IDEAS home Printed from https://ideas.repec.org/p/zbw/cauewp/7368.html
   My bibliography  Save this paper

Artificial Long Memory Effects in Two Agend-Based Asset Pricing Models

Author

Listed:
  • Franke, Reiner

Abstract

This note is concerned with two recent agent-based models of speculative dynamics from the literature, one by Gaunersdorfer and Hommes and the other by He and Li. At short as well as long lags, both of them display an autocorrelation structure in absolute and squared returns that comes remarkably close to that of real data at a daily frequency. The note argues that these long memory effects are to be ascribed to the stochastic specification of the price equation, which given the wide fluctuations in these models unduly fails to normalize the price shocks. Under an appropriate respecification, the long memory completely disappears.

Suggested Citation

  • Franke, Reiner, 2008. "Artificial Long Memory Effects in Two Agend-Based Asset Pricing Models," Economics Working Papers 2008-15, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:7368
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/22059/1/EWP-2008-15.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrea Gaunersdorfer & Cars Hommes, 2007. "A Nonlinear Structural Model for Volatility Clustering," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 265-288, Springer.
    2. Beja, Avraham & Goldman, M Barry, 1980. "On the Dynamic Behavior of Prices in Disequilibrium," Journal of Finance, American Finance Association, vol. 35(2), pages 235-248, May.
    3. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    4. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.
    5. Gilles Teyssière & Alan P. Kirman (ed.), 2007. "Long Memory in Economics," Springer Books, Springer, number 978-3-540-34625-8, March.
    6. Manzan, Sebastiano & Westerhoff, Frank, 2005. "Representativeness of news and exchange rate dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 677-689, April.
    7. Franke, Reiner, 2008. "On the Interpretation of Price Adjustments and Demand in Asset Pricing Models with Mean-Variance Optimization," Economics Working Papers 2008-13, Christian-Albrechts-University of Kiel, Department of Economics.
    8. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, 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. Youwei Li & Bas Donkers & Bertrand Melenberg, 2010. "Econometric analysis of microscopic simulation models," Quantitative Finance, Taylor & Francis Journals, vol. 10(10), pages 1187-1201.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Franke, Reiner, 2010. "On the specification of noise in two agent-based asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1140-1152, June.
    2. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    3. Zheng, Min & Liu, Ruipeng & Li, Youwei, 2018. "Long memory in financial markets: A heterogeneous agent model perspective," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 38-51.
    4. He, Xue-Zhong & Li, Kai, 2012. "Heterogeneous beliefs and adaptive behaviour in a continuous-time asset price model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(7), pages 973-987.
    5. He, Xue-Zhong & Li, Youwei & Zheng, Min, 2019. "Heterogeneous agent models in financial markets: A nonlinear dynamics approach," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 135-149.
    6. 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.
    7. Xue-Zhong He, 2012. "Recent Developments on Heterogeneous Beliefs and Adaptive Behaviour of Financial Markets," Research Paper Series 316, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. Zhao, Zhijun & Zhang, Xiaoqi, 2022. "A continuous heterogeneous-agent model for the co-evolution of asset price and wealth distribution in financial market," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    9. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy (IfW Kiel).
    10. Schmitt, Noemi & Westerhoff, Frank, 2014. "Speculative behavior and the dynamics of interacting stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 262-288.
    11. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2011. "Do heterogeneous beliefs diversify market risk?," The European Journal of Finance, Taylor & Francis Journals, vol. 17(3), pages 241-258.
    12. Xue-Zhong He & Youwei Li, 2017. "The adaptiveness in stock markets: testing the stylized facts in the DAX 30," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1071-1094, November.
    13. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2013. "Time-varying beta: a boundedly rational equilibrium approach," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 609-639, July.
    14. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    15. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    16. Dieci, Roberto & Westerhoff, Frank, 2010. "Heterogeneous speculators, endogenous fluctuations and interacting markets: A model of stock prices and exchange rates," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 743-764, April.
    17. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    18. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    19. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2011. "The dynamic behaviour of asset prices in disequilibrium: a survey," International Journal of Behavioural Accounting and Finance, Inderscience Enterprises Ltd, vol. 2(2), pages 101-139.
    20. Kai Li, 2014. "Asset Price Dynamics with Heterogeneous Beliefs and Time Delays," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 13, July-Dece.

    More about this item

    Keywords

    Volatility clustering; Autocorrelations of returns; Fundamentalists and trendfollowers;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

    Access and download statistics

    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:zbw:cauewp:7368. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vakiede.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.