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Financial stability, liquidity risk and income diversification: evidence from European banks using the CAMELS–DEA approach

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Abstract

Liquidity risk was at the heart of 2007–2008 global financial crisis, which has led to a series of financial institutions failure. We test whether and how liquidity risk impacts European banks’ stability (i.e., a bank risk-return profile) under different levels of engagement in non-traditional banking activities after the global financial crisis and during the implementation of the Basel III liquidity rules. To calculate financial stability, we adopt an efficiency perspective based on the combination of the CAMELS rating system with the data envelopment analysis technique. We implement a nonlinear panel smooth transition regression approach, where transitional factors of income diversification are endogenously captured from the data. We find that, liquidity risk stemming from liquidity creation has a positive impact on bank stability, implying that income diversification can serve as a “buffer” through which banks can ensure their liquidity creation and offset for the compression of intermediation margin in lending and deposit activities. This suggests that diversification does not impede the ability of banks to operate with lower liquidity holdings but allows them to make greater use of their balance sheets to fulfill their primary roles of credit provision and liquidity creation. The results offer interesting implications for regulators and bank managers in managing liquidity risk.

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Notes

  1. In this paper, bank stability is conceived as the opposite of bank distress and failure.

  2. In this study bank stability refers to the risk-return profile of a bank. A bank is called stable when it is financially profitable and less risky.

  3. CCR and BCC are the initial DEA models that were developed by Charnes et al. (1978) and Banker et al. (1984). While the former considers the constant return to scale (CRS) assumption, the latter overcomes this drawback and introduce the variable return to scale (VRS).

  4. Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Liechtenstein, Lithuania, Malta, Monaco, Netherlands, Norway, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom.

  5. For more details about the procedure please refer to González et al. (2005, 2017).

  6. For more details on these two tests, please refer to González et al. (2017)

  7. The PSTR specification procedures are based on the assumption that all variables in Eq. (1) are I(0) stationary in level (González et al., 2005, 2017).

  8. HAC stands for Heteroskedasticity and Autocorrelation Consistency.

  9. b1 = 0.0025 in the first regime and (b1 + b2) = 0.0012 in the second regime.

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Authors and Affiliations

  1. IPAG Business School Paris, Paris, France

    Béchir Ben Lahouel

  2. Ecole supérieure des sciences économiques et commerciales de Tunis, Université de Tunis, Tunis, Tunisia

    Lotfi Taleb

  3. EDC Paris Business School – Paris, Courbevoie, France

    Younes Ben Zaied

  4. Kyushu University, Fukuoka, Japan

    Shunsuke Managi

  5. Laboratoire LARIMRAF LR21ES29 Ecole supérieure de commerce de Tunis, Université de la, Manouba, Tunisia

    Lotfi Taleb

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  1. Béchir Ben Lahouel
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [BBL], [LT], [YBZ], and [SM]. The current version of the manuscript was written and approved by all authors.

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Correspondence to Younes Ben Zaied.

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Ben Lahouel, B., Taleb, L., Ben Zaied, Y. et al. Financial stability, liquidity risk and income diversification: evidence from European banks using the CAMELS–DEA approach. Ann Oper Res 334, 391–422 (2024). https://doi.org/10.1007/s10479-022-04805-1

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