Regulatory reform and banking diversity: reassessing Basel  3

The theme of banks’ business models (BMs) has been increasingly emphasised in recent years for various reasons, among which the international financial and economic crisis and the reform of the capital adequacy framework (Basel 3). The resulting prudential regulation aims to improve the banking sector’s ability to absorb shocks arising from financial and economic stress, thus trying to reduce spillover from the financial sector to the real economy, improve risk management and governance, and strengthen banks’ transparency and disclosure (BCBS 2011, 2013, 2014).

The basic question is whether Basel 3 has achieved its key aim of reducing excess risk taking and ensuring banks’ stability independently of business focus (banks’ specialisation or business model). Our paper contributes in this respect. Indeed, while Basel 3 is multifaceted, featuring four chief regulatory innovations—capital requirement (CR), precisely capital buffer, leverage ratio (LR), liquidity coverage ratio (LCR), and net stable funding ratio (NSFR)—most studies have focused on just one or two of the four innovations failing to capture the overall thrust of Basel 3 on banks’ risk taking (e.g., Bordeleau and Graham 2010; Blundell-Wignall et al. 2013; Dietrich et al. 2014; Vázquez and Federico 2015). Thus, our first task is offering a comprehensive analysis of the impact of Basel 3 by considering whether and how all four innovations jointly affect banks’ riskiness.

In addition, over time, banks’ business conduct has changed, reflecting both “voluntary” strategic management decisions and the uncertain international scenario. In unison, experiences from the crisis show that banks’ soundness may also be undermined by their type of activity. This makes it even more crucial understanding the business model banks adopt as a strategic tool to improve efficiency and competitiveness as well as stability, thereby sustainably contributing to economic growth. That issue must be weighed also considering the role Basel 3 innovations play in reshaping BMs. For example, many banks downsized their securitization exposures, particularly affected by the new regulatory requirements, while others reduced certain lending activities in high-risk sectors (e.g., commercial real estate) or curbed volatile wholesale funding. In other words, progress in reshaping BMs is continuing and will advance further along a refocusing on core activities and markets.

Nonetheless, if on one side Basel 3 (in addition to other internal and external factors, e.g. weak profitability and overall economic conditions) affects banks’ BMs, on the other side a key question is whether it can reach its goals, in primis ensuring banks’ overall soundness. Our paper explores this line of research by analysing whether and how strongly the Basel 3 requirements have different impacts on the stability of banks with different specialisation. Indeed, we argue that banking diversity, which may be expressed in terms of banks’ size (small versus large), ownership (Shareholder Value—SHV—vs. Stakeholder Value—STV—orientation) and business focus (retail versus investment banking), was disregarded by Basel 3 regulators, as it is a “one-size-fits-all” regulatory paradigm in Europe where, differently from the US, all banks must comply with such regulatory framework.

Based on the widely-held belief that Basel 3 does not sufficiently consider banks’ diversity (Altunbas et al. 2011; Ayadi et al. 2012), we focus on the consequences—in terms of stability, expressed by the z-score indicator—of complying to the prudential framework by banks working under various BMs. As different BMs may entail different risk structures, we ask whether it may be appropriate to reflect this in regulation by specifying different requirements to better align with banks’ business characteristics (Ayadi et al. 2012).

Existing studies on the effects of Basel 3 on the risk of banks with different BMs are largely based on the analysis of one or more regulatory measures; other studies focus on the relationship between regulation and BMs and on BMs and risk (see Sect. 2). However, to the best of our knowledge, the studies focusing on the mix of regulation—business model—stability are still scarce and differ from our approach because they do not examine the Basel four innovations jointly.

This paper contributes to the existing literature for various reasons.

Firstly, our analysis assesses the impact that Basel 3 requirements produce on the z-score, as a proxy for banks’ stability, when banks’ BMs are considered. This is the main contribution of our paper, whose insights should be crucial for regulators and policymakers. By examining the impact of Basel 3 requirements on the stability of banks with different BMs, our paper broadly contributes to gauge whether the current prudential framework may achieve its goal of promoting financial stability irrespective of each bank’s business model. Hence, we propose that the “one-size-fits-all” regulatory paradigm should be reviewed, considering also banks’ business features, to enhance the overall effectiveness of the current framework in ensuring financial stability (e.g., Kupiec 2007; Kasselaki and Tagkalakis 2014).

Moreover, we use a longer time series (2000–2014) than other studies (e.g., Ayadi et al. 2012; Blundell-Wignall and Roulet 2013) and split it into two subperiods (2000–2010 vs. 2011–2014) to control for Basel 3 regulatory effect and thereby show the evolution of the relationship among Basel 3, BMs and stability. Our test of the differential impact of the new Basel 3 measures on banking soundness is run on the subsample ot the years 2011–2014, after the new Accord was finalised, to see the immediate effect of the measures. However, we present also the estimations on the subsample 2000–2010 as it helps to see through the eyes of the regulator when deciding the measures and to test the robustness of our results.

Also, while other studies focus on big banks only (e.g., Altunbas et al. 2011; Blundell-Wignall and Roulet 2013), our large sample sheds light on banks of different size (large, medium and small banks), helping to gauge the impact of Basel 3 standards by controlling for a rich set of attributes.

Lastly, we focus on the Eurozone, which is particularly interesting in light of its increasing uniformity and comparability in terms of supervision. This allows us to limit institutional heterogeneity across our banks. At the same time, however, referring to this area makes our results in part potentially influenced also by the coinciding sovereign debt crisis and the policies by the ECB to ease the Eurozone financial conditions.¹

The remainder of the paper is organized as follows. Section 2 hosts a concise general review of the relevant literature on banks’ BMs. Section 3 describes the sample used in the analysis. In Sect. 4, we justify dependent and explanatory variables, supporting our selection with specific references to the literature. Section 5 presents the methodology. Section 6 provides the empirical results, and Sect. 7 reports our robustness tests. Finally, Sect. 8 concludes.

Lately, business models (BMs) analysis has gained new traction in banking.

Some studies focus on the link between banks’ BMs and stability, measured—as for us—by the z-score. For example, Köhler (2015) shows a positive diversification effect on the z-score of a higher share of non-interest income, even though such an effect tends to decrease as size increases. In a previous study, Köhler (2014) finds a close correlation between non-interest income and the z-score depending on the adopted BM. Specifically, a positive diversification effect on stability is confirmed for retail-oriented banks but not for investment ones. Demirgüç-Kunt and Huizinga (2009) study how banks’ income and funding mixes influence banks’ z-score and return. Traditional banks, relying on interest income and deposit funding, show less risk taking (and hence greater stability) than banks mainly oriented towards non-interest income generation and wholesale funding. Chiaramonte et al. (2015), examining the link between the z-score and banks’ BMs—classified into commercial, savings and cooperative banks—, find that cooperative banks’ BM is more stable than the others. Moreover, this bank type contributes to stabilize large banks, particularly during periods of financial distress. Following previous reports (Ayadi et al. 2011, 2012; Ayadi and De Groen 2014), based on various risk indicators, including the z-score, Ayadi et al. (2015) identify focused-retail banks as safest, while wholesale and investment banks presented greater risk during the financial crisis.

Another stream of analysis targets the impact of regulation on BMs. For example, EBA (2015) argues the Basel 3 implications for banks’ BMs. It focuses on the consequences that the CRR/CRDIV capital requirements, Basel 3 liquidity coverage ratio and net stable funding ratio (in addition to structural reforms) could exert on banks’ BMs. It turns out that compliance with the Basel regulation could strongly affect banks’ profitability and their ability to support the real economy by providing it credit. Ötker-Robe and Pazarbasioglu (2010) consider the Basel prudential regulation by examining the BMs of large and complex financial institutions. The authors state that “the new Basel package is not business model neutral”, as the most relevant effects deriving from tighter provisions for capital and liquidity emerge for investment and universal banks.

Other studies highlight the link between banks’ BMs and one or more Basel 3 requirements, particularly capital measures, on one side, and liquidity indicators, on the other. Vázquez and Federico (2015) distinguish large, internationally active, vs. domestically oriented institutions and classify the latter into commercial, savings and cooperative banks. They explore systematic differences in the link between leverage and structural liquidity, and the consequent probability of failure across bank types. Capital shortages emerge as the determining factor for the failure of global banks (e.g., Russo et al. 2020) and, within the subsample of domestic intermediaries, savings banks are most vulnerable in terms of adequate capital buffers. Blundell-Wignall et al. (2013) show that leverage ratios predict large banks’ risk (measured by the distance-to-default—DTD) better than tier 1 capital ratios. However, the leverage ratio cannot compensate for the great impact of BM characteristics on DTD. Furthermore, the appropriate level of the leverage ratio and the trade-off between it and regulatory capital should be addressed, depending on the specific kind of activity that banks conduct and therefore their BM. Moving on to liquidity requirements, Dietrich et al. (2014) focus on the NSFR, examining its features and drivers. They find that also the NSFR is not business model neutral, as it is strongly affected by the features of BMs: a higher NSFR associates with a more interest-oriented activity rather than those more related to asset management or investment banking. By the same token, King (2013) finds a negative correlation between net interest margin and NSFR for universal banks with highly diversified funding and highly concentrated trading assets. Again, Vázquez and Federico (2015) show that weakness in structural liquidity, resulting in low NSFR, plays a key role in the failure of domestic banks. Within this subsample, commercial banks are most vulnerable to liquidity problems. EBA (2015) shows a high degree of LCR dispersion within BMs, probably due to the low correlation between this indicator and the BM adopted by the bank. More broadly, Bordeleau and Graham (2010) show that the relationship between liquid assets and profitability varies across banks’ BMs. A more traditional banking model, in fact, associates with higher profits in case of less liquid assets. Hence, the authors support appropriately tailored regulatory liquidity standards to achieve financial stability.

We consider Eurozone banks along the six business models (BMs) identified in the Bankscope database: commercial banks, bank holdings and holding companies, cooperative banks, investment banks, real estate and mortgage banks, and savings banks. Restricting to Eurozone countries allows greater uniformity in assessing the impact of Basel 3 requirements on the risk of banks with different BMs. Also, increasingly uniform supervision suggests that Eurozone banks are not too dissimilar, so omitted variables should not endanger the validity of our estimations.

The extent of our time horizon (2000–2014) should ensure a complete view of the issue by allowing us to detect the effects of the international financial and economic crisis on bank risk, before and after Basel 3 rules did apply. To this end, we split the data into two subperiods: prior to Basel 3 release (2000–2010) and after the issue of Basel 3 to the year 2014, in the initial years when adaptation took shape (2011–2014).

Our final sample consists of 3,498 Eurozone banks: 759 commercial banks, 89 bank holdings and holding companies, 1,664 cooperative banks, 132 investment banks, 116 real estate and mortgage banks, and 738 savings banks. Table 1 shows the sample distribution of the banks by BM for each country over all the years. Germany is the country with the highest number of banks in each BM. Italy comes second for cooperative banks (421) and the Netherlands for bank holdings and holding companies (12). France is second for commercial banks (121), investment banks (20) and real estate and mortgage banks (30), while Austria for savings banks (106).

To investigate empirically the impact of banks’ business models (BMs) on the relation between Basel 3 indices and ln_z on a panel data set, we use the methodology by Blundell and Bond (1998). Building on the work of Arellano and Bover (1995), they develop a system estimator of linear dynamic panel data models that uses additional moment conditions. All the estimated specifications have first-differenced residuals that are insignificantly autocorrelated from the second-order onwards (Arellano‒Bond test). First-order autocorrelation is inevitable in differenced residuals, but it is not a problem.

The dynamic specification of our models accounts for the potential residual endogeneity that can bias the results of static specifications. We fit our estimates with the two-step GMM system estimator (Blundell and Bond 1998) and use Windmeijer’s (2005) finite-sample correction for standard errors. All specifications include year dummies and a constant. We also include the first lag of ln_z. This dynamic specification allows us to consider the degree of a bank’s risk persistence connected to some factors, such as competition, banks’ risky customer relationships, intertemporal risk smoothing and regulation (Delis and Kouretas 2011). Given the coherence among the signs of the estimated coefficients along many specifications that we tested (some presented and others available from the authors upon request), our results receive sound empirical support.

Tables 6 and 7 present the results of different dynamic panel regression models. The base models control for a large set of relevant predictors suggested in the literature, as discussed above, and differ only in the estimation sample: the years 2000–2010 and the years 2011–2014. These subsamples may detect possible structural changes in the parameters across the two subperiods. Two further models with interactions, estimated on the two sample periods, follow. The interactions of the banks’ BMs with the Basel 3 variables serve to identify the effect that is the main object of our investigation: whether and how the Basel 3 variables’ effect on banks’ z-score differs among banks working under different BMs. The estimated coefficients represent the variation in the effects of each category with respect to commercial banks, the omitted category. Finally, tests on linear combinations (composite effects) of the coefficients of the Basel 3 variables by BM allow appraising the different impacts on the stability of banks working under different BMs when a policy based on an increase of one standard deviation of the Basel 3 variables is implemented (Table 8).

Table 6 states the estimation results on the dependent variable, ln_z. The two models cover the two sample-split subperiods (2000–2010 vs. 2011–2014), with 21,106 and 5,699 observations, respectively.

Significant year dummies in Table 6 models, pointing to common movements in z-score across time, suggest that all banks share some degree of business risk. In addition, our results confirm that banks’ risk taking is highly persistent, as testified by the high and significantly positive coefficient for the lag of ln_z. This suggests that to derive consistent estimates one must control for dynamics in bank risk taking.

About Basel 3 variables, Table 6 suggests that CR favours banks’ stability only in the pre-Basel 3 rules period, which could be interpreted as consistent with the view that procyclicality in Basel 2 did not extend to Basel 3 (Repullo and Suarez 2013). Stability is mainly explained by LR. For this requirement, our results are consistent with the literature: in fact, LR has a positive and significant (at the 1% level) effect on banks’ stability and this holds in both time periods. Among the liquidity indicators, LCR has a negative coefficient scarcely significant. This result validates the hypothesis that illiquid banks are less risky, since holding more liquid buffers may induce banks to take more risk (Wagner 2007). Finally, NSFR has a positive but not significant coefficient in both time periods.

All bank-level variables strongly affect banks’ stability. However, SIZE does not exert a coherent effect along the two time periods. The coefficient of the cost-income ratio, our efficiency measure, is negative and highly significant: an increase in the CIR (lower efficiency) damages bank stability, probably because less-efficient banks tend to undertake more risk (Kwan and Eisenbeis 1997; Williams 2004; Fiordelisi et al. 2010). As expected, the last bank-level variable proxying for loan portfolio risk (loans loss provisions/gross loans) has a negative and significant coefficient: lower asset quality links to higher risk (e.g., Stiroh 2006; Van Oordt and Zhou 2014).

Turning to country-level variables, GDPGR has a positive coefficient, suggesting that banks’ credit risk drops during economic booms (Kashyap et al. 1993; Conrad et al. 2009). Instead, the degree of concentration in the banking industry has a negative and significant coefficient. This supports the hypothesis that more concentrated banking markets heighten financing costs, so companies tend to enter debt only for high-risk projects, raising the credit risk borne by banks (Boyd and De Nicolò 2005). The central government debt to GDP ratio seems to have negative but irrelevant effect in subperiod 2000–2010. Instead, its effect is positive and significant in subperiod 2011–2014 featuring the euro sovereign crisis. This would seem at odds with the sovereign-bank doom loop view by Brunnermeier et al. (2016) where high public debt may translate into large holdings of public sector securities by domestic banks and that would raise the credit risk of both the sovereign and the banking sector, via a diabolic loop that increases the probability of twin crises. However, this switch in sign and significance of public debt could be in line with more recent reassessments of larger fiscal multipliers (Blanchard and Leigh 2013; Dallari and Ribba 2020; Dosi et al. 2015) and with the growing evidence that these multipliers tend to be smaller during economic booms and larger during recessions (Afonso et al. 2018). Finally, inflation shows a negative and significant impact only in subperiod 2011–2014. While this effect seems consistent with the related literature (Arpa et al. 2001; Baboucek and Jancar 2005; Uhde and Heimeshoff 2009; Demirgüç-Kunt and Detragiache 2011), one might question whether that can be stretched to the Eurozone situation featuring a risk of deflation at that time. Upon a closer look we can see that core inflation never dipped below zero in the Eurozone average and even for the GIPSI countries, facing sovereign debt tensions, it only did so marginally (− 0.07%) in 2014, while the GIPSI continued to have higher inflation than the Eurozone average still in 2011 and 2012. Thus, higher inflation in the peripheral countries could still be denting export competitiveness, possibly damaging the macroeconomy and the performance of the banking sector (Johnston and Regan 2016).

In non reported results we checkecd also for holdings of domestic and foreign general government debt, non-performing loans to total loans ratio, general government debt, not central government debt and interbank money market rate (we thank an anonymous reviewer for suggesting additional controls to check the robustness of our results). The results were not substantially changed by the inclusion of these additional controls at the expenses of a reduction of the observations.

The second step of the analysis is the most important and novel part of our work. The dynamic panel regressions of Table 7 study banks’ stability by interacting Basel 3 variables and banks’ BMs.

Also in Table 7, the two models are estimated over the two subperiods (2000–2010 and 2011–2014).

Our results found many differences in the coefficients of the Basel 3 variables—even opposite signs—across different BMs. This supports our view that the current framework is a “one-size-fits-all” regulatory paradigm, also in accordance with other studies (e.g., Ötker-Robe and Pazarbasioglu 2010; Dietrich et al. 2014; EBA 2014; Vázquez and Federico 2015; Khafagy 2018).

Finally, we quantify the overall impact that each BM exerts on the effects of Basel 3 requirements on banks’ stability. To accomplish this, we use the estimated coefficients of the Basel 3 variables in Table 7 to compute, for each BM, the composite effect of a contemporaneous increase of one standard deviation (\( SD\)) in the Basel 3 variables. Thus, the composite effect (CE) for \( {BM}_{k}\) (BM of type \( k=m,h,o,i,r,s\)) is the linear combination obtained as follows:

where \( {SD}_{j}\) is an increase equal to one standard deviation in the estimation sample of variable \( j\) (\( =CR, LR, LCR, NSFR\)); and \( {\beta }_{j}\) and \( {\beta }_{j*B{M}_{k}}\) are the estimated coefficients presented in Table 7. Note that for the reference category \( m\), commercial banks, all the coefficients \( {\beta }_{j*{BM}_{k}}\) are equal to zero.

Table 8 reports the results. For each period, Δ stands for the difference between the CE of each line and the CE of the first line, which is relative to the benchmark category commercial banks. In other words, Δ is equal to the linear combination of the increases of the Basel 3 variables with only the coefficients of the interaction terms:

All effects are tested to check whether they significantly differ from zero. Δ significantly positive means that the policy based on increases of one standard deviation of the Basel 3 variables has a larger positive effect on banks’ stability for \( {BM}_{k}\)vis-à-vis the benchmark.

Table 8 shows that cooperative and savings banks are the most affected by the Basel 3 rules (see the Δ columns). This suggests that more focus on traditional activity, rather than strategies of diversification, could represent a positive driver of stability when reform measures based on Basel 3 are introduced.

We perform several robustness checks.² First, we estimate the models reported in Tables 6 and 7 using an alternative LR: tier 1 capital to total assets (Altunbas et al. 2011; Brei and Gambacorta 2014) rather than tangible common equity to tangible assets. Our conclusions remain substantially unchanged. In particular, LR has a positive and significant impact on banks’ stability.

Third, we test the regressions using an alternative LCR proxy: liquid assets to total assets (Bourke 1989; Molyneux and Thornton 1992; Barth et al. 2003; Demirgüç-Kunt et al. 2003; Shen and Chen 2014) rather than liquid assets to total deposits, money market and short-term funding. The findings are in line with the models reported above : this liquidity indicator is not significant.

Finally, we repeat our estimates excluding the country with almost half of the banks of our sample (Germany, see Table 1). Most results still hold (in particular, LR has a positive and significant coefficient in all the periods). We attain the same results considering only Germany.

The global financial and economic crisis urged regulators, supervisors and policymakers to assess which drivers can effectively avoid excess risk taking and ensure banks’ stability. On one hand, regulatory reform in Europe homogeneously applies Basel 3–CRD IV–CRR to each bank. On the other hand, however, it is increasingly accepted that banks’ business models (BMs) can play a key role too. Against this background, we tackled two main research questions on a large sample of Eurozone banks classified into six different BMs over 2000–2014 (divided in two subsamples 2000–2010 and 2011–2014). First, while Basel 3 is multifaceted, featuring four chief regulatory innovations—capital buffer (CR), leverage ratio (LR), liquidity coverage ratio (LCR), and net stable funding ratio (NSFR)—, most studies have focused on just one or two of the four innovations, not venturing to capture the overall thrust of Basel 3 on banks’ risk taking. Thus, our first task was to offer a comprehensive analysis of the impact of Basel 3 by considering whether and how all four innovations jointly affect banks’ riskiness.

Among the most interesting results of the base model without interactions, our analysis showed that LR is a positive determinant of banks’ stability, unlike CR. On the liquidity side, NSFR and LCR do not contribute to mitigate banks’ risk.

Our results call for further research on this issue as well. Investigating other geographical macro-areas could enrich the discussion about important differences to consider when discussing financial stability. To this end, comparative studies of areas differing in supervision could offer insight. Finally, the impact of regulation on banks’ stability could vary not only across different business models, as our results indicate, but also be nonlinear across different levels of banks’ risk.