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Surrogate-based optimization for reduction of contagion susceptibility in financial systems

This paper studies a bi-objective optimization problem in which the goal is to optimize connections between entities in the market in order to make the entire system resilient to shocks of varying magnitudes. As observed in the literature concerning contagion on the inter-bank market, no system structure maximizes resilience to shocks of all sizes. For larger shocks more connections between entities make the crisis worse, and for smaller shocks more connected systems turn out to be more resilient than less connected ones. In order to benefit from the information about the system connectivity, a machine learning model is used to estimate the values of the objectives attained by a given solution. The paper presents a comparison of a multiobjective optimization algorithm using simulations for evaluating solutions with a surrogate-based algorithm using a machine learning model. In the experiments the surrogate-based method outperformed the simulation-based one. This observation along with the analysis of the dependence between system connectivity and the resilience to shocks of different magnitudes presented in the paper allow to conclude that the information on system connectivity can be used for improving the working of optimization methods aimed at making the system less susceptible to financial contagion.