Publication detail

Estimation of financial agent-based models with simulated maximum likelihood

Author(s): doc. PhDr. Jozef Baruník Ph.D.,
PhDr. Jiří Kukačka Ph.D.,
Type: Articles in journals with impact factor
Year: 2017
Number: 0
ISSN / ISBN:
Published in: Journal of Economic Dynamics and Control, 85, pp. 21-45, DOI
Publishing place:
Keywords: heterogeneous agent model, simulated maximum likelihood, estimation, intensity of choice, switching
JEL codes: C14, C51, C63, D84, G02, G12
Suggested Citation: Kukacka, J., Barunik, J. (2017). Estimation of financial agent-based models with simulated maximum likelihood. Journal of Economic Dynamics and Control, 85, pp. 21-45.
Grants: DYME – Dynamic Models in Economics GAUK 192215 - Simulated ML Estimation of Financial Agent-Based Models
Abstract: This paper proposes a general computational framework for empirical estimation of financial agent-based models, for which criterion functions have unknown analytical form. For this purpose, we adapt a recently developed nonparametric simulated maximum likelihood estimation based on kernel methods. In combination with the model developed by Brock and Hommes (1998), which is one of the most widely analysed heterogeneous agent models in the literature, we extensively test the properties and behaviour of the estimation framework, as well as its ability to recover parameters consistently and efficiently using simulations. Key empirical findings indicate the statistical insignificance of the switching coefficient but markedly significant belief parameters that define heterogeneous trading regimes with a predominance of trend following over contrarian strategies. In addition, we document a slight proportional dominance of fundamentalists over trend-following chartists in major world markets.

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