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

Adam Zawadzki
Adam Zawadzki
   | Oct 16, 2023
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Published Online: Oct 16, 2023
Page range: 275 - 286
DOI: https://doi.org/10.2478/ceej-2023-0016
Keywords
© 2023 Adam Zawadzki, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Top 10 of the highest and lowest NPL ratios in 2019

Country NPL as assets % Country NPL as assets %
Ukraine 46.8 Macao 0.2
Greece 39.4 South Korea 0.4
Cyprus 30.5 Micronesia 0.4
Angola 17.7 Hong Kong 0.7
Burundi 17.7 Luxembourg 0.8
Iraq 14.1 Australia 0.9
Moldova 13.1 Norway 1.0
Italy 12.9 United Kingdom 1.0
Albania 12.8 United States 1.1
Vanuatu 12.7 Singapore 1.2

Wu-Hausman test results

df df2 statistic p-value
Wu-Hausman 1 97.00 0.137 0.71

Correlation matrix

ln NPL INF UN DCTOPS DEBT GDP IR OECD CurDep
ln NPL 1
INF 0.30 1
UN 0.25 −0.20 1
DCTOPS −0.54 −0.35 −0.08 1
DEBT 0.33 0.02 0.14 0.08 1
GDP −0.11 −0.05 −0.13 0.00 −0.26 1
IR 0.27 0.41 −0.11 −0.47 −0.04 −0.02 1
OECD −0.28 −0.32 −0.01 0.34 0.13 −0.27 −0.48 1
CurDep 0.28 0.91 −0.07 −0.28 0.08 −0.10 0.27 −0.16 1

Data specification and symbols

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

VIF testing results

INF UN DCTOPS DEBT GDP IR OECD
vif(a) 1.3699 1.1468 1.4102 1.1042 1.2153 1.6610 1.5495
vif(b) 1.3278 1.1215 1.2615 1.1038 1.1885 X 1.3277
vif(c) 1.3531 1.1194 1.4102 1.0576 X 1.6245 1.3884
vif(d) 1.0817 1.3429 1.0971 X 1.2004 1.6100 1.4983
vif(e) 1.0280 1.1399 1.0400 X X X 1.1428

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

Variable Expected impact on NPL Basis of the NPL influence sign Relevant 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 rate − Unemployment 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 development − A 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 rate − Lower 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

Variable Model (a) Model (b) Model (c) Model (d) Model (e)
INF 0.1624 0.1499 0.1690 n/a n/a
(0.1526) (0.1783) (0.1328)
UN 0.0268* 0.0279* 0.0277* 0.0227 0.0243*
(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)
DEBT 0.0105*** 0.0104*** 0.01071*** 0.0107*** 0.0110***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
GDP −0.1737 −0.1430 n/a −0.2259 n/a
(0.5958) (0.6577) (0.4903)
IR −0.0075 n/a −0.0066 −0.0045 n/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 error 0.7050 0.7028 0.7024 0.7088 0.7037
White test p-value: 0.3654 0.3014 0.406 0.3185 0.2484
Goldfeld–Quandt test p-value: 0.9114 0.8307 0.9144 0.8789 0.8730
Multiple R-squared 0.4922 0.4901 0.4907 0.4814 0.4785
Adjusted R-squared 0.4559 0.4592 0.4598 0.45 0.4578
AIC 236.3774 234.8116 234.6833 236.6023 233.1925
BIC 260.3484 256.1191 255.9909 257.9098 249.1731
RESET 1.2344 (0.2956)
Shapiro 0.9841(0.2376)
Jarque Bera 3.0718 (0.2153)
Observations 106 106 106 106 106

Ramsey's RESET test results

RESET 5.3029
df1 2
df2 95
p value 0.0065

Descriptive statistics of variables

NPL INF UN DCTOPS DEBT GDP IR CurDep
Mean 6.44 1.60 8.37 61.40 53.60 1.24 10.16 1.31
Maximum 46.82 6.19 31.65 236.75 194.11 2.16 54.48 8.58
Minimum 0.20 1.00 0.71 3.23 2.58 0.79 0.52 0.76
Standard deviation 6.92 0.71 6.21 42.32 31.36 0.23 7.43 0.90
Skewness 3.38 3.92 1.55 1.37 1.40 0.89 2.78 6.20
Kurtosis 15.23 20.04 2.09 2.38 3.28 1.54 12.94 44.56
Jarque-Bera test 1.119.70 1.868 57.70 53.63 75.08 22.27 799.40 8,610.70
JB Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Number of observations 106 106 106 106 106 106 106 106
eISSN:
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Language:
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Business and Economics, Political Economics, Economic Theory, Systems and Structures, Microeconomics, Macroecomics, Economic Policy
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