Simulated ML Estimation of Financial Agent-Based Models

Arbeitspapier

This paper proposes computational framework for empirical estimation of Financial Agent-Based Models (FABMs) that does not rely upon restrictive theoretical assumptions. We customise a recent methodology of the Non-Parametric Simulated Maximum Likelihood Estimator (NPSMLE) based on kernel methods by Kristensen and Shin (2012) and elaborate its capability for FABMs estimation purposes. To start with, we apply the methodology to the popular and widely analysed model of Brock and Hommes (1998). We extensively test finite sample properties of the estimator via Monte Carlo simulations and show that important theoretical features of the estimator, the consistency and asymptotic efficiency, also hold in small samples for the model. We also verify smoothness of the simulated log-likelihood function and identification of parameters. Main empirical results of our analysis are the statistical insignificance of the switching coefficient but markedly significant belief parameters defining heterogeneous trading regimes with an absolute superiority of trend-following over contrarian strategies and a slight proportional dominance of fundamentalists over trend following chartists.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IES Working Paper ; No. 07/2016

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Model Construction and Estimation ; Computational Techniques; Simulation Modeling ; Expectations; Speculations ; Asset Pricing; Trading Volume; Bond Interest Rates
• Subject: heterogeneous agent model ; heterogeneous expectations ; behavioural finance ; intensity of choice ; switching ; non-parametric simulated maximum likelihood estimator

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Baruník, Jozef ; Kukačka, Jiří
• Event: Veröffentlichung
• (who): Charles University in Prague, Institute of Economic Studies (IES)
• (where): Prague
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/174174
• Last update: 10.03.2025, 11:42 AM CET