A calibration procedure for analyzing stock price dynamics in an agent-based framework
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Otros documentos de la autoría: Recchioni, Maria Cristina; Tedeschi, Gabriele; Gallegati, Mauro
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8643
comunitat-uji-handle3:10234/8644
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A calibration procedure for analyzing stock price dynamics in an agent-based frameworkFecha de publicación
2015-11Editor
ElsevierCita bibliográfica
Recchioni, M. C., Tedeschi, G., & Gallegati, M. (2015). A calibration procedure for analyzing stock price dynamics in an agent-based framework. Journal of Economic Dynamics and Control, 60, 1-25.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0165188915001517Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
In this paper we introduce a calibration procedure for validating of agent based models. Starting from the well-known financial model of (Brock and Hommes, 1998), we show how an appropriate calibration enables the ... [+]
In this paper we introduce a calibration procedure for validating of agent based models. Starting from the well-known financial model of (Brock and Hommes, 1998), we show how an appropriate calibration enables the model to describe price time series. We formulate the calibration problem as a nonlinear constrained optimization that can be solved numerically via a gradient-based method. The calibration results show that the simplest version of the Brock and Hommes model, with two trader types, fundamentalists and trend-followers, replicates nicely the price series of four different markets indices: the S&P 500, the Euro Stoxx 50, the Nikkei 225 and the CSI 300. We show how the parameter values of the calibrated model are important in interpreting the trader behavior in the different markets investigated. These parameters are then used for price forecasting. To further improve the forecasting, we modify our calibration approach by increasing the trader information set. Finally, we show how this new approach improves the model׳s ability to predict market prices. [-]
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Journal of Economic Dynamics and Control Volume 60, November 2015Derechos de acceso
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