Detail publikace

Behavioural breaks in the heterogeneous agent model: The impact of herding, overconfidence, and market sentiment

Autor: doc. PhDr. Jozef Baruník Ph.D.,
PhDr. Jiří Kukačka Ph.D.,
Typ: Články v impaktovaných časopisech
Rok: 2013
Číslo: 0
ISSN / ISBN: 0378-4371
Publikováno v: Physica A, 392 (23), pp. 5920-5938, DOI
Místo vydání:
Klíčová slova: heterogeneous agent model, behavioural finance, herding, overconfidence, market sentiment, stock market crash
JEL kódy: C1, C61, D84, G01, G12
Citace: Kukacka, J., Barunik, J. (2013). Behavioural breaks in the heterogeneous agent model: The impact of herding, overconfidence, and market sentiment. Physica A: Statistical Mechanics and its Applications, 392 (23), pp. 5920-5938.
Granty: 402/09/0965: Nové přístupy pro monitorování a predikci na kapitálových trzích GAUK 588912 - Empirická validace modelů s heterogenními agenty SVV 267 504: Intensification of Doctoral Research in Economics and Finance: Extending Alternative Approaches to Economic Models
Abstrakt: The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) framework. Behavioural patterns are injected into an asset pricing framework through the so-called ‘Break Point Date’, which allows us to examine their direct impact. In particular, we analyse the dynamics of the model around the behavioural break. Price behaviour of 30 Dow Jones Industrial Average constituents covering five particularly turbulent US stock market periods reveals interesting patterns in this aspect. To replicate it, we apply numerical analysis using the Heterogeneous Agent Model extended with the selected findings from behavioural finance: herding, overconfidence, and market sentiment. We show that these behavioural breaks can be well modelled via the Heterogeneous Agent Model framework and they extend the original model considerably. Various modifications lead to significantly different results and model with behavioural breaks is also able to partially replicate price behaviour found in the data during turbulent stock market periods.


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