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Macroeconomic Determinants of Credit Risk on the Example of Non-performing Loans Cover

Macroeconomic Determinants of Credit Risk on the Example of Non-performing Loans

By: Adam ZawadzkiORCID  
Open Access
|Oct 2023

Figures & Tables

Top 10 of the highest and lowest NPL ratios in 2019

CountryNPL as assets %CountryNPL as assets %
Ukraine46.8Macao0.2
Greece39.4South Korea0.4
Cyprus30.5Micronesia0.4
Angola17.7Hong Kong0.7
Burundi17.7Luxembourg0.8
Iraq14.1Australia0.9
Moldova13.1Norway1.0
Italy12.9United Kingdom1.0
Albania12.8United States1.1
Vanuatu12.7Singapore1.2

Wu-Hausman test results

dfdf2statisticp-value
Wu-Hausman197.000.1370.71

Correlation matrix

ln NPLINFUNDCTOPSDEBTGDPIROECDCurDep
ln NPL1
INF0.301
UN0.25−0.201
DCTOPS−0.54−0.35−0.081
DEBT0.330.020.140.081
GDP−0.11−0.05−0.130.00−0.261
IR0.270.41−0.11−0.47−0.04−0.021
OECD−0.28−0.32−0.010.340.13−0.27−0.481
CurDep0.280.91−0.07−0.280.08−0.100.27−0.161

Data specification and symbols

VariableSymbol
Non-performing loan ratio in 2019NPL
Natural logarithm of non-performing loan ratio in 2019ln_NPL
Inflation rate is expressed as an index in 2019, where the base year is 2008INF
Unemployment rate is expressed as the average unemployment rate in 2009–2019UN
Banking sector development indicator as a relation between total loans granted to the private sector and GDP in 2019DCTOPS
State debt as a ratio of public debt to GDP in 2019DEBT
GDP growth rate as a relation of the value of GDP expressed in constant prices to the national currency in 2019 to 2008GDP
Interest rate as the average interest rate on loans granted in 2009–2019IR
OECD membership (dummy variable)OECD
Currency depreciation as a ratio of the value of the currency expressed in SDRs in 2019 in relation to 2008CurDep

VIF testing results

INFUNDCTOPSDEBTGDPIROECD
vif(a)1.36991.14681.41021.10421.21531.66101.5495
vif(b)1.32781.12151.26151.10381.1885X1.3277
vif(c)1.35311.11941.41021.0576X1.62451.3884
vif(d)1.08171.34291.0971X1.20041.61001.4983
vif(e)1.02801.13991.0400XXX1.1428

Description of the determinants and proxies of NPLs and a representative sample of their use in the literature

VariableExpected impact on NPLBasis of the NPL influence signRelevant literature
Inflation rate+The state of high inflation hinders running a business and reduces the ability of households to settle their liabilities.Curak et al., 2013; Klein, 2013; Kjosevski & Petkovski, 2021
Unemployment rateUnemployment reduces the ability of households to pay their liabilities.Klein, 2013; Messai & Jouini, 2013; Wdowiński, 2014; Louzis et al., 2015; Kjosevski & Petkovski, 2021
Banking sector developmentA large number of loans means greater availability of financing. In addition, the high value of loans means that banks are mature and can properly assess creditworthiness.Keeton & Morris, 1987;Abid & Zouari, 2014; Petkovski et al., 2021
State debt+High state debt limits the availability of financing, which contributes to higher loan-servicing costs.Louzis et al., 2015; Kjosevski & Petkovski, 2021
GDP growth rateLower GDP growth means limited ability to settle liabilities.Messai & Jouini, 2013; Wdowiński, 2014; Beck et al., 2015; Louzis et al., 2015; Kjosevski & Petkovski, 2021
Interest rate+/−A high interest rate may limit lending, i.e., only those entities with high creditworthiness receive loans. On the other hand, the high cost of money may make it difficult to settle liabilities.Curak et al., 2013; Messai & Jouini, 2013; Wdowiński, 2014; Beck et al., 2015
Currency depreciation+The depreciation of the domestic currency means an increase in loans in foreign currency. This is important in the case of loans with a significant value in a foreign currency.Klein, 2013; Wdowiński, 2014; Beck et al., 2015

Regression statistics for the assessment of non-performing loans

VariableModel (a)Model (b)Model (c)Model (d)Model (e)
INF0.16240.14990.1690n/an/a
(0.1526)(0.1783)(0.1328)
UN0.0268*0.0279*0.0277*0.02270.0243*
(0.0258)(0.0187)(0.0190)(0.0520)(0.0318)
DCTOPS−0.0110***−0.0106***−0.0110***−0.0116***−0.0114***
(0.0000)(0.0000)(0.0000)(0.0000)(0.0000)
DEBT0.0105***0.0104***0.01071***0.0107***0.0110***
(0.0000)(0.0000)(0.0000)(0.0000)(0.0000)
GDP−0.1737−0.1430n/a−0.2259n/a
(0.5958)(0.6577)(0.4903)
IR−0.0075n/a−0.0066−0.0045n/a
(0.5274)(0.5744)(0.7007)
OECD−0.3360−0.2906−0.3035−0.3856*−0.3268*
(0.0789)(0.0992)(0.0921)(0.0419)(0.0468)
p-value:0.0000***0.0000***0.0000***0.0000***0.0000***
Residual standard error0.70500.70280.70240.70880.7037
White test p-value:0.36540.30140.4060.31850.2484
Goldfeld–Quandt test p-value:0.91140.83070.91440.87890.8730
Multiple R-squared0.49220.49010.49070.48140.4785
Adjusted R-squared0.45590.45920.45980.450.4578
AIC236.3774234.8116234.6833236.6023233.1925
BIC260.3484256.1191255.9909257.9098249.1731
RESET1.2344 (0.2956)
Shapiro 0.9841(0.2376)
Jarque Bera3.0718 (0.2153)
Observations106106106106106

Ramsey's RESET test results

RESET5.3029
df12
df295
p value0.0065

Descriptive statistics of variables

NPLINFUNDCTOPSDEBTGDPIRCurDep
Mean6.441.608.3761.4053.601.2410.161.31
Maximum46.826.1931.65236.75194.112.1654.488.58
Minimum0.201.000.713.232.580.790.520.76
Standard deviation6.920.716.2142.3231.360.237.430.90
Skewness3.383.921.551.371.400.892.786.20
Kurtosis15.2320.042.092.383.281.5412.9444.56
Jarque-Bera test1.119.701.86857.7053.6375.0822.27799.408,610.70
JB Probability0.00000.00000.00000.00000.00000.00000.00000.0000
Number of observations106106106106106106106106
DOI: https://doi.org/10.2478/ceej-2023-0016 | Journal eISSN: 2543-6821 | Journal ISSN: 2544-9001
Language: English
Page range: 275 - 286
Published on: Oct 16, 2023
Published by: Faculty of Economic Sciences, University of Warsaw
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Adam Zawadzki, published by Faculty of Economic Sciences, University of Warsaw
This work is licensed under the Creative Commons Attribution 4.0 License.