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Stochastic Behavioral Asset Pricing Models and the Stylized Facts

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  • Thomas Lux

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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  • Thomas Lux, 2008. "Stochastic Behavioral Asset Pricing Models and the Stylized Facts," Working Papers wp08-03, Warwick Business School, Finance Group.
  • Handle: RePEc:wbs:wpaper:wp08-03
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    Cited by:

    1. Leoni, Patrick L., 2011. "Psychological determinants of occurrence and magnitude of market crashes," Economic Modelling, Elsevier, vol. 28(5), pages 2190-2196, September.
    2. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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