Reinforcement Learning Policy Recommendation for Interbank Network Stability

TL;DR

This paper explores how reinforcement learning-based policy recommendations can enhance the stability of interbank networks by guiding credit relationships and reducing systemic risk.

Contribution

It introduces a reinforcement learning approach to optimize policy recommendations that influence interbank lending and network structure for improved systemic resilience.

Findings

Emergence of a core-periphery network structure enhances system resilience.

Optimal policies mitigate systemic risk effectively.

Homogeneity in lender and borrower sizes supports network stability.

Abstract

In this paper, we analyze the effect of a policy recommendation on the performance of an artificial interbank market. Financial institutions stipulate lending agreements following a public recommendation and their individual information. The former is modeled by a reinforcement learning optimal policy that maximizes the system's fitness and gathers information on the economic environment. The policy recommendation directs economic actors to create credit relationships through the optimal choice between a low interest rate or a high liquidity supply. The latter, based on the agents' balance sheet, allows determining the liquidity supply and interest rate that the banks optimally offer their clients within the market. Thanks to the combination between the public and the private signal, financial institutions create or cut their credit connections over time via a preferential attachment evolving procedure able to generate a dynamic network. Our results show that the emergence of a core-periphery interbank network, combined with a certain level of homogeneity in the size of lenders and borrowers, is essential to ensure the system's resilience. Moreover, the optimal policy recommendation obtained through reinforcement learning is crucial in mitigating systemic risk.