Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-23T09:30:38.342Z Has data issue: false hasContentIssue false
  • English
  • Français

Agent-based economic models and econometrics

Published online by Cambridge University Press:  26 April 2012

Shu-Heng Chen ,
Chia-Ling Chang  and
Ye-Rong Du
Shu-Heng Chen*
Affiliation:
AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan; e-mail: chchen@nccu.edu.tw, cutesphinx6@yahoo.com.tw, yerong.du@gmail.com
Chia-Ling Chang*
Affiliation:
AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan; e-mail: chchen@nccu.edu.tw, cutesphinx6@yahoo.com.tw, yerong.du@gmail.com
Ye-Rong Du*
Affiliation:
AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan; e-mail: chchen@nccu.edu.tw, cutesphinx6@yahoo.com.tw, yerong.du@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, which is able to provide evidence of this three-stage development is finance or financial economics. We will, therefore, focus our review only on the literature of agent-based computational finance, or, more specifically, the agent-based modeling of financial markets.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Special Issue 2: Agent-Based Computational Economics , 26 April 2012 , pp. 187 - 219
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alfarano, S., Lux, T., Wagner, F. 2005. Estimation of agent-based models: the case of an asymmetric herding model. Computational Economics 26(1), 19–49.Google Scholar
Alfarano, S., Lux, T., Wagner, F. 2006. Estimation of a simple agent-based model of financial markets: an application to Australian stock and foreign exchange data. Physica A 370(1), 38–42.CrossRefGoogle Scholar
Alfarano, S., Lux, T., Wagner, F. 2007. Empirical validation of stochastic models of interacting agents: a ‘maximally skewed’ noise trader model. The European Physical Journal B 55(2), 183–187.CrossRefGoogle Scholar
Allen, H., Taylor, M. 1990. Charts, noise and fundamentals in the London foreign exchange market. Economic Journal 100, 49–59.Google Scholar
Amilon, H. 2008. Estimation of an adaptive stock market model with heterogeneous agents. Journal of Empirical Finance 15, 342–362.CrossRefGoogle Scholar
Anufriev, M., Panchenko, V. 2009. Asset prices, traders’ behavior, and market design. Journal of Economic Dynamics and Control 33, 1073–1090. Available as the CeNDEF Working paper 07-14, University of Amsterdam.CrossRefGoogle Scholar
Aoki, M. 2002. Open models of share markets with two dominant types of participants. Journal of Economic Behavior and Organization 49(2), 199–216.CrossRefGoogle Scholar
Arifovic, J., Gencay, R. 2000. Statistical properties of genetic learning in a model of exchange rate. Journal of Economic Dynamics and Control 24(5–7), 981–1005.CrossRefGoogle Scholar
Arifovic, J., Maschek, M. 2006. Revisiting individual evolutionary learning in the cobweb model—an illustration of the virtual spite-effect. Computational Economics 28(4), 333–354.CrossRefGoogle Scholar
Arthur, W., Holland, J., LeBaron, B., Palmer, R., Tayler, P. 1997. Asset pricing under endogenous expectations in an artificial stock market. In The Economy as an Evolving Complex System II, Arthur, B., Durlauf, S. & Lane, D. (eds). Addison-Wesley, 15–44.Google Scholar
Barber, B., Odean, T. 2000. Trading is hazardous to your wealth: the common stock investment performance of individual investors. Journal of Finance 55(2), 773–806.CrossRefGoogle Scholar
Barker, T., Pesaran, H. 1990 (eds). Disaggregation in Econometric Modeling. Routledge.Google Scholar
Blinder, A. 1983. A Skeptical Note on the New Econometrics. NBER Working Paper Series 1092.Google Scholar
Blume, L. 1993. The statistical mechanics of strategic interaction. Games and Economic Behavior 5(3), 387–424.CrossRefGoogle Scholar
Boswijk, P., Hommes, C., Manzan, S. 2007. Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control 31(6), 1938–1970.CrossRefGoogle Scholar
Bottazzi, G., Dosi, G., Rebesco, I. 2005. Institutional architectures and behavioral ecologies in the dynamics of financial markets. Journal of Mathematical Economics 41(1–2), 197–228.Google Scholar
Bovier, A., Cerny, J., Hryniv, O. 2006. The opinion game: stock price evolution from microscopic market modeling. International Journal of Theoretical and Applied Finance 9(1), 91–111.CrossRefGoogle Scholar
Brenner, T. 2006. Agent learning representation: advice on modelling economic learning. In Handbook of Computational Economics, Tesfatsion, L. & Judd, K. (eds). Elsevier, 881–947.Google Scholar
Brock, W., Hommes, C. 1997. A rational route to randomness. Econometrica 65, 1059–1095.CrossRefGoogle Scholar
Brock, W., Hommes, C. 1998. Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22(8–9), 1235–1274.CrossRefGoogle Scholar
Brock, W., Hommes, C., Wagener, F. 2005. Evolutionary dynamics in markets with many trader types. Journal of Mathematical Economics 41(1–2), 7–42.CrossRefGoogle Scholar
Casella, A., Thoron, S., Trannoy, A. 2007. Alan Kirman: a non-representative economist: introduction of the special issue of the journal of economic behavior and organization in honor of Alan Kirman. Journal of Economic Behavior and Organization 64(3–4), 269–274.CrossRefGoogle Scholar
Challet, D., Galla, T. 2005. Price return autocorrelation and predictability in agent-based models of financial markets. Quantitative Finance 5(6), 569–576.CrossRefGoogle Scholar
Challet, D., Zhang, Y.-C. 1997. Emergence of cooperation and organization in an evolutionary game. Physica A 246(3–4), 407–418.CrossRefGoogle Scholar
Chan, N., LeBaron, B., Lo, A., Poggio, T. 1999. Agent-based Models of Financial Markets: A Comparison with Experimental Markets. Unpublished working paper, MIT Artificial Markets Project. MIT.Google Scholar
Chen, S.-H. 2008. Computational intelligence in agent-based computational economics. In Computational Intelligence: A Compendium, Fulcher, J. & Jain, L. (eds). Springer, 517–594.CrossRefGoogle Scholar
Chen, S.-H., Liao, C.-C., Chou, P.-J. 2008. On the plausibility of sunspot equilibria: simulations based on agent-based artificial stock markets. Journal of Economic Interaction and Coordination 3(1), 25–41.CrossRefGoogle Scholar
Chen, S.-H., Yeh, C.-H. 2001. Evolving traders and the business school with genetic programming: a new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control 25(3–4), 363–393.CrossRefGoogle Scholar
Chen, S.-H., Huang, Y.-C., Wang, J.-F. 2008. Bounded Rationality and the Elasticity Puzzle: An Analysis of Agent-based Computational Consumption Capital Asset Pricing Models. Working paper, AI-ECON Research Center, National Chengchi University.Google Scholar
Chiarella, C., Dieci, R., Gardini, L. 2002. Speculative behaviour and complex asset price dynamics: a global analysis. Journal of Economic Behavior and Organization 49(2), 173–197.Google Scholar
Chiarella, C., He, X.-Z., Hommes, C. 2006. Moving average rules as a source of market instability. Physica A 370(1), 12–17.CrossRefGoogle Scholar
Cont, R. 2001. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance 1, 223–236.CrossRefGoogle Scholar
Cont, R. 2005. Volatility clustering in financial markets: empirical facts and agent-based models. In Long Memory in Economics, Teyssière, G. & Kirman, A. (eds). Springer, 289–310.Google Scholar
Cont, R., Bouchaud, Y.-P. 2000. Herd behavior and aggregate fluctuations in financial markets. Macroeconomic Dynamics 4(2), 170–196.Google Scholar
Cross, R., Grinfeld, M., Lamba, H., Seaman, T. 2007. Stylized facts from a threshold-based heterogeneous agent model. The European Physical Journal B 57(2), 213–218.Google Scholar
De Grauwe, P., Grimaldi, M. 2005a. The exchange rate and its fundamentals in a complex world. Review of International Economics 13(3), 549–575.CrossRefGoogle Scholar
De Grauwe, P., Grimaldi, M. 2005b. Heterogeneity of agents, transactions costs and the exchange rate. Journal of Economic Dynamics and Control 29(4), 691–719.CrossRefGoogle Scholar
De Grauwe, P., Grimaldi, M. 2006. The Exchange Rate in a Behavior Finance Framework. Princeton University Press.Google Scholar
de Jong, E., Verschoor, W., Zwinkels, R. 2006. Heterogeneity of Agents and Exchange Rate Dynamics: Evidence from the EMS. Working paper, Nijmegen Centre for Economics.CrossRefGoogle Scholar
de Jong, E., Verschoor, W., Zwinkels, R. 2009. A heterogeneous route to the European monetary system crisis. Applied Economics Letters 16(9), 929–932.CrossRefGoogle Scholar
Delli Gatti, D., Gaffeo, E., Gallegati, M., Giulioni, G., Kirman, A., Palestrini, A., Russo, A. 2007. Complex dynamics and empirical evidence. Information Sciences 177(5), 1204–1221.Google Scholar
Derveeuw, J. 2005. Market dynamics and agents behaviors: a computational approach. Artificial Economics 564, 15–27.CrossRefGoogle Scholar
Diebold, F., Mariano, R. 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13, 253–263.Google Scholar
Diebold, F., Nason, J. 1990. Nonparametric exchange rate prediction? Journal of International Economics 28(3–4), 315–332.CrossRefGoogle Scholar
Diks, C., van der Weide, R. 2005. Herding, a-synchronous updating and heterogeneity in memory in a CBS. Journal of Economic Dynamics and Control 29(4), 741–763.Google Scholar
Duffy, J. 2006. Agent-based models and human subject experiments. In Handbook of Computational Economics, Tesfatsion, L. & Judd, K. (eds). Elsevier, 949–1011.Google Scholar
Duffy, J., Fisher, E. 2005. Sunspots in the laboratory. American Economic Review 95(3), 510–529.CrossRefGoogle Scholar
Ecemis, I., Bonabeau, E., Ashburn, T. 2005. Interactive estimation of agent-based financial markets models: modularity and learning. In Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, Beyer, H. (ed.). ACM, 1897–1904.Google Scholar
Engle, R., Russell, J. 2007. Analysis of high frequency financial data. In Handbook of Financial Econometrics, Ait-Sahalia, Y. (ed.). Elsevier.Google Scholar
Farmer, D., Patelli, P., Zovko, I. 2005. The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences of the United States of America 102(6), 2254–2259.Google Scholar
Ferreira, F., De Oliveira, V., Crepaldi, A., Campos, P. 2007. Agent-based model with heterogeneous fundamental prices. Physica A 357(3–4), 534–542.Google Scholar
Föllmer, H., Horst, U., Kirman, A. 2005. Equilibria in financial markets with heterogeneous agents: a probabilistic perspective. Journal of Mathematical Economics 41(1–2), 123–155.Google Scholar
Forni, M., Lippi, M. 1997. Aggregation and the Microfoundation of Dynamic Macroeconomics. Clarendon Press.Google Scholar
Frankel, J., Froot, K. 1990. Chartists, fundamentalists, and trading in the foreign exchange market. American Economic Review 80(2), 181–186.Google Scholar
Gallegati, M., Palestrini, D., Delli Gatti, D., Scalas, E. 2006. Aggregation of heterogeneous interacting agents: the variant representative agent framework. Journal of Economic Interaction and Coordination 1(1), 5–19.CrossRefGoogle Scholar
Gabaix, X., Gopikrishnan, P., Plerou, V., Stanley, H. 2003. A theory of power-law distributions in financial market fluctuations. Nature 423, 267–270.Google Scholar
Gaunersdorfer, A., Hommes, C. 2007. A nonlinear structural model for volatility clustering. In Long Memory in Economics, Teyssière, G. & Kirman, A. (eds). Springer-Verlag, 265–288.Google Scholar
Ghose, D., Kroner, K. 1995. The relationship between GARCH and symmetric stable processes: finding the source of fat tails in financial data. Journal of Empirical Finance 2(3), 225–251.Google Scholar
Ghoulmie, F., Cont, R., Nadal, J.-P. 2005. Heterogeneity and feedback in an agent-based market model. Journal of Physics 17, 1259–1268.Google Scholar
Gilli, M., Winker, P. 2003. A global optimization heuristic for estimating agent based models. Computational Statistics and Data Analysis 42(3), 299–312.Google Scholar
Gourieroux, C., Monfort, A. 1996. Simulation-Based Econometric Methods. Oxford University.Google Scholar
Hall, R. 1988. Intertemporal substitution in consumption. Journal of Political Economy 96(2), 339–357.CrossRefGoogle Scholar
Hartley, J. 1997. The representative agent in macroeconomics. Routledge.CrossRefGoogle Scholar
He, X.-Z., Li, Y. 2007. Power-law behaviour, heterogeneity, and trend chasing. Journal of Economic Dynamics and Control 31(10), 3396–3426.Google Scholar
Hein, O., Schwind, M., Spiwoks, M. 2008. Frankfurt artificial stock market: a microscopic stock market model with heterogeneous interacting agents in small-world communication networks. Journal of Economic Interaction and Coordination 3(1), 59–71.CrossRefGoogle Scholar
Holland, J. H. 1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press.Google Scholar
Holland, J. H., Miller, J. H. 1991. Artificial adaptive agents in economic theory. American Economic Review 81(2), 365–370.Google Scholar
Hommes, C. 2002. Modeling the stylized facts in finance through simple nonlinear adaptive systems. Proceedings of the National Academy of Sciences of the United States of America 99, 7221–7228.Google Scholar
Hommes, C. 2006. Heterogeneous agent models in economics and finance. In Handbook of Computational Economics, Tesfatsion, L. & Judd, K. (eds). Elsevier, 1109–1186.Google Scholar
Iori, G. 2002. A microsimulation of traders activity in the stock market: The role of heterogeneity, agents’ interactions and trade frictions. Journal of Economic Behavior and Organization 49(2), 269–285.CrossRefGoogle Scholar
Izumi, K., Okatsu, T. 1996. An artificial market analysis of exchange rate dynamics. In Evolutionary Programming V, Fogel, L., Angeline, P. & Bäck, T. (eds). MIT, 27–36.Google Scholar
Izumi, K., Ueda, K. 1999. Analysis of dealers’ processing financial news based on an artificial market approach. Journal of Computational Intelligence in Finance 7, 23–33.Google Scholar
Janssen, M., Ostrom, E. 2006. Empirically based, agent-based models. Ecology and Society 11(2), 37.Google Scholar
Kaizoji, T. 2003. Speculative bubbles and fat tail phenomena in a heterogeneous agent model. In Economic Complexity, Barnette, W., Deissenberg, C. & Feichinger, G. (eds). Elsevier, 259–275.Google Scholar
Kirman, A. 1991. Epidemics of opinion and speculative bubbles in financial markets. In Money and Financial Markets, Taylor, M. (ed). Blackwell, 354–368.Google Scholar
Kirman, A. 1992. Whom or what does the representative individual represent? Journal of Economic Perspectives 6(2), 117–136.CrossRefGoogle Scholar
Kirman, A. 1993. Ants, rationality, and recruitment. Quarterly Journal of Economics 108(1), 137–156.CrossRefGoogle Scholar
Kirman, A., Teyssière, G. 2002. Microeconomic models for long memory in the volatility of financial time series. Studies in Nonlinear Dynamics and Econometrics 5(4), 3.Google Scholar
Kocherlakota, N. 1996. The equity premium: it's still a puzzle. Journal of Economic Literature 34(1), 42–71.Google Scholar
Koza, J. R. 1993. Genetic Programming: On the Programming of Computers by Means of Natural Selection. M.I.T. Press.Google Scholar
Lawrenz, C., Westerhoff, F. 2001. Explaining exchange rate volatility with a genetic algorithm. In Proceedings of the 6th International Conference of the Society for Computational Economics on Computing in Economics and Finance. Barcelona, Spain.Google Scholar
LeBaron, B. 2000. Agent-based computational finance: suggested readings and early research. Journal of Economic Dynamics and Control 24(5–7), 679–702.Google Scholar
LeBaron, B. 2006. Agent-based computational finance. In Handbook of Computational Economics, Tesfatsion, L. & Judd, K. (eds). Elsevier, 1187–1233.Google Scholar
LeBaron, B., Yamamoto, R. 2007. Long-memory in an order-driven market. Physica A 383(1), 85–89.Google Scholar
LeBaron, B., Arthur, W., Palmer, R. 1999. Time series properties of an artificial stock market. Journal of Economic Dynamics and Control 23(9–10), 1487–1516.CrossRefGoogle Scholar
Levy, H., Levy, M., Solomon, S. 2000. Microscopic Simulation of Financial Markets: From Investor Behavior to Market Phenomena. Academic.Google Scholar
Li, H., Rosser, B. 2004. Market dynamics and stock price volatility. The European Physical Journal B 39(3), 409–413.Google Scholar
Luce, R. 1959. Individual Choice Behavior: A Theoretical Analysis. Wiley.Google Scholar
Lux, T. 1995. Herd behavior, bubbles and crashes. Economic Journal 105(431), 881–896.CrossRefGoogle Scholar
Lux, T. 1997. Time variation of second moments from a noise trader/infection model. Journal of Economic Dynamics and Control 22(1), 1–38.Google Scholar
Lux, T. 1998. The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions. Journal of Economic Behavior and Organization 33(2), 143–165.Google Scholar
Lux, T. 2007. Applications of Statistical Physics in Finance and Economics. Working paper, Department of Economics, Kiel University.Google Scholar
Lux, T., Marchesi, M. 1999. Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(11), 498–500.Google Scholar
Lux, T., Marchesi, M. 2000. Volatility clustering in financial markets: a micro-simulation of interacting agents. International Journal of Theoretical and Applied Finance 3(4), 675–702.Google Scholar
Manzan, S., Westerhoff, F. 2005. Representativeness of news and exchange rate dynamics. Journal of Economic Dynamics and Control 29(4), 677–689.Google Scholar
Manzan, S., Westerhoff, F. 2007. Heterogeneous expectations, exchange rate dynamics and predictability. Journal of Economic Behavior and Organization 64(1), 111–128.Google Scholar
Mark, N. 1995. Exchange rates and fundamentals: evidence on long-horizon predictability. American Economic Review 85(1), 201–218.Google Scholar
Martinez-Jaramillo, S., Tsang, E. 2009. A heterogeneous, endogenous and co-evolutionary GP-based financial market. IEEE Transactions on Evolutionary Computation 13(1), 33–55.Google Scholar
Midgley, D., Marks, R., Kunchamwar, D. 2007. Building and assurance of agent-based models: an example and challenge to the field. Journal of Business Research 60(8), 884–893.Google Scholar
Miller, J. 1998. Active nonlinear tests (ANTs) of complex simulation models. Management Science 44(6), 820–830.Google Scholar
Muranaga, J., Ohsawa, M. 1997. Measurement of liquidity risk in the context of market risk calculation. In The Measurement of Aggregate Market Risk. Bank for International Settlements.Google Scholar
Neely, C., Roy, A., Whiteman, C. 2001. Risk aversion versus intertemporal substitution: a case study of identification failure in the intertemporal consumption capital asset pricing model. Journal of Business and Economic Statistics 19(4), 395–403.CrossRefGoogle Scholar
Neuberg, L., Bertels, K. 2003. Heterogeneous trading agents. Complexity 8(5), 28–35.Google Scholar
Neuberg, L., Louargant, C., Protin, P. 2004. From heterogeneous expectations to exchange rate dynamics. In Proceedings of the 10th International Conference of the Society for Computational Economics on Computing in Economics and Finance. University of Amsterdam, Amsterdam, Netherlands.Google Scholar
Newell, A., Simon, H. 1972. Human Problem Solving. Prentice Hall.Google Scholar
Pacurar, M. 2006. Autoregressive Conditional duration (ACD) Models in Finance: A Survey of the Theoretical and Empirical Literature. Working paper, HEC-Montreal.CrossRefGoogle Scholar
Palmer, R., Arthur, W., Holland, J., LeBaron, B., Tayler, P. 1994. Artificial economic life: a simple model of a stock market. Physica D 75(1–3), 264–274.CrossRefGoogle Scholar
Parke, W., Waters, G. 2007. An evolutionary game theory explanation of ARCH effects. Journal of Economic Dynamics and Control 31(7), 2234–2262.CrossRefGoogle Scholar
Pollard, M. 2006. Volatility Clustering, Excess Variability and Paretian Wealth Distribution in a Minimum Market Model. Working paper, Department of Finance and Applied Statistics, Australian National University.Google Scholar
Rabertoa, M., Cincottia, S., Focardib, S. 2001. Agent-based simulation of a financial market. Physica A 299(1–2), 319–327.CrossRefGoogle Scholar
Reimann, S., Tupak, A. 2007. Prices are macro-observables! Stylized facts from evolutionary finance. Computational Economics 29(3), 313–331.Google Scholar
Reitz, S., Westerhoff, F. 2007. Commodity price cycles and heterogeneous speculators: A STAR-GARCH model. Empirical Economics 33(2), 231–244.Google Scholar
Rosser, B. 1997. Speculations on nonlinear speculative bubbles. Nonlinear Dynamics, Psychology, and Life Sciences 1(4), 275–300.Google Scholar
Sallans, B., Pfister, A., Karatzoglou, A., Dorffner, G. 2003. Simulation and validation of an integrated markets model. Journal of Artificial Societies and Social Simulation 6(4), 1–33.Google Scholar
Samanidou, E., Zschischang, E., Stauffer, D., Lux, T. 2007. Agent-based models of financial markets. Reports of Progress in Physics 70, 409–450.Google Scholar
Sansone, A., Garofalo, G. 2007. Asset price dynamics in a financial market with heterogeneous trading strategies and time delays. Physica A 382(1), 247–257.Google Scholar
Shimokawa, T., Suzuki, K., Misawa, T. 2007. An agent-based approach to financial stylized facts. Physica A 379(1), 207–225.Google Scholar
Stoker, T. 1993. Empirical approaches to the problem of aggregation over individuals. Journal of Economic Literature 314, 1827–1874.Google Scholar
Suominen, M. 2001. Trading volume and information revelation in stock markets. Journal of Financial and Quantitative Analysis 36(4), 545–565.CrossRefGoogle Scholar
Tay, N., Lin, S. 2001. Fuzzy inductive reasoning, formation and the behavior of security prices. Journal of Economic Dynamics and Control 25(3–4), 321–362.Google Scholar
Taylor, S. 2005. Asset Price Dynamics, Volatility, and Prediction. Princeton University.Google Scholar
Tsay, R.-S. 2002. Analysis of Financial Time Series. Wiley Interscience.CrossRefGoogle Scholar
Vriend, N. 2000. An illustration of the essential difference between individual and social learning, and its consequences for computational analysis. Journal of Economic Dynamics and Control 24(1), 1–19.Google Scholar
Vriend, N. 2002. On two types of GA-Learning. In Evolutionary Computation in Economics and Finance, Chen, S.-H. (ed.). Physica-Verlag, 233–243.Google Scholar
Westerhoff, F., Reitz, S. 2003. Nonlinearities and cyclical behavior: the role of chartists and fundamentalists. Studies in Nonlinear Dynamics and Econometrics 7(4), Art. 3.Google Scholar
Winker, P., Gilli, M. 2001. Indirect Estimation of the Parameters of Agent-Based Models of Financial Markets. FAME Research paper, University of Geneva, 38.Google Scholar
Winker, P., Gilli, M., Jeleskovic, V. 2007. An objective function for simulation-based inference on exchange rate data. Journal of Economic Interaction and Coordination 2(2), 125–145.Google Scholar
Yang, J. 2002. The efficiency of an artificial double auction stock market with neural learning agents. In Evolutionary Computation in Economics and Finance, Chen, S.-H. (ed.). Physica-Verlag, 87–107.Google Scholar
Yeh, C.-H., Chen, S.-H. 2001. Market diversity and market efficiency: the approaches based on genetic programming. The Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behavior 1(1), 147–167.Google Scholar
Zovko, I., Farmer, P. 2007. Correlations and clustering in the trading of members of the London Stock Exchange. In Complexity, Metastability and Nonextensivity: An International Conference, AIP Conference Proceedings, Abe, S., Mie, T., Herrmann, H., Quarati, P., Rapisarda, A. & Tsallis, C. (eds). Springer.CrossRefGoogle Scholar