IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2003-5-2.html
   My bibliography  Save this article

Simulation and Validation of an Integrated Markets Model

Author

Listed:

Abstract

The behavior of boundedly rational agents in two interacting markets is investigated. A discrete-time model of coupled financial and consumer markets is described. The integrated model consists of heterogenous consumers, financial traders, and production firms. The production firms operate in the consumer market, and offer their shares to be traded on the financial market. The model is validated by comparing its output to known empirical properties of real markets. In order to better explore the influence of model parameters on behavior, a novel Markov chain Monte Carlo method is introduced. This method allows for the efficient exploration of large parameter spaces, in order to find which parameter regimes lead to reproduction of empirical phenomena. It is shown that the integrated markets model can reproduce a number of empirical ``stylized facts'', including learning-by-doing effects, fundamental price effects, low autocorrelations, volatility clustering, high kurtosis, and volatility-volume correlations.

Suggested Citation

  • Brian Sallans & Alexander Pfister & Alexandros Karatzoglou & Georg Dorffner, 2003. "Simulation and Validation of an Integrated Markets Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(4), pages 1-2.
  • Handle: RePEc:jas:jasssj:2003-5-2
    as

    Download full text from publisher

    File URL: http://jasss.soc.surrey.ac.uk/6/4/2.html
    Download Restriction: no

    References listed on IDEAS

    as
    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.
    2. C. Chiarella & X-Z. He, 2001. "Asset price and wealth dynamics under heterogeneous expectations," Quantitative Finance, Taylor & Francis Journals, vol. 1(5), pages 509-526.
    3. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    4. Chiarella, Carl & He, Xue-Zhong, 2002. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 95-132, February.
    5. Sanford Grossman, 1989. "The Informational Role of Prices," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262572141, January.
    6. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    7. Martin Natter & Andreas Mild & Markus Feurstein & Georg Dorffner & Alfred Taudes, 2001. "The Effect of Incentive Schemes and Organizational Arrangements on the New Product Development Process," Management Science, INFORMS, vol. 47(8), pages 1029-1045, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Witte, Björn-Christopher, 2011. "Removing systematic patterns in returns in a financial market model by artificially intelligent traders," BERG Working Paper Series 82, Bamberg University, Bamberg Economic Research Group.
    2. Stuart Rossiter & Jason Noble & Keith R.W. Bell, 2010. "Social Simulations: Improving Interdisciplinary Understanding of Scientific Positioning and Validity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-10.
    3. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 47-78, February.
    4. László Gerencsér & Zalán Mátyás, 2008. "A behavioral stock market model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 67(1), pages 43-63, February.
    5. Marco Raberto & Andrea Teglio & Silvano Cincotti, 2008. "Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 147-162, September.
    6. repec:spr:compst:v:67:y:2008:i:1:p:43-63 is not listed on IDEAS

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2003-5-2. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Flaminio Squazzoni). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.