Estimation of Financial Agent-Based Models with Simulated Maximum Likelihood

0478481 - ÚTIA 2018 RIV NL eng J - Journal Article
Kukačka, Jiří - Baruník, Jozef
Estimation of Financial Agent-Based Models with Simulated Maximum Likelihood.
Journal of Economic Dynamics & Control. Roč. 85, č. 1 (2017), s. 21-45. ISSN 0165-1889. E-ISSN 1879-1743
R&D Projects: GA ČR(CZ) GBP402/12/G097
Institutional support: RVO:67985556
Keywords : heterogeneous agent model, * simulated maximum likelihood * switching
OECD category: Finance
Impact factor: 1.579, year: 2017
http://library.utia.cas.cz/separaty/2017/E/kukacka-0478481.pdf

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 e ciently using simulations. Key empirical findings indicate the statistical insignificance of the switching coe cient 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.
Permanent Link: http://hdl.handle.net/11104/0275483