• DOI:10.1007/978-3-030-87473-5_19
• Corpus ID: 211678111

Financial Replicator Dynamics: Emergence of Systemic-Risk-Averting Strategies

@inproceedings{Saha2020FinancialRD,
  title={Financial Replicator Dynamics: Emergence of Systemic-Risk-Averting Strategies},
  author={Indrajit Saha and Veeraruna Kavitha},
  booktitle={International Conference on Network Games, Control and Optimization},
  year={2020}
}

• Indrajit Saha, V. Kavitha
• Published in
  International Conference on…
  1 February 2020
• Economics

We consider a random financial network with a large number of agents. The agents connect through credit instruments borrowed from each other or through direct lending, and these create the liabilities. The settlement of the debts of various agents at the end of the contract period can be expressed as solutions of random fixed point equations. Our first step is to derive these solutions (asymptotically), using a recent result on random fixed point equations. We consider a large population in which agents adapt one of the two available strategies, risky or risk-free investments, with an aim to maximize their expected returns (or surplus). We aim to study the emerging strategies when different types of replicator dynamics capture inter-agent interactions. We theoretically reduced the analysis of the complex system to that of an appropriate ordinary differential equation (ODE). We proved that the equilibrium strategies converge almost surely to that of an attractor of the ODE. We also derived the conditions under which a mixed evolutionary stable strategy (ESS) emerges; in these scenarios the replicator dynamics converges to an equilibrium at which the expected returns of both the populations are equal. Further the average dynamics (choices based on large observation sample) always averts systemic risk events (events with large fraction of defaults). We verified through Monte Carlo simulations that the equilibrium suggested by the ODE method indeed represents the limit of the dynamics.