Explaining stock return distributions via an agent-based model

作     者：Seedat, Shaheen Abelman, Shirley 

作者机构：Univ Witwatersrand Sch Comp Sci & Appl Math Private Bag 3 ZA-2050 Johannesburg South Africa 

出 版 物：《NONLINEAR DYNAMICS》 (非线性动力学)

年 卷 期：2021年第105卷第1期

页      面：1063-1096页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Complexity analysis Multiscale entropy Agent-based modelling Mixed Gaussian models Stock return distributions Expectation-maximization algorithm 

摘      要：US stock returns exhibit mixed Gaussian probabilistic features as well as nonlinear and complex dynamics, but existing agent-based models of stock markets have not focused on replicating or explaining all these phenomena jointly. In this paper, a new agent-based model of the stock market is proposed that can replicate and explain such phenomena jointly. In the new model, stocks are a claim to a dividend process determined by a hidden state process which follows a Markov chain. The model produces a probability distribution of stock returns that is mixed Gaussian, like the US stock market. Using a generalized multiscale entropy method, it is shown that the simulated returns have similar complexity and entropy properties to US stock returns for plausible parameter values. Sensitivity analyses show that the simulated stock price series generated by the model varies in a plausible manner with various underlying important parameters such as agent risk aversion, agent beliefs, the underlying stock dividend process, returns to risk-free assets and dividend transition probabilities.