Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach
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https://doi.org/10.1007/s10614-016-9638-4 |
Metadatos
Título
Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments ApproachFecha de publicación
2018-10Editor
SpringerISSN
0927-7099; 1572-9974Cita bibliográfica
CHEN, Zhenxi; LUX, Thomas. Estimation of sentiment effects in financial markets: A simulated method of moments approach. Computational Economics, 2018, vol. 52, no 3, p. 711-744Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s10614-016-9638-4Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
We take the model of Alfarano et al. (J Econ Dyn Control 32:101–136, 2008) as a prototype agent-based model that allows reproducing the main stylized facts of financial returns. The model does so by combining fundamental ... [+]
We take the model of Alfarano et al. (J Econ Dyn Control 32:101–136, 2008) as a prototype agent-based model that allows reproducing the main stylized facts of financial returns. The model does so by combining fundamental news driven by Brownian motion with a minimalistic mechanism for generating boundedly rational sentiment dynamics. Since we can approximate the herding component among an ensemble of agents in the aggregate by a Langevin equation, we can either simulate the model in full at the micro level, or via an approximate aggregate law of motion. In the simplest version of our model, only three parameters need to be estimated. We explore the performance of a simulated method of moments (SMM) approach for the estimation of this model. As it turns out, sensible parameter estimates can only be obtained if one first provides a rough “mapping” of the objective function via an extensive grid search. Due to the high correlations of the estimated parameters, uninformed choices will often lead to a convergence to any one of a large number of local minima. We also find that the efficiency of SMM is relatively insensitive to the size of the simulated sample over a relatively large range of sample sizes and the SMM estimates converge to their GMM counterparts only for large sample sizes. We believe that this feature is due to the limited range of moments available in univariate asset pricing models, and that the sensitivity of the present model to the specification of the SMM estimator could carry over to many related agent-based models of financial markets as well as to similar diffusion processes in mathematical finance. [-]
Publicado en
Computational Economics, 2018, vol. 52, no 3Proyecto de investigación
European Union's Seventh Framework Programme / 612955Derechos de acceso
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