Economic uncertainty, asset prices and bank credit growth: a Bayesian analysis

Credit Deepening and Bank Asset Quality: Dynamic Early-Warning Evidence from 58 Countries

This study examines whether the accumulated stock of private credit provides early-warning information for subsequent deterioration in banking-sector asset quality. It combines annual Passport banking indicators with World Development Indicators for 58 countries over 2010–2024; the preferred sample contains 746 country–year observations. A second-order dynamic fixed-effects model links log(1 + NPL), where NPL denotes the non-performing loan ratio, to lagged private credit to gross domestic product (GDP), real credit growth, lending rates, bank capital, GDP growth, inflation, and unemployment. Its preferred credit-depth coefficient is 0.00377, implying that a 10-percentage-point increase is associated with approximately 0.15 percentage points more NPLs one year later at the sample median. To operationalize early-warning calibration without claiming a universal cutoff, the paper reports the sample credit-depth quartiles and estimates a country fixed-effects linear probability model using the European Banking Authority’s 5% gross-NPL supervisory trigger. In that alternative outcome, a 10-percentage-point increase in credit depth is associated with a 2.78-percentage-point higher conditional probability of NPLs reaching 5% or more (p = 0.002). On a strictly common 609-observation sample, the credit-depth coefficients at one-, two-, and three-year horizons are 0.00501, 0.00960, and 0.01266. Lending rates and unemployment are positive, whereas annual credit growth and capital ratios are not robust predictors. Pooled interactions do not reject equal slopes across broad country partitions. System generalized method of moments (GMM) passes conventional tests but violates a persistence-bound credibility check. The evidence supports an early-warning interpretation, not a causal claim.

Anticipating the Credit Cycle: the Predictive Role of Lending Standards in Brazil

Identifying credit supply shocks separately from demand shocks remains a central challenge in the empirical credit-channel literature, particularly in emerging economies. We use the Brazilian Central Bank's Quarterly Credit Conditions Survey (PTC), launched in 2011, which records the lending standards reported by financial institutions and provides a direct measure of credit-supply conditions independent of price. We embed this variable in a hierarchical Bayesian VAR estimated on quarterly data for 2011Q1–2025Q4. A tightening in lending standards is followed by a sustained contraction in non-earmarked credit, and standards account for about a quarter of credit’s forecast-error variance at the twelve-quarter horizon. Impulse responses, variance decomposition, and Granger tests point in the same direction. Surveys like the PTC provide an early indicator of credit supply conditions, with implications for the conduct of monetary policy in emerging economies.

Evaluating the Determinants of Credit Growth in Banks Across Western Balkan Countries: A Panel Data Approach

This study investigates the determinants of credit growth in Western Balkan countries over the period 2011–2023, assessing whether lending dynamics are driven by macroeconomic fundamentals or financial sector conditions. The analysis focuses on key variables, including GDP growth, foreign direct investment (FDI), inflation, and lending interest rates. Using a balanced panel dataset, the study employs pooled Ordinary Least Squares (OLS), fixed and random effects models, and a two-way fixed effects specification with Driscoll–Kraay standard errors to address cross-sectional dependence and unobserved heterogeneity. The empirical results show that lending interest rates exert a statistically significant negative effect on credit growth, indicating that financial conditions play a central role in constraining lending activity. In contrast, GDP growth and inflation are not found to be significant determinants, challenging conventional macro-financial expectations. FDI becomes significant only when introduced in a lagged specification, suggesting a delayed transmission mechanism through which external capital inflows influence credit expansion. The study contributes to the literature by providing comparative multi-country evidence from structurally constrained and bank-dominated financial systems. The findings suggest that credit growth is driven less by traditional macroeconomic factors and more by financial sector conditions and institutional characteristics. These results have important policy implications, highlighting the need to strengthen financial intermediation efficiency and credit transmission mechanisms rather than relying solely on macroeconomic expansion to stimulate lending.

Impact of monetary policy on bank liquidity creation: Economic policy uncertainty channel

This study examines how monetary policy affects bank liquidity creation in the U.S. from 2001 to 2022. Utilizing a dynamic panel Generalized Method of Moments (GMM) estimator on quarterly bank-level data, the findings indicate that monetary policy exerts a significant negative effect on bank liquidity creation. Crucially, the results demonstrate that economic policy uncertainty (EPU) acts as a key transmission channel through which monetary policy influences the banking sector’s output. This adverse effect is particularly pronounced during periods of negative output gaps, suggesting that policy-induced uncertainty significantly dampens the supply of credit during specific economic cycles. These results remain robust across alternative measures of bank liquidity creation, including both category- and maturity-based indices. These findings offer important policy implications, highlighting the need for central banks to account for the mediating role of uncertainty when designing monetary interventions to mitigate shocks to the banking sector.

Economic policy uncertainty and bank wholesale funding: Does the quality of political signals matter?

Energy price shocks and bank credit: sectoral multi-horizon evidence from a resource-rich economy

This paper examines the dynamic relationship between energy price volatility and sectoral bank credit allocation in Qatar. It identifies how energy price shocks are associated with credit dynamics across sectors and time horizons and assesses the implications for financial stability in a hydrocarbon-dependent economy. The study employs wavelet coherence (WTC) analysis on monthly data from February 2009 to December 2023 to capture time–frequency linkages and lead–lag dynamics between energy prices and bank credit across aggregate and sectoral credit categories – public sector, real estate, general trade, consumption, services, and other sectors. The sample period spans major economic shocks, including the 2014–2016 oil price collapse, the 2017 diplomatic blockade, the COVID-19 pandemic, and the 2022 FIFA World Cup. The results reveal pronounced sectoral and temporal heterogeneity in the energy–credit relationship. Public sector credit exhibits persistent high-magnitude, low-frequency coherence with energy prices, reflecting structural dependence on hydrocarbon revenues. General trade displays a countercyclical pattern, with credit growth strengthening during episodes of declining energy prices. Short-term coherence with aggregate credit emerges primarily during periods of economic distress, while medium- and long-term dynamics are driven by fiscal policy transmission and bank risk-taking behavior. Real estate credit shows conditional rather than absolute resilience, with significant short- and medium-term responses during 2014–2015 and 2018–2020 despite limited long-horizon sensitivity. This study provides sector-level, time-frequency evidence on the energy–credit nexus in a hydrocarbon-dependent economy – a context underexplored in the literature. By decomposing credit dynamics across multiple horizons, it uncovers heterogeneous sectoral responses that aggregate and time-invariant approaches commonly obscure.