Regulatory Arbitrage in EU Banking: Do Business Models Matter?

Ayadi, Rym; Ferri, Giovanni; Pesic, Valerio

doi:10.2139/ssrn.2829027

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…Equation (1) uses the following controls: Density, the ratio of RWAs over Total Assets, which is a measure of risk (Bruno et al, 2015, Melis andWeissenberg, 2019); Size, the log of Total As-sets; Z-score, defined by Cihák and Hesse (2007), Ayadi et al (2017) as LR+ROA σ(ROA) , where LR is the leverage ratio and ROA the return on assets; Profitability, the return on equity (ROE, in percent); SSM, a binary value set to one for banks that are subjected to the Single Supervisory Mechanism; Credit Growth defined as the relative change in credit exposure values over one year. The regression models rely on p-values that account for two-dimensional within-cluster correlation.…”

Section: Discretionary Buffers and Bank Characteristicsmentioning

confidence: 99%

Max headroom: Discretionary capital buffers and bank risk

Lubberink

2022

International Review of Financial Analysis

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Section: Discretionary Buffers and Bank Characteristicsmentioning

confidence: 99%

Max headroom: Discretionary capital buffers and bank risk

Lubberink

2022

International Review of Financial Analysis

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“…The retail model, characterized by a predominance of the loan portfolio and wide use of deposits as a primary source of financing, is defined by average levels of profitability and low-performance variability (Ayadi et al , 2016b).…”

Section: Literature Reviewmentioning

confidence: 99%

European banks’ business models as a driver of strategic planning: one size fits all

Palmieri

Geretto

Polato

2022

JFRC

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Purpose This paper aims to verify the presence of a management model that confirms or not the one size fits all hypothesis expressed in terms of risk-return. This study will test the existence of stickiness phenomena and discuss the relevance of business model analysis integration with the risk assessment process. Design/methodology/approach The sample consists of 60 credit institutions operating in Europe for 20 years of observations. This study proposes a classification of banks’ business models (BMs) based on an agglomerative hierarchical clustering algorithm analyzing their performance according to risk and return dimensions. To confirm BM stickiness, the authors verify the tendency and frequency with which a bank migrates to other BMs after exogenous events. Findings The results show that it is impossible to define a single model that responds to the one size fits all logic, and there is a tendency to adapt the BM to exogenous factors. In this context, there is a propensity for smaller- and medium-sized institutions to change their BM more frequently than larger institutions. Practical implications Quantitative metrics seem to be only able to represent partially the intrinsic dynamics of BMs, and to include these metrics, it is necessary to resort to a holistic view of the BM. Originality/value This paper provides evidence that BMs’ stickiness indicated in the literature seems to weaken in conjunction with extraordinary events that can undermine institutions’ margins.

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“…Equation 1uses the following controls: Density, the ratio of RWAs over Total Assets, which is a measure of risk (Bruno et al, 2015, Melis andWeissenberg, 2019); Size, the log of Total As-sets; Z-score, defined by Cihák and Hesse (2007), Ayadi et al (2017) as LR+ROA σ(ROA) , where LR is the leverage ratio and ROA the return on assets; Profitability, the return on equity (ROE, in percent); SSM, a binary value set to one for banks that are subjected to the Single Supervisory Mechanism; Credit Growth defined as the relative change in credit exposure values over one year. The regression models rely on p-values that account for two-dimensional within-cluster correlation.…”

Section: Discretionary Buffers and Bank Characteristicsmentioning

confidence: 99%

Discretionary Capital Buffers and Bank Risk

Lubberink¹

2020

Preprint

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This paper examines the association between discretionary capital buffers, capital requirements, and risk for European banks. The discretionary buffers are banks' own buffers, or headroom: the difference between reported and required capital. I exploit capital requirements data that banks started to disclose since the release of a 2015 European Banking Authority opinion. Results using detailed SREP and Pillar 2 data of the largest 99 European banks over 2013-2019 show that less headroom is associated with increased bank risk. An additional examination reveals a positive association between headroom and stress test results for banks subjected to the Single Supervisory Mechanism, a result that runs against supervisory requirements.

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Regulatory Arbitrage in EU Banking: Do Business Models Matter?

Cited by 14 publications

References 16 publications

Max headroom: Discretionary capital buffers and bank risk

Max headroom: Discretionary capital buffers and bank risk

European banks’ business models as a driver of strategic planning: one size fits all

Discretionary Capital Buffers and Bank Risk

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