A mixed-game and co-evolutionary genetic programming agent-based model of financial contagion

Abstract

Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during financial crises are referred to as financial contagion. We simulate the transmission of financial crises in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a comprehensive approach, we develop an agent-based multinational model and investigate the reasons for contagion. Our model comprises four types of traders: noise, herd, game, and technical traders respectively. Different types of traders use different computational strategies to make “buy”, “sell”, or ”hold” decisions. Although contagion has been extensively investigated in the financial literature, it has not yet been studied through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at an earlier stage, hence recognizing financial crises with the potential to destabilize cross-market linkages. In the real world, such information would be extremely valuable to develop appropriate risk management strategies.