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Agent-based simulation of a financial market

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Author Info

  • Raberto, Marco
  • Cincotti, Silvano
  • Focardi, Sergio M.
  • Marchesi, Michele

Abstract

This paper introduces an agent-based artificial financial market in which heterogeneous agents trade one single asset through a realistic trading mechanism for price formation. Agents are initially endowed with a finite amount of cash and a given finite portfolio of assets. There is no money-creation process; the total available cash is conserved in time. In each period, agents make random buy and sell decisions that are constrained by available resources, subject to clustering, and dependent on the volatility of previous periods. The model proposed herein is able to reproduce the leptokurtic shape of the probability density of log price returns and the clustering of volatility. Implemented using extreme programming and object-oriented technology, the simulator is a flexible computational experimental facility that can find applications in both academic and industrial research projects.

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File URL: http://www.sciencedirect.com/science/article/pii/S0378437101003120
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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 299 (2001)
Issue (Month): 1 ()
Pages: 319-327

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Handle: RePEc:eee:phsmap:v:299:y:2001:i:1:p:319-327

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Artificial financial markets; Heterogeneous agents; Financial time series; Econophysics;

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Cited by:
  1. Kirchler, Michael & Huber, Jurgen, 2007. "Fat tails and volatility clustering in experimental asset markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1844-1874, June.
  2. Boer-Sorban, K. & Bruin, A. de & Kaymak, U., 2005. "On the Design of Artificial Stock Markets," Research Paper ERS-2005-001-LIS, 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.
  3. Marco Raberto & Silvano Cincott & Sergio M. Focardi & Michele Marchesi, 2002. "Traders’ long-run wealth in an artificial financial market," Computing in Economics and Finance 2002 301, Society for Computational Economics.
  4. Carl Chiarella & Giulia Iori, 2005. "The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows," Research Paper Series 152, Quantitative Finance Research Centre, University of Technology, Sydney.
  5. Derveeuw, Julien & Beaufils, Bruno & Mathieu, Philippe & Brandouy, Olivier, 2007. "Testing double auction as a component within a generic market model architecture," MPRA Paper 4918, University Library of Munich, Germany.
  6. Sabrina Ecca & Michele Marchesi & Alessio Setzu, 2008. "Modeling and Simulation of an Artificial Stock Option Market," Computational Economics, Society for Computational Economics, vol. 32(1), pages 37-53, September.
  7. 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, Society for Computational Economics, vol. 32(1), pages 147-162, September.
  8. Ribin Lye & James Peng Lung Tan & Siew Ann Cheong, 2012. "Understanding agent-based models of financial markets: a bottom-up approach based on order parameters and phase diagrams," Papers 1202.0606, arXiv.org.
  9. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics: agent-based models," Post-Print hal-00621059, HAL.
  10. Boer-Sorban, K. & Kaymak, U. & Bruin, A. de, 2005. "A Modular Agent-Based Environment for Studying Stock Markets," Research Paper ERS-2005-017-LIS, 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.
  11. A. Zaccaria & M. Cristelli & V. Alfi & F. Ciulla & L. Pietronero, 2009. "Asymmetric statistics of order books: The role of discreteness and evidence for strategic order placement," Papers 0906.1387, arXiv.org, revised May 2010.
  12. Marco Bartolozzi, 2010. "A Multi Agent Model for the Limit Order Book Dynamics," Papers 1005.0182, arXiv.org, revised Oct 2010.
  13. Erika Corona & Sabrina Ecca & Michele Marchesi & Alessio Setzu, 2008. "The Interplay Between Two Stock Markets and a Related Foreign Exchange Market: A Simulation Approach," Computational Economics, Society for Computational Economics, vol. 32(1), pages 99-119, September.
  14. Boer-Sorban, K. & Kaymak, U. & Spiering, J., 2006. "From Discrete-Time Models to Continuous-Time, Asynchronous Models of Financial Markets," Research Paper ERS-2006-009-LIS, 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.
  15. Mannaro, Katiuscia & Marchesi, Michele & Setzu, Alessio, 2008. "Using an artificial financial market for assessing the impact of Tobin-like transaction taxes," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 445-462, August.

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