Forecasting the macro determinants of bank credit quality: a non-linear perspective

Abstract

Purpose This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability. Design/methodology/approach Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach. Findings Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017. Research limitations/implications The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique. Practical implications The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans. Originality/value The authors show a clear outperformance of the MS model concerning the linear one.

Duty calls: prediction of failure in reorganization processes

“…Macroeconomic conditions have been identified in several studies as a factor to be taken into account. In particular, Fallanca et al . (2020) cite the macroeconomic climate as a key determinant in predicting bank credit vulnerability; Afzal and Firdousi (2022) show that the cost of bank capital is influenced by GDP growth; Berninger et al .…”

Green Banking—Can Financial Institutions support green recovery?

“…The first relates to profitability, and the second is linked with the risk exposure. Many studies, notably ( Robin et al, 2018 , Moradi-Motlagh and Jubb, 2020 , Cincinelli and Piatti, 2021 , Fallanca et al, 2020 ), argue that banking performance is a function of their earnings yield and risk exposure. In line with these propositions, we opt for banking spread as an indicator of profitability and default likelihood as the measure of risk.…”

“…The metric is based on Merton (1974) and provides an ex-ante estimate of the distance to default. While there are multiple ways to proxy banking risks like tail risk ( Prorokowski et al, 2020 ), infection ratio ( Fallanca et al, 2020 , Ozili, 2019 ), etc., the default likelihood is considered to be more robust as it incorporates the market value of assets. The market values matter most from the default perspective ( Li et al, 2020b , Yu et al, 2022 ).…”

Do the Determinants of Non-Performing Loans Have a Different Effect over Time? A Conditional Correlation Approach

“…A suitable proxy to represent the NPLs is the delinquency rate (del hereafter), frequently used in the study of credit risk (Fallanca et al 2020). We drew our data from the Federal Reserve Board's website, which every quarter provides the ratio of loans and leases from US commercial banks more than 30 days past due to the total number of loans and leases.…”