Bank size and capital: A trade-off between risk-taking incentives and diversification
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DOIhttp://dx.doi.org/10.21511/bbs.17(4).2022.01
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Article InfoVolume 17 2022, Issue #4, pp. 1-11
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This paper analyzes the importance of size and capital for risk-taking incentives of Jordanian banks using panel data of 13 commercial banks for the period 2007–2017. The results reveal that size and capital add to stability, consistent with the economies of scale and scope hypothesis. In developing countries, banks are more conservative and less involved in market-based activities; however, they are interconnected just as in developed countries. The results of the first model and second model reveal that as size increases by 1 percent, risk decreases by 0.11 percent and 0.03 percent, respectively, implying that too-big-to-fail is not present and that moral hazard is not a serious issue. In both models, large size is driven by diversification not by risk-taking incentives. In terms of capital, the results of the first model and second model reveal that as capital increases by 1 percent, risk decreases by 0.48 and 0.12 percent, respectively. The fact that Jordanian banks are overcapitalized indicates that the central bank regulation is not binding. Banks increase their capital adequacy ratios to reduce risk. It is clear that there is economic benefit from increased size. However, the failures of large banks are systemic due to their interconnectedness. Therefore, regulators need to pay special attention to them in accordance with Basel III Accord.
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JEL Classification (Paper profile tab)G11, G20, G21, G28
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References49
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Tables5
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Figures4
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- Figure 1. Bank size (Ln assets) and credit risk (LLP)
- Figure 2. Bank size (Ln assets) and total risk (RWATA)
- Figure 3. Capital adequacy ratio (CAR1) and credit risk (LLP)
- Figure 4. Capital adequacy ratio (CAR1) and total risk (RWATA)
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- Table 1. Variable definition
- Table 2. Descriptive statistics
- Table 3. Correlation coefficient matrix
- Table 4. Panel regression results of the model (RWATA)
- Table 5. Panel regression results of the model (LLP)
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