Effect of the Central Bank's Contractionary Monetary Policy on Banks' Risk-Taking based on the Factor-Based Approach

The Banking Network has become a popular subject for empirical and methodological research that employs artificial intelligence (AI) methods, including agent-based modeling. The agent-based approach (ABM) plays a crucial role in evaluating the performance of banking networks under external shocks, which may trigger failures within the network of agents. The present study uses this approach to examine the impact of contractionary monetary policy on the risk-taking behavior of the banking system, specifically in terms of the amount of lending to firms in the real sector. The model includes five agents: banks, depositors, the central bank, firms, and a clearinghouse, of which the first two agents are intelligent, while the others are non-intelligent. The model encompasses ten major banks—Mellat, Tejarat, Sepah, Maskan, Keshavarzi, Ayandeh, Parsian, Pasargad, Saderat, and Refah—over the period 2018–2023, with uniform distribution in size. In this study, the total amount of loans to clients in the real sector is used as a proxy for bank risk. The findings indicate that contractionary monetary policy prompts banks to reduce the volume of loans to firms in the real sector, thereby taking on lower risk. However, simultaneously, the share of higher-risk loans increases. Moreover, the central bank’s contractionary policy, by activating the interbank market, alleviates liquidity constraints in the banking system. Nevertheless, the increased interbank transactions lead to greater risk propagation across the banking network. Consequently, under such a policy, resolving liquidity shortages in the banking network results in higher risk transmission along with reduced credit to the real economy.