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The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies

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  • Frank H. Westerhoff

    ()
    (Universitaet Bamberg)

Abstract

Models with heterogeneous interacting agents have proven to be quite successful in the past. For instance, such models are able to mimic the dynamics of financial markets quite well. The goal of our paper is to explore whether this approach may offer new insights into the working of certain regulatory policies such as transaction taxes, central bank interventions and trading halts. Although this strand of research is rather novel, we argue that agent-based models may be used as artificial laboratories to improve our understanding of how regulatory policy tools function.

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Bibliographic Info

Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

Volume (Year): 228 (2008)
Issue (Month): 2+3 (June)
Pages: 195-227

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Handle: RePEc:jns:jbstat:v:228:y:2008:i:2-3:p:195-227

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Related research

Keywords: Financial markets; technical and fundamental analysis; transaction taxes; central bank interventions; trading halts;

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References

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Citations

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Cited by:
  1. Neuberger, Doris & Rissi, Roger, 2012. "Macroprudential banking regulation: Does one size fit all?," Thuenen-Series of Applied Economic Theory 124, University of Rostock, Institute of Economics.
  2. Anufriev, Mikhail & Tuinstra, Jan, 2013. "The impact of short-selling constraints on financial market stability in a heterogeneous agents model," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1523-1543.
  3. 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.
  4. Riccetti, Luca & Russo, Alberto & Mauro, Gallegati, 2013. "Financial Regulation in an Agent Based Macroeconomic Model," MPRA Paper 51013, University Library of Munich, Germany.
  5. Lengnick, Matthias & Wohltmann, Hans-Werner, 2010. "Agent-based financial markets and New Keynesian macroeconomics: A synthesis," Economics Working Papers 2010,10, Christian-Albrechts-University of Kiel, Department of Economics.
  6. Sacht, Stephen & Jang, Tae-Seok, 2012. "Identification of Animal Spirits in a Bounded Rationality Model: An Application to the Euro Area," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62071, Verein für Socialpolitik / German Economic Association.
  7. Daniel Fricke & Thomas Lux, 2013. "The Effects of a Financial Transaction Tax in an Artificial Financial Market," Kiel Working Papers 1868, Kiel Institute for the World Economy.
  8. Fabio Tramontana & Frank Westerhoff & Laura Gardini, 2014. "One-dimensional maps with two discontinuity points and three linear branches: mathematical lessons for understanding the dynamics of financial markets," Decisions in Economics and Finance, Springer, vol. 37(1), pages 27-51, April.
  9. Poledna, Sebastian & Thurner, Stefan & Farmer, J. Doyne & Geanakoplos, John, 2014. "Leverage-induced systemic risk under Basle II and other credit risk policies," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 199-212.
  10. Blake LeBaron & Peter Winker, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 141-148, June.
  11. Zhu, Mei & Chiarella, Carl & He, Xue-Zhong & Wang, Duo, 2009. "Does the market maker stabilize the market?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(15), pages 3164-3180.
  12. Fischer, Thomas, 2011. "News Reaction in Financial Markets within a Behavioral Finance Model with Heterogeneous Agents," Darmstadt Discussion Papers in Economics 54196, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute of Economics (VWL).
  13. Witte, Björn-Christopher, 2013. "Fundamental traders' ‘tragedy of the commons’: Information costs and other determinants for the survival of experts and noise traders in financial markets," Economic Modelling, Elsevier, vol. 32(C), pages 377-385.
  14. Hermsen, Oliver & Witte, Björn-Christopher & Westerhoff, Frank, 2010. "Disclosure requirements, the release of new information and market efficiency: new insights from agent-based models," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 4(7), pages 1-26.
  15. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
  16. Hai-Chuan Xu & Wei Zhang & Xiong Xiong & Wei-Xing Zhou, 2014. "An agent-based computational model for China's stock market and stock index futures market," Papers 1404.1052, arXiv.org.
  17. Timm Gudehus, 2010. "Logik des Marktes, Marktordnung, Marktverhalten und Marktergebnisse," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 230(5), pages 601-629, October.

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