Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility
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http://dx.doi.org/10.1016/j.jempfin.2016.02.002 |
Metadatos
Título
Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatilityFecha de publicación
2016Editor
ElsevierISSN
0927-5398; 1879-1727Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0927539816300093Palabras clave / Materias
Resumen
We explore the issue of estimating a simple agent-based model of price formation in an asset market using the approach of Alfarano et al. (2008) as an example. Since we are able to derive various moment conditions for ... [+]
We explore the issue of estimating a simple agent-based model of price formation in an asset market using the approach of Alfarano et al. (2008) as an example. Since we are able to derive various moment conditions for this model, we can apply generalized method of moments (GMM) estimation. We find that we can get relatively accurate parameter estimates with an appropriate design of the GMM estimator that reduces the biases arising from strong correlations of the estimates of certain parameters. We apply our estimator to a sample of long records of returns of various stock and foreign exchange markets as well as the price of gold. Using the estimated parameters to form the best linear forecasts for future volatility we find that the behavioral model generates sensible forecasts that get close to those of a standard GARCH(1,1) model in their overall performance, and often provide useful information on top of the information incorporated in the GARCH forecasts. [-]
Publicado en
Journal of Empirical Finance 37 (2016) 1–19Derechos de acceso
© 2016 Elsevier B.V. All rights reserved.
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