Parameter estimation of an agent-based model under loss aversion

Abstract EN

An agent-based model under loss aversion behavioral bias is introduced in Selim et al.

(2015), however, without estimating its parameters.

The proposed model proves great ability to replicate important stylized facts of real financial markets, such as random-walk prices, heavy-tailed returns distribution, clustered volatility, excess volatility, the absence of autocorrelation in raw returns, and the power-law autocorrelations in absolute returns, and fractal structure.

However, the extent to which the model is able to predict the behavior of certain stock markets will be increased by estimating model parameters.

In this article, the model parameters are estimated by conducting stability analysis and by indirect estimation.

By this, policy makers can use this model as testbed to investigate the effect of any decision prior to applying it on the real stock market.

Also, researchers can use this model to predict traders’ behavior towards different hypotheses.