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Publisher Info
Paper provided by University of Bonn, Germany in its series Discussion Paper Serie B with number 447.

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Date of creation: Feb 1999
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Handle: RePEc:bon:bonsfb:447

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Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
Fax: +49 228 73 9221
Web page: http://www.bgse.uni-bonn.de/index.php?id=517

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Related research
Keywords: artificial financial market; chaos; non-linearity; ARCH models;

Other versions of this item:

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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  1. Xue-Zhong He & Youwei Li, 2005. "Long Memory, Heterogeneity and Trend Chasing," Research Paper Series 148, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]
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  2. Boer-Sorban, K. & Bruin, A. de & Kaymak, U., 2005. "On the Design of Artificial Stock Markets," Research Paper ERS-2005-001-LIS Revision, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni. [Downloadable!]
  3. Li, Youwei & Donkers, Bas & Melenberg, Bertrand, 2006. "The econometric analysis of microscopic simulation models," Discussion Paper 99, Tilburg University, Center for Economic Research. [Downloadable!]
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  4. Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2007. "Asset price dynamics with small world interactions under hetereogeneous beliefs," Working Papers 149, Department of Applied Mathematics, University of Venice. [Downloadable!]
  5. Boer-Sorban, K. & Kaymak, U. & Bruin, A. de, 2005. "A Modular Agent-Based Environment for Studying Stock Markets," Research Paper ERS-2005-017-LIS Revision, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni. [Downloadable!]
  6. 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!]
  7. Norman Ehrentreich, 2002. "The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections," Computational Economics 0209001, EconWPA. [Downloadable!]
  8. Lux, Thomas, 2006. "Financial power laws : empirical evidence, models, and mechanism," Economics Working Papers 2006,12, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
  9. Sergiy Gerasymchuk, 2008. "Asset return and wealth dynamics with reference dependent preferences and heterogeneous beliefs," Working Papers 160, Department of Applied Mathematics, University of Venice. [Downloadable!]
  10. Henrik Amilon, 2003. "Estimation of an Adaptive Stock Market Model with Heterogeneous Agents," Research Paper Series 107, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]
  11. Lux, Thomas & Schornstein, Sascha, 2003. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Economics Working Papers 2003,12, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
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  12. Shu-Heng Chen & Chia-Hsuan Yeh, 1999. "Evolving Traders and the Faculty of the Business School: A New Architecture of the Artificial Stock Market," Computing in Economics and Finance 1999 613, Society for Computational Economics. [Downloadable!]
  13. 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)
  14. Alfarano, Simone & Lux, Thomas, 2005. "A noise trader model as a generator of apparent financial power laws and long memory," Economics Working Papers 2005,13, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
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  15. 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!]
  16. Alfarano, Simone & Lux, Thomas, 2003. "A minimal noise trader model with realistic time series properties," Economics Working Papers 2003,15, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
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  17. Catherine Kyrtsou & Michel Terraza, 2000. "Is It Possible To Study Jointly Chaotic And Arch Behaviour? Application Of A Noisy Mackey-Glass Equation With Heteroskedastic Errors To The Paris Stock Exchange," Computing in Economics and Finance 2000 Z226, Society for Computational Economics. [Downloadable!]
  18. Andrea Morone, 2004. "Financial Market in the Laboratory," Experimental 0401002, EconWPA. [Downloadable!]
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  19. Amilon, Henrik, 2005. "Estimation of an Adaptive Stock Market Model with Heterogeneous Agents," Working Paper Series 177, Sveriges Riksbank (Central Bank of Sweden). [Downloadable!]
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