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Nonperforming loan of European Islamic banks over the economic cycle

  • S.I. : Risk Management Decisions and Value under Uncertainty
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Abstract

This paper investigates the variation in nonperforming loans over the economic cycle and the effect of past returns based on a nonparametric quantile analysis of the largest Islamic banks in the United Kingdom and Turkey from 2010 to 2019. The findings show a weak variation in nonperforming loans that increases with an increasing return on assets and a decreasing return on equity and decreases in an inverse scenario. As a result, the credit risk of Islamic banks is countercyclical. We suggest that the inverse relationships evidence the existence of trade-offs within bank returns and credit risk. Thus, banks’ past profitability and risk mitigation are determinants of asset quality. These findings provide support for risk-taking and risk-sharing principles in which flight-to-safety mirrors the calibration of risk factors in a disruptive economy. Our estimates indicate that nonparametric quantile regression captures considerably more variation in a risk-return analysis.

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Notes

  1. Existing studies on the correlation between nonperforming loans and bank- and country-level factors (e.g. Krüger & Rösch, 2017; Us, 2017; Konstantakis et al., 2016; Zhang et al., 2016; Vithessonthi, 2016; Dimitrios et al., 2016) find controversial results. In fact, there is no consensus on the efficient management of nonperforming loans or on their contribution to system risk. This study highlights the variation in nonperforming loans at a quantile scale as an alarming indicator of the overall health of a bank and a key credit risk indicator. Meanwhile, asset and equity returns are receptive of economic disruptions.

  2. GDP growth reflects the cyclical behavior of banking nonperforming loans, and returns on assets and equity measure banks’ profitability. This paper provides evidence that the relationship between bank returns and credit risk is inverted during economic turns. We find a weak variation in nonperforming loans that increases with an increasing return on assets and a decreasing return on equity and decreases in an inverse scenario.

  3. See, for instance, Beck et al. (2013a, 2013b), Zins and Weill (2017) and Anginer et al. (2018).

  4. Although the institutional environments in the UK and Turkey are not similar, our selected Islamic banks are comparable in terms of business lines, asset growth, credit risk and returns.

  5. The general agreement is that Islamic banking activities are approved by the Shariah board, since Shariah principles are essentially based on a prohibition of excessive risk-taking and Riba, risk-sharing and equity-oriented (Doumpos et al., 2017; Imam & Kpodar, 2016; Mollah & Zaman, 2015).

  6. Islamic banks were considered as a safe refuge from global financial turbulence by the OECD (2009) during the 2008 financial crisis.

  7. For instance, Adrian et al. (2019) argue that the risk-return trade-off does exist when the relationship between risk measure and returns exhibits a “mirror image” effect or inverse relationships.

  8. The European Banking Authority Report (2019) “Accounting and auditing” recommends high-quality accounting and auditing to synthesize standards and support economic growth.

  9. The period was extended to cover annual data from 2010 to 2019 in Sect. 4.3., Nonparametric quantile regression versus ordinary least squares.

  10. Based on the average annual conversion rate, 1 lb sterling equaled 1.27 United States Dollars in 2019.

  11. Based on the average annual conversion rate, 1 Turkish Lira equaled 0.17 United States Dollar in 2019.

  12. According to Machado and Silva (2013), it should be noted that both the MSS test and the test proposed by Koenker and Bassett (1978) check not only for heteroskedasticity but also for other departures from the assumption that the errors are identically distributed.

  13. The kernel local linear estimator allows edge bias to be avoided (Su et al., 2009).

  14. Additional figures plottting data by country and by bank are available in “Appendices A2, A3, and A4”.

  15. The iqreg command performs interquantile range regression (regression in different quantiles). By default, the quantiles (0.25, 0.75) produce interquantile range estimates and Bootstrap standard errors are produced. The sqreg command produces QR estimates for several quantile values simultaneously, giving differences between QR coefficients in different quantiles; Bootstrap standard errors are also produced.

  16. Yao et al. (2020) conclude that the level of green efficiency is related to geographical location, and innovation also has significant effect. However, efficiency and technical leadership in China require improvements.

  17. For instance, a report by Reuters (2017) explains how a “London-based Islamic financial technology start-up has become the first company of its kind to be given regulatory approval in the UK, as Britain seeks to position itself as a hub for both fintech and Islamic finance.”

  18. In this respect, Imam and Kpodar (2016) summarize four major mechanisms of Islamic banks as follows: “(1) all forms of riba (interest paid on loans) are prohibited; (2) Islamic banking prohibits maysir (games of chance) and gharar (chance) rhat means that derivative products are not permitted. (3) Islamic banking services and products are subject to a code of conduct that prohibits the financing of haram (illegal) activities, activities deemed to have a negative impact on society or forbidden by Islamic law. (4) Islamic banks have to redistribute part of their profits to society in the form of ‘zakat.’”

  19. The testing sample includes fully Shariah-compliant four banks operating in Turkey and five in the United Kingdom. Thus, we expect that the level of risk taking on credit as measured by the increase in nonperforming loans year-over-year is not significantly volatile, which improves the reliability of our estimates.

  20. Figure 7 presents the distribution of estimated coefficients per quantile of Table 5 in “Appendix A5”.

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Acknowledgements

The authors thank the editor and an associate editor of Annals of Operations Research and the two anonymous referees for their helpful comments and suggestions. We have read the Annals of Operations Research disclosure policy and have no conflicts of interest to disclose.

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Authors and Affiliations

  1. Ipag Business School (Finance Department), Paris, France

    Faten Ben Bouheni

  2. European Business School - Paris/INSEEC U Research Center, Paris, France

    Hassan Obeid

  3. National Bank of Moldova, Chisinau, Moldova

    Elena Margarint

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  1. Faten Ben Bouheni
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Appendices

Appendix

Appendix A1

See Table 7.

Table 7 Islamic banks in Turkey and the UK.
Full size table

Appendix A2

See Fig. 7.

Fig. 7
figure 7

Data presentation from 2010 to 2019 for the UK and Turkey. Note: This figure presents the variability of the candidate variables: GDP growth rate, change in nonperforming loans (ΔNPL), lagged one-period returns on assets and returns on equity (Lag ROA and Lag ROE), Bank ROA and Bank ROE. In all cases, returns on equity (ROE) have the most pronounced variability, which corresponds to the variables Lagged ROE and Bank ROE. The bottom right of Fig. 4 shows that the level of ΔNPL moves at a similar magnitude to Lag ROA. The sample covers the nine largest Islamic banks operating in the UK and Turkey from 2010 to 2019

Full size image

Appendix A3

See Fig. 8.

Fig. 8
figure 8

Distribution of candidate variables by country and by bank from 2010 to 2019. Note: This figure plots the variability of the candidate variables: GDP growth rate, change in nonperforming loans (ΔNPL), lagged one-period returns on assets and returns on equity (Lag ROA and Lag ROE), and the interactions between returns and GDP growth (LagROA*GDP growth and LagROE*GDP growth). In all cases, returns on equity (ROE) and their interaction with the economic cycle (LagROE*GDP growth) have the most pronounced variability, shown in yellow and green. While the interaction term LagROE*GDP growth is highly negative for Islamic banks in the UK, it is highly positive for Islamic banks in Turkey, which could be explained by the higher GDP growth year-on-year in Turkey as compared to the UK. The testing sample includes the nine largest Islamic banks operating in the UK and Turkey during the 2010–2019 period

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Appendix A4

See Fig. 9.

Fig. 9
figure 9

Distribution of change in nonperforming loan values per quantile from 2010 to 2019. Note: This figure plots the distribution of the dependent variable NPL per quantile on the left side of the figure and per year on the right side. Change in nonperforming loans has a tendency to decline before its median at the 0.6th quantile then increase at higher quantiles

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Appendix A5

See Fig. 10.

Fig. 10
figure 10

Estimated coefficient distribution per quantile for candidate variables. Note: This figure presents the estimated coefficient distribution per quantile from 2010 to 2019. Credit risk increases with rising past returns on assets (Lag ROA) and declining past returns on equity (Lag ROE)

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Ben Bouheni, F., Obeid, H. & Margarint, E. Nonperforming loan of European Islamic banks over the economic cycle. Ann Oper Res 313, 773–808 (2022). https://doi.org/10.1007/s10479-021-04038-8

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