From discrete-time models to continuous-time, asynchronous modeling of financial markets
Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information asymmetry, and investigate the difference caused in the market dynamics between the discrete-time simulation and continuous-time, asynchronous simulation. We show that the characteristics of the market prices are different in the two cases, and observe that additional information is being revealed in the continuous-time, asynchronous models, which can be acted upon by the agents in such models. Because most financial markets are continuous and asynchronous in nature, our results indicate that explicit consideration of this fundamental characteristic of financial markets cannot be ignored in their agent-based modeling.
market maker information asymmetry artificial stock markets agent-based computational economics continuous-time simulation