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Combining Monetary Policy and Prudential Regulation: an agent-based modeling approach

Michel Alexandre and Gilberto Lima

No 394, Working Papers Series from Central Bank of Brazil, Research Department

Abstract: The aim of this paper is to study the interaction between monetary policy and prudential regulation in an agent-based modeling framework. In the model proposed here, firms borrow funds from the banking system in an economy regulated by a central bank. The central bank is responsible for carrying out monetary policy, by setting the interest rate, and prudential regulation, by establishing the banking capital requirement. Different combinations of interest rate and capital requirement rules are evaluated regarding both macroeconomic and financial stability. Several relevant policy implications are drawn from this analysis. First, the implementation of a cyclical capital component as proposed in Basel III, while successful in reducing financial instability when applied alone, loses its efficacy when combined with some interest rate rules. Second, interest rate smoothing is more effective than the other interest rate rules assessed, as it outperforms them concerning financial stability and performs as well as them regarding macroeconomic stability. Finally, there is no long-run tradeoff between monetary and financial stability regarding the sensitiveness of the cyclical capital component to the credit-to-output ratio, as well as the smoothing interest rate parameter.

Date: 2015-08
New Economics Papers: this item is included in nep-cba, nep-cmp, nep-ger, nep-mac and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Combining monetary policy and prudential regulation: an agent-based modeling approach (2020) Downloads
Working Paper: Combining Monetary Policy and Prudential Regulation: An Agent-Based Modeling Approach (2017) Downloads
Working Paper: COMBINING MONETARY POLICY AND PRUDENTIAL REGULATION: AN AGENT-BASED MODELING APPROACH (2016) Downloads
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