Abstract
This work aims to investigate the behavior of financial agents in a complex setting where they interact and learn about the environment. Using the bottom-up approach of agent-based models, we simulate a situation where banks, depositors, a central bank, firms, and a clearing house compose an artificial financial system under different scenarios regarding monetary and macroprudential policy instances and emerging and developed countries realities. Banks are able to learn from the outcomes and endogenously set the market interest rate. The main conclusions are: with regard to the credit market, (i) policies reinforce each other's effects on credit supply when they are both restrictive. Regarding banks' risk-taking behavior, (ii) expansive monetary policy increases banks' loans and portfolio risk. Finally, (iii) restrictive instances in both policies, while promoting more capital and less risk in the balance sheet, are able to reduce risk to some extent. If combined in the right way, these policies may improve overall system stability.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 In more concentrated markets, banks often assess clients' risk imperfectly, potentially having a partial understanding of the clients' risk distribution.
2 We denote as the Cartesian product of the set n times.
3 In a real world scenario, we may estimate a probit or a logit model for a sample of firms and use the attributes of a given firm in order to estimate the value of p or 1−p.
4 This is equivalent to having .
5 Financial Soundness Indicators (FSI) and International Financial Statistics (IFS).
6 The full description of groups may be found here.