Should Oil Economies Worry About Oil Shocks’ Impact on the Banking System? The Case of Oman

Should Oil Economies Worry About Oil Shocks’ Impact on the Banking System? The Case of Oman
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Volume/Issue: Volume 2026 Issue 142
Publication date: July 2026
ISBN: 9798229050814
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Topics covered in this book

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Banks and Banking , Finance , Economics- Macroeconomics , Money and Monetary Policy , Oil price shocks , DSGE , Credit rationing , Banking liquidity , Sovereign Wealth Fund , Fiscal-financial nexus , Islamic banking , Oman , Credit , Commercial banks , Oil , Liquidity

Summary

This paper investigates the transmission of oil price shocks to the banking sector in oil-dependent economies, using Oman as a case study. We develop a DSGE model featuring an integrated banking block with endogenous credit rationing and a sovereign wealth fund stabilization rule, calibrated to Omani institutional targets and disciplined by Bayesian methods. Our structural approach disentangles two primary transmission channels: the solvency channel, driven by credit risk and non-performing loans (NPLs), and the liquidity channel, driven by pro-cyclical government deposit withdrawals and sovereign debt issuance. The structural variance decomposition attributes over 54% of non-oil GDP variance and 53% of credit variance to oil price shocks, while bank capital shocks account for less than 0.1%, confirming the quantitative dominance of the liquidity channel. We identify a precautionary liquidity motive—a “liquidity buffer trap”—where banks maintain excess liquidity during booms to hedge against hydrocarbon volatility, structurally suppressing credit to the productive sector. Our counterfactual regime analysis reveals the stabilizing power of credit depth: banking conservatism protects long-term physical capital formation, and the ongoing financialization of the corporate sector— including the rapid growth of Islamic banking and sukuk markets—under Vision 2040 further amplifies this structural resilience. We acknowledge identification challenges inherent in small-sample structural estimation and discuss the sensitivity of results to key modeling assumptions.