Title
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.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586243
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
financial contagion,economic cycles,mixed-game genetic programming,co-evolutionary genetic programming,game theory,risk management,financial market crises,genetic algorithms,financial management,cross-market linkages,globalisation,agent-based multinational model,games,decision trees,computational intelligence,couplings,evolutionary genetics,financial market,sensitivity,noise,correlation
Financial economics,Agent-based model,Financial contagion,Actuarial science,Computer science,Risk management,Artificial intelligence,Financial market,Globalization,Game theory,Financial management,Machine learning,Business cycle
Conference
ISBN
Citations 
PageRank 
978-1-4244-6909-3
1
0.42
References 
Authors
2
3
Name
Order
Citations
PageRank
Fang Liu11188125.46
Antoaneta Serguieva2235.05
Paresh Date39316.15