The paper presents a simple agent-based model of the financial market, in which one type of security is traded. There are two assets in the model - the speculative security and money. All agents in the model are players of the same type, their behavior obeys a simple algorithm. An important feature of the model is that the expectations of the future prices are the same for all agents. Agents differ in their investment horizon. Experimentally obtained time series were tested for the presence of well known stylized facts. In particular, the experimental time series show presence of the fat tails effect. On the other hand, the effect of volatility clustering for these time series is not revealed.
You will need Adobe Acrobat reader. For more information and free download of the reader, please follow this link.
References
[1] B. Mandelbrot, The variation of certain speculative prices, The Journal of
Business, 36, No 4 (1963), 394419.
[2] R. Cont, Empirical properties of asset returns: stylized facts and statistical
issues, Quantitative Finance, 1, No 2 (2001), 223-236.
[3] N. Ehrentreich, Agent-based modeling: The Santa Fe Institute artificial
stock market model revisited, In: Lecture Notes in Economics and Mathematical
Systems, Springer Science & Business Media (2007).
[4] J. Campbell, A. Lo, A. MacKinlay, R. Whitelaw, The econometrics of financial markets, In: Macroeconomic Dynamics, Princeton University Press
(1997).
[5] T. Lux, Stochastic behavioral asset-pricing models and the stylized facts,
Handbook of financial markets: Dynamics and evolution, In: Handbooks
in Finance (2009), 161-215.
[6] S. Cramer, T. Trimborn, Stylized Facts and Agent-Based Modeling,
Preprint arXiv:1912.02684 (2019).
[7] G. Kim, H. Markowitz, Investment rules, margin and market volatility,
Journal of Portfolio Management, 16 (1989), 4552.
[8] M. Levy, H. Levy, S. Solomon, A microscopic model of the stock market:
Cycles, booms, and crashes, Economics Letters, 45, No 1 (1994), 103111.
[9] M. Levy, S. Solomon, Power laws are logarithmic Boltzmann laws, International
Journal of Modern Physics C, 7, No 4 (1996), 595601.
[10] S. Solomon, M. Levy, Spontaneous scaling emergence in generic stochastic systems, International Journal of Modern Physics C, 7, No 5 (1996),
745751.
[11] X. He, K. Li, Heterogeneous beliefs and adaptive behaviour in a
continuous-time asset price model, Journal of Economic Dynamics and
Control, 36, No 7 (2012), 973987.
[12] F. Westerhoff, Multiasset market dynamics, Macroeconomic Dynamics, 8,
No 5 (2004), 591616.
[13] N. Schmitt, F. Westerhoff, Speculative behavior and the dynamics of interacting stock markets, Journal of Economic Dynamics and Control, 45
(2014), 262288.
[14] F.Westerhoff, R. Dieci, The effectiveness of KeynesTobin transaction taxes
when heterogeneous agents can trade in different markets: A behavioral
finance approach, Journal of Economic Dynamics and Control, 30, No 2
(2006), 293322.
[15] R. Dieci, I. Foroni, L. Gardini, X. He, Market Mood, Adaptive Beliefs
and Asset Price Dynamics, Chaos, Solutions & Fractals, 29, No 3 (2006),
520-534.
[16] M. Lettau, Explaining the facts with adaptive agents: The case of mutual
fund flows, Journal of Economic Dynamics and Control, 21, No 7 (1997),
11171147.
[17] J. Arifovic, The behavior of the exchange rate in the genetic algorithm and
experimental economies, Journal of Political Economy, 104, No 3 (1996),
510541.
[18] D. Ladley, Zero intelligence in economics and finance, The Knowledge Engineering
Reviw, 27, No 2 (2012), 273-286.
[19] W. Brock, C. Hommes, Heterogeneous beliefs and routes to chaos in a
simple asset pricing model, Journal of Economic Dynamics and Control,
22, No 8-9 (1998), 12351274.
[20] W. Arthur, J. Holland, B. LeBaron, R. Palmer, P. Tayler, Asset pricing
under endogenous expectations in an artificial stock market. In: W.B.
Arthur, S. Durlauf, D. Lane (Eds.), The Economy as an Evolving Complex
System II. Addison-Wesley, Reading, MA (1997), 1544.