Parameter Estimation of an Agent-Based Model Under Loss Aversion | ||||
| The Egyptian Statistical Journal | ||||
| Editorial, Volume 65, Issue 2, December 2021, Page 14-29 PDF (770.18 K) | ||||
| DOI: 10.21608/esju.2021.246403 | ||||
 View on SCiNiTO | ||||
| Abstract | ||||
| An agent-based model under loss aversion behavioral bias is introduced in Selim et al. (2015), however, without estimating its parameters. The virtual financial market is populated with agents following two heterogeneous trading beliefs; technical and fundamental prediction rules. Agents switch between technical and fundamental trading with respect to their past performance. The agents are loss averse over asset price fluctuations. Loss aversion behavior depends on past performance measures of the trading strategies in terms of an evolutionary fitness measure. 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 | ||||
| Keywords | ||||
| Agent-based modelling; parameter estimation; bifurcation analysis | ||||
|
Statistics Article View: 104 PDF Download: 115 |
||||
