This study examines the threshold effect in the nonperforming loans–profitability nexus within the Nigerian banking industry. Using the innovative dynamic panel threshold of Seo, Kim, and Kim (2019), the work documents threshold levels of 3.5% and 5.0% of nonperforming loans for return on average assets (ROAA) and return on average equity (ROAE), respectively. These levels of nonperforming loans ensure equilibrium profitability without stability trade‐off in the industry. Similarly, the robust models suggest the threshold of 5.2% and 2.81% of impaired loans for optimal ROAA and ROAE, respectively. The results are important for policy formulations. It is recommended that the Central Bank of Nigeria (CBN) should review the 5% threshold nonperforming loans adopted in the industry in 2019 prudential guidelines to ensure stability in the Nigerian banking industry.

No abstract

No abstract

PurposeThis study investigates the nexus between the returns on oil prices (OP) and unemployment (UR) while taking into account the influences of two of the most representative measures of uncertainty, the Baker et al. (2016) and Caldara and Iacovello (2021) indexes of economic policy uncertainty (EP) and geopolitical risks (GP), in the relationship.Design/methodology/approachThe authors use data on the US, Canada, France, Italy, Germany and Japan from January 2000 to February 2022 and the UK from January 2000 to December 2021. The authors then apply the continuous wavelet transform (CWT), wavelet coherence (WC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) to examine the returns within a time and frequency framework.FindingsThe CWT tracks the movement and evolution of individual return series with evidence of high variances and heterogenous tendencies across frequencies that also align with critical events such as the GFC and COVID-19 pandemic. The WC reveals the presence of a bidirectional relationship between OP and UR across economies, showing that the two variables affect each other. The authors’ findings establish the predictive influence of oil price on unemployment in line with theory and also show that the variation in UR can impact the economy and alter the dynamics of OP. The authors employ the PWC and MWC to capture the impact of uncertainty indexes in the co-movement of oil price and unemployment in line with the theory of “investment under uncertainty”. Taking into account the common effects of EP and GP, PWC finds that uncertainty measures significantly drive the co-movement of oil prices and unemployment. This result is robust when the authors control for the influence of economic activity (proxied by the GDP) in the co-movement. Furthermore, the MWC reveals the combined intensity, strength and significance of both oil prices and the uncertainty measures in predicting unemployment across countries.Originality/valueThis study investigates the relationship between oil prices, uncertainty measures and unemployment under a time and frequency approach.HighlightsWavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.We account for uncertainty measures in the dynamics of oil prices and unemployment.We observe a bidirectional relationship between oil prices and unemployment.Uncertainty measures significantly drive oil prices and unemployment co-movement.Both oil prices and uncertainty measures significantly drive unemployment.

Given the cyclicality of energy and food commodity prices influenced by global macroeconomic uncertainties, there is a need to provide appropriate measures for understanding the predictive relationship between energy and food commodities. This study revisits the dynamics of oil and food prices using Shi et al. (J Financ Econom 18:158–180, 2020) bootstrapped time-varying Granger causality method to identify and date-stamp causal changes in the predictive effects between oil and food markets, while considering homoscedasticity and heteroscedasticity assumptions. Our results reveal bidirectional and feedback influences between Brent oil and six food commodity prices: corn, rice, sugar, coffee, meat, and palm oil. These influences align with critical global events such as the mid-1990s Asian financial crisis, the early 2000s recession, the 2000s energy crisis, the 2014 oil price crisis, the GFC and food crisis of 2008, the 2020 oil-price war, and the COVID-19 pandemic. Additionally, we observed a causal effect running from wheat and soybean prices to Brent oil prices, highlighting the importance of the predictive power of food prices in the trajectory of oil prices during periods of global events. Longer episodes of Granger causality from food price to oil price were date-stamped across the algorithms. The study suggests that global economic events and crises can affect the relationship between prices in different markets, indicating that the ability to predict prices based on information from another market may change during times of economic and financial instability. The research has a number of practical implications.