Abstract
Several studies have explored the linkage between non-performing loans and major macroeconomic indicators, using a wide variety of methodologies, sometimes with different results. This occurs, we argue, because these relationships are generally derived in terms of correlation coefficients evaluated in certain time spans, which represent a sort of average level of correlations. However, such correlations are necessarily time-varying, because the relationships between bank loan indicators and macroeconomic variables could be stronger during particular periods or in correspondence with important economic events. We propose an empirical exercise using dynamic conditional correlation models, with constant and time-varying parameters. Applying these models to quarterly delinquency rates and an array of macroeconomic variables for the US, for the period 1985–2019, we find that the correlation is often negligible in this period except during periods of economic crises, in particular the early 1990 crisis and the subprime mortgage crisis.
Highlights
Management 14: 21. https://doi.org/The strong relationship between bank loan quality and macroeconomic variables is undisputed
To detect the existence of a time-varying correlation between Non-Performing Loans (NPLs) and individual macroeconomic variables, we adopt a class of multivariate models widely used in the analysis of financial time series, the Dynamic Conditional Correlation (DCC) model proposed by Engle (2002)
M2; conditional correlations depending on news, characterizing the dynamics of the correlation of del with Consumer Price Index (CPI), UN, treasury bills (TB); persistent conditional correlation, present in the pairs del-Gross Domestic Product (GDP) and del-Standard & Poor’s index (S&P)
Summary
The strong relationship between bank loan quality and macroeconomic variables is undisputed. Gross Domestic Product (GDP) growth, unemployment, inflation, interest rates, stock prices, and real estate prices are commonly considered to affect NPLs, either individually or in combination with bank-specific variables (see the recent literature reviews by Manz 2019; Naili and Lahrichi 2020; Nikolopoulos and Tsalas 2017). The detection of these relationships is dependent on the methodology adopted, the samples, the geographical settings, etc.
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