Document Type : Research Paper

Authors

Abstract

Simulation has been increasingly applied in social sciences and economics in the two last decades. Agent Based Simulation (ABS) provides the opportunity of creation of an artificial environment for many agents to have interaction in a computer. In this paper, concerning ABS literature and the new characteristics, Tehran Stock Exchange has been simulated. Primary tests show that this model is capable of reproducing the existing statistical identifications in time-series of prices and returns in International and Tehran stock markets.

1. Bak P., Pazuski M. and Shubik M. "Price Variations in a Stock Market with Many Agents"., Santa Fe Institute Working Paper, No. 96-09-075, (1996).
 
2. Bornholdt, S. "Expectation Bubbles in a Spin Model of Markets: Intermittency from Frustration Across Scales"., in: International Journal of Modern Physics C, Vol. 12, No. 5, (2001): 667-674.
 
3. Caldarelli G., Marsili M. and Zhang Y.-C. "A Prototype Model of Stock Exchange"., in: Europhisics Letters, Vol. 40, No. 5, (1997): 479- 484.
 
4. Chen S.-H., and Yeh C.-H. "Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Market"., in: Journal of Economic Dynamics and Control, Vol. 25, (2001): 363-393.
 
5. De la Maza M. and Yuret D. "A Model of Stock Market Partici-Pants"., in: Eds. J. Biethahn and V. Nissen, Evolutionary Algo-rithmsin Management Applications, Springer Verlag, Heidelberg, (1995): 290-304.
 
6. Farmer, J. D. "Market Force, Ecology, and Evolution"., Santa Fe Institute Working Paper, 98-12-116, (1998).
 
7. Franses P.H. and Dijk D. Van. Non Linear Time Series Models in Empirical Finance. Cambridge University Press., 2001.
 
8. Gode D. K. and Sunder, S. "Allocative Efficiency of Markets with Zero Intelligence Traders"., Journal of Political Economy, No. 101, (1993): 119-137.
 
9. Joshi S., Parker J. and Bedau M. A. Technical Trading Creates a Prisoner’s Dilemma: Results from an Agent-based Model. in: Proceedings of the 6th International Conference Computational Finance, Eds. Y. Abu-Mostsfa, B. LeBaron, A. W. Lo and A. S. Weigend, MIT Press., 2000.
 
10. LeBaron B. "A Builder’s Guide to Agent Based Financial Markets"., Quantitative Finance, Vol. 1, No.2, (2001): 245-261.
 
11. Lux T., Chen S. H. and Marchesi M. "Testing for Nonlinear Structure in an Artificial Financial Market"., Journal of Economic Behavior and Organization, Vol. 46, (2001): 327- 342.
 
12. Lux T. and Marchesi M. "Volatility Clustering in Financial Markets: A Micro-Simulation of Interacting Agents"., Journal of Theoretical and Applied Finance, No. 3, (2001): 675-702.
13. Lux, T. "The Socio-Economic Dynamics of Speculative Markets: Interacting Agents, Chaos, and the Fat Tail of Return Distributions"., Journal of Economic Behavior and Organization, Vol. 33, (1998): 143-165.
 
14. Marchesi M., Cincotti S., Focardi S. and Raberto M. Development and Testing of an Artificial Stock Market. MDEF 2000: Urbino, September 2000, pp. 28-30.
http://www.econ.uniurb.it/bischi/mdef2000/marchesimdef.pdf
 
15. Murphy, J. J. Intermarket Technical Analysis: Trading Strategies for the Global Stock, Bond, Commodity and Currency Markets, Wiley Finance, New York., 1991.
 
16. Raberto M., Cincotti S., Focardi S. M and Marchesi M. "Traders’ Long-Run Wealth in an Artificial Financial Market"., Computational Economics, No. 22, (2003): 255-272.
 
17. Shiller R. J. "Human Behavior and the Efficiency of the Financial System"., Handbook of Macroeconomics, Vol. 1, (1997): 1305-40.
 
18. Tesfatsion L. Agent-Based Computational Economics: A Brief Guide to the Literature. Discussion Paper, Economics Dpt., Iowa State University, Jan. 2000, Reader’s Guide to the Social Sciences, Fitzroy-Dearborn, London., 2000.
 
19. Valente M., Program Codes,
www.business.aau.dk/~mv/Lsd/Lsd/ExampeModels/Economics/fin/body_report_fin.html.
 
20. Youssefmir M. and Huberman B. A. "Clustered Volatility in Multi-Agent Dynamics"., Working Paper, Santa Fe Institute, 95-05-051, (1995).
 
21. Zeigler B.P. and H.S. Sarioughian. "Approach and Techniques for Building Component Based Simulation Models"., Presentation, Arizona Center for Integrative Modelling and Simulation, (Dec, 2004).