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Stochastic behavioral asset pricing models and the stylized facts

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  • Lux, Thomas

Abstract

High-frequency financial data are characterized by a set of ubiquitous statistical properties that prevail with surprising uniformity. While these 'stylized facts' have been well-known for decades, attempts at their behavioral explanation have remained scarce. However, recently a new branch of simple stochastic models of interacting traders have been proposed that share many of the salient features of empirical data. These models draw some of their inspiration from the broader current of behavioural finance. However, their design is closer in spirit to models of multi-particle interaction in physics than to traditional asset-pricing models. This reflects a basic insight in the natural sciences that similar regularities like those observed in financial markets (denoted as 'scaling laws' in physics) can often be explained via the microscopic interactions of the constituent parts of a complex system. Since these emergent properties should be independent of the microscopic details of the system, this viewpoint advocates negligence of the details of the determination of individuals' market behavior and instead focuses on the study of a few plausible rules of behavior and the emergence of macroscopic statistical regularities in a market with a large ensemble of traders. This chapter will review the philosophy of this new approach, its various implementations, and its contribution to an explanation of the stylized facts in finance.

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  • Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:7328
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    7. Torsten Trimborn, 2018. "A Macroscopic Portfolio Model: From Rational Agents to Bounded Rationality," Papers 1805.11036, arXiv.org, revised Oct 2018.
    8. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    9. Demary, Markus, 2010. "Transaction taxes and traders with heterogeneous investment horizons in an agent-based financial market model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-44.
    10. Torsten Trimborn & Lorenzo Pareschi & Martin Frank, 2017. "Portfolio Optimization and Model Predictive Control: A Kinetic Approach," Papers 1711.03291, arXiv.org, revised Feb 2019.
    11. Daniel Fricke & Thomas Lux, 2015. "The effects of a financial transaction tax in an artificial financial market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 119-150, April.
    12. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2018. "Simulation of Stylized Facts in Agent-Based Computational Economic Market Models," Papers 1812.02726, arXiv.org, revised Nov 2019.
    13. Torsten Trimborn & Martin Frank & Stephan Martin, 2017. "Mean Field Limit of a Behavioral Financial Market Model," Papers 1711.02573, arXiv.org.
    14. Trimborn, Torsten & Frank, Martin & Martin, Stephan, 2018. "Mean field limit of a behavioral financial market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 613-631.
    15. Radu T. Pruna & Maria Polukarov & Nicholas R. Jennings, 2016. "A new structural stochastic volatility model of asset pricing and its stylized facts," Papers 1604.08824, arXiv.org.

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