A1123
Title: Simulated ML estimation of a financial agent-based herding model
Authors: Jiri Kukacka - Czech Academy of Sciences (Czech Republic) [presenting]
Jozef Barunik - UTIA AV CR vvi (Czech Republic)
Abstract: We apply the very recent simulated MLE methodology to a stylised financial agent-based herding model where noise traders switch between the optimistic and pessimistic states. We test small sample properties of the estimator via Monte Carlo simulations and confirm important theoretical features of the estimator such as consistency and asymptotic efficiency. Via exploring behaviour of the objective simulated log-likelihood function we also verify the identification of parameters and theoretical assumptions of the estimation method. Next, we estimate the model using three stock market indices (DAX, SP500, and Nikkei), price of gold in USD, and three exchange rates (USD/EUR, USD/YEN, and CHF/YEN). Results of the full sample as well as rolling simultaneous estimation of parameters $a$ and $b$ governing switches of opinion and sentiment dynamics together with standard deviation of the innovations of the fundamental value are presented. Finally, we compare and contrast the performance of the NPSMLE method to the simulated method of moments approach.
