Monetary and Macroprudential Policy and Welfare in an Estimated Four‐Agent New Keynesian Model

Mon, 01 Jan 0001 00:00:00 +0000

This paper introduces a four-agent estimated New Keynesian DSGE model—comprising banked simple households, underbanked simple households, firm owners, and bank owners—to examine agent-specific and social welfare effects of monetary and macroprudential policy, estimated on U.S. quarterly data (1985Q1–2016Q4) via Bayesian methods. The model features two layers of endogenous default probability (for borrowers and banks), nominal, real, and financial frictions, and trend inflation and stochastic growth. The optimal bank capital requirement ratio (CRR) is estimated at 12.6%, which is 2.1% above Basel III’s 10.5%; increasing CRR up to approximately 12.2% raises welfare for all four agent types, though with smaller gains for credit-reliant simple households and firm owners. Countercyclical capital buffers benefit firm owners and bank owners with smaller gains for simple households. Coordinated monetary and macroprudential policy yields higher social welfare than non-coordinated policies.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. Why does the paper use four agent types instead of the usual borrower-saver distinction?

The standard borrower-saver split lumps together all interest-earning agents—including both simple deposit-holding households and wealthy bank owners—so that macroprudential policies that shift surplus from borrowers to savers appear to benefit the simple household and the banker equally; the four-agent framework separates these groups and allows for heterogeneous welfare effects. Population shares are calibrated using Compustat and the Survey of Consumer Finances (firm owners and bank owners as shareholders of non-financial and financial firms) and the National Survey of Unbanked and Underbanked Households (underbanked simple households with very limited access to banking services).

Q2. What is the optimal CRR and how does it compare to existing benchmarks?

The optimal social CRR is estimated at 12.6%, which is 2.1% higher than Basel III’s 10.5%, 4.6% higher than Basel II’s 8%, and 3.6% higher than the 9% optimal CRR of Mendicino et al. (2019) who use a borrower-saver welfare framework. Increasing the CRR up to approximately 12.2% improves welfare for all four agent types, though unequally: simple households and firm owners who rely on credit see smaller gains. Above 12.2%, stricter CRR harms firm owners and simple households (tighter credit reduces activity), while bank owners continue to gain via higher capital income share until the CRR exceeds 25.9%, above which even bank owners are harmed as loans fall dramatically.

Q3. How do countercyclical capital buffers and loan loss provisions affect welfare by agent type?

Countercyclical capital buffers support firm owners and bank owners with smaller gains for the two simple household types; countercyclical loan loss provisions improve social welfare only for specific shocks and benefit underbanked simple households and firm owners at the expense of bank owners and banked simple households. The asymmetry reflects the different income streams: bank owners’ income derives primarily from loan returns and capital gains on bank equity, while underbanked simple households are most sensitive to credit availability. Loan loss provisions affect the timing of income recognition and loss absorption, generating distributional trade-offs that differ from those of capital requirements.

Q4. What are the gains from coordinating monetary and macroprudential policy?

Coordinating monetary and macroprudential policy yields higher social welfare than assigning each policy to an independent authority targeting its own objective, demonstrating that the interaction between interest rate policy and bank capital regulation matters for welfare outcomes. Investment shocks (27.41% of GDP growth variance) and financial risk shocks (~20%) are quantitatively important in this interaction. The model’s rich friction structure means that optimal monetary policy must account for how macroprudential policy changes the credit supply environment, and vice versa; failing to coordinate creates inefficiencies that coordinated policy avoids.

Key concepts

four-agent model : the model’s typology distinguishing banked simple households, underbanked simple households, firm owners, and bank owners; enables agent-specific welfare analysis of macroprudential policy with heterogeneous income streams and credit access. optimal capital requirement ratio (CRR) : the bank capital-to-assets ratio that maximizes social welfare; estimated at 12.6% in this model; 2.1% above Basel III’s current 10.5% requirement. countercyclical capital buffer (CCyB) : a macroprudential tool requiring banks to hold additional capital during economic expansions to be released in downturns; shown here to benefit firm owners and bank owners with smaller gains for simple households. dynamic loan loss provisions : a macroprudential tool requiring banks to build provisions against future expected losses during expansions; shown here to have welfare effects that depend on the source of the shock and to benefit different agent types than capital requirements.

Oil price fluctuations, US banks, and macroprudential policy

Mon, 01 Jan 0001 00:00:00 +0000

This paper estimates the effect of oil price fluctuations on US banking variables using a Bayesian SVAR with sign restrictions following Baumeister and Hamilton (2019). Oil market shocks that lead to a contraction in world economic activity are found to unambiguously lower the amount of bank credit to the US economy, tend to decrease US banks’ net worth, and tend to increase the US credit spread. The effects can be strong and long-lasting or more modest and short-lived, depending on the source of the oil price fluctuation. The effects are found to be stronger for smaller and lower-leveraged banks.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the empirical strategy?

The paper extends the state-of-the-art oil market SVAR of Baumeister and Hamilton (2019) to incorporate three US banking variables—banks’ net worth, the US credit spread, and the amount of bank credit extended—estimated with monthly data over January 1974 through December 2019. An agnostic approach is taken on sign restrictions for the US banking block: no restrictions are imposed on banking variables beyond those already imposed by Baumeister and Hamilton (2019) on the oil block, so the results for banking variables are driven primarily by data rather than prior restrictions. This extends earlier work that studied oil prices and credit spreads (Abbritti et al., 2020) or oil prices and stock markets (Kilian and Park, 2009) in isolation.

Q2. What is the main finding regarding the effect of oil shocks on banks?

Oil market shocks that lead to a contraction in world economic activity are found to unambiguously lower the amount of bank credit to the US economy, tend to decrease US banks’ net worth, and tend to increase the US credit spread. “Unambiguously” reflects that the sign restrictions impose no prior on the direction of credit’s response, so the finding that credit falls is driven entirely by data. The paper is the first to characterize the effect of oil market shocks on banks’ net worth and to estimate the credit effect within the SVAR framework.

Q3. How do the effects differ by the source of oil price fluctuations?

The effects on banking variables can be strong and long-lasting or more modest and short-lived, depending on the underlying source of the oil price change—reflecting the SVAR framework’s decomposition of oil price movements into distinct structural shocks. The distinction between oil supply shocks, demand shocks driven by global activity, and demand shocks driven by speculative factors implies that shocks of the same sign in the oil price may have different magnitudes and durations of effects on banks, consistent with Kilian (2009)’s decomposition.

Q4. Which banks are most affected?

The effects of oil market shocks on banking variables are found to be stronger for smaller and lower-leveraged banks. Smaller banks may be more exposed to oil-related regional economic downturns through concentrated loan portfolios, while lower-leveraged banks may face different collateral and risk dynamics relative to more highly leveraged peers.

Key concepts

oil market Bayesian SVAR : a structural vector autoregression that uses a Bayesian prior over sign restrictions to identify oil supply shocks, oil demand shocks related to global real activity, and oil-specific demand shocks, following Baumeister and Hamilton (2019); extended here to include US banking variables. credit spread : the difference between yields on corporate bonds or loans and a risk-free reference rate; used as a measure of the credit risk premium and financial conditions in US credit markets.

Macroprudential | Macro Paper Warehouse

Monetary and Macroprudential Policy and Welfare in an Estimated Four‐Agent New Keynesian Model

In depth

Q1. Why does the paper use four agent types instead of the usual borrower-saver distinction?

Q2. What is the optimal CRR and how does it compare to existing benchmarks?

Q3. How do countercyclical capital buffers and loan loss provisions affect welfare by agent type?

Q4. What are the gains from coordinating monetary and macroprudential policy?

Key concepts

Oil price fluctuations, US banks, and macroprudential policy

In depth

Q1. What is the empirical strategy?

Q2. What is the main finding regarding the effect of oil shocks on banks?

Q3. How do the effects differ by the source of oil price fluctuations?

Q4. Which banks are most affected?

Key concepts