This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Testing for Non-Linear Structure in an Artificial Financial Market

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Shu-Heng Chen
Thomas Lux
Michele Marchesi

Additional information is available for the following registered author(s):

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.

Download Info
To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

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

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Length: pages
Date of creation: Feb 1999
Date of revision:
Handle: RePEc:bon:bonsfb:447

Contact details of provider:
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

For technical questions regarding this item, or to correct its listing, contact: (Daniel Park).

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

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, 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!]
  2. 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!]
    Other versions:
  3. 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!]
    Other versions:
  4. Norman Ehrentreich, 2002. "The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections," Computational Economics 0209001, EconWPA. [Downloadable!]
  5. 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!]
  6. 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!]
  7. Alfarano, Simone & Lux, Thomas, 2006. "A minimal noise trader model with realistic time series properties," Economics working papers 2006,11, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
    Other versions:
  8. 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!]
    Other versions:
  9. Andrea Morone, 2004. "Financial Market in the Laboratory," Experimental 0401002, EconWPA. [Downloadable!]
    Other versions:
  10. 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!]
  11. Li, Youwei & Donkers, Bas & Melenberg, Bertrand, 2006. "The econometric analysis of microscopic simulation models," Discussion Paper 99, Tilburg University, Center for Economic Research. [Downloadable!]
    Other versions:
  12. 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!]
  13. 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!]
  14. Amilon, Henrik, 2005. "Estimation of an Adaptive Stock Market Model with Heterogeneous Agents," Working Paper Series 177, Sveriges Riksbank (Central Bank of Sweden). [Downloadable!]
Statistics
Access and download statistics

Did you know? Citation analysis on IDEAS includes online papers that are freely accessible and whose text could be automatically analyzed, currently about 150000 papers.

This page was last updated on 2008-7-22.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.