Reproduce stylized facts of artificial financial market and comparison with real data

Agent-based computational models represent a big challenge in many disciplines. A vital approach receiving much interest isagent-based models, which gives a new area providing some ways to tackle some of the restrictions of the analytical modelsin finance. The aim of our research is to contribute to the behavioral finance and agent-based artificial markets by studyingtheir market-wise implications using computational simulations. We investigate and analyze the behavioral foundations of thestylized facts of empirical data such as that characterize real data in financial markets. Our results confirm the existence of mostthe stylized facts such as leptokurtosis, non-independently distributed, and volatility clustering. From this attention, the artificialfinancial market will for all time be evaluated in order to have explication about market dynamics in Tunisian financial market.

[1]  J. Farmer,et al.  The price dynamics of common trading strategies , 2000, cond-mat/0012419.

[2]  Norman Ehrentreich.  Technical Trading in the Santa Fe Institute Artificial Stock Market Revisited , 2006 .

[3]  R. Cont.  Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models. , 2005 .

[4]  R. Lupu,et al.  Statistical Properties of the CEE Stock Market Dynamics. A Panel Data Analysis , 2010 .

[5]  Sanford J. Grossman.  On the Impossibility of Informationally Efficient Markets , 1980 .

[6]  I. Lupu,et al.  The behavior of the Bucharest Stock Exchange during the current financial markets crisis and proposed measures for its sustainable development , 2009 .

[7]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[8]  S. Alfarano,et al.  What distinguishes individual stocks from the index? , 2010 .

[9]  Robert Hudson,et al.  Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns , 2013 .

[10]  Jozef Baruník,et al.  Behavioural breaks in the heterogeneous agent model: The impact of herding, overconfidence, and market sentiment , 2012, 1205.3763.

[11]  D. Bradford.  Economics in Crisis , 2011 .

[12]  Volatility and causality study of the daily returns on the Bucharest Stock Exchange during 2007-2011 , 2011 .

[13]  Frédéric Abergel,et al.  Econophysics: Empirical facts and agent-based models , 2009, 0909.1974.

[14]  W. Brock,et al.  Heterogeneous beliefs and routes to chaos in a simple asset pricing model , 1998 .

[15]  R. Cont.  Empirical properties of asset returns: stylized facts and statistical issues , 2001 .

[16]  F. Westerhoff,et al.  Structural stochastic volatility in asset pricing dynamics: Estimation and model contest , 2012 .

[17]  Ralph Grothmann,et al.  Multi agent market modeling based on neutral networks , 2002 .

[18]  Wei Zhang,et al.  Impact of information cost and switching of trading strategies in an artificial stock market , 2013, 1311.4274.

[19]  Sebastiano Manzan,et al.  Behavioral Heterogeneity in Stock Prices , 2005 .

[20]  J. Hussman.  Market efficiency and inefficiency in rational expectations equilibria: Dynamic effects of heterogeneous information and noise , 1992 .

[21]  W. Arthur.  Inductive Reasoning and Bounded Rationality , 1994 .

[22]  Robert L. Axtell,et al.  WHY AGENTS? ON THE VARIED MOTIVATIONS FOR AGENT COMPUTING IN THE SOCIAL SCIENCES , 2000 .

[23]  B. LeBaron.  Building the Santa Fe Artificial Stock Market , 2002 .

[24]  W. Arthur,et al.  Inductive Reasoning and Bounded Rationality ( The El Farol Problem ) , 1999 .

[25]  Adolfo López-Paredes,et al.  Modelling Learning and R&D in Innovative Environments: a Cognitive Multi-Agent Approach , 2004, J. Artif. Soc. Soc. Simul..

[26]  M. Marchesi,et al.  VOLATILITY CLUSTERING IN FINANCIAL MARKETS: A MICROSIMULATION OF INTERACTING AGENTS , 2000 .

[27]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .