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.