Abstract ___________________________________________________________________________ Instituto Complutense de Estudios Internacionales, Universidad Complutense de Madrid. Campus de Somosaguas, Finca Mas Ferré. 28223, Pozuelo de Alarcón, Madrid, Spain. © Victor Echevarria-Icaza and Simón Sosvilla-Rivero Victor Echevarria-Icaza, Complutense institute for international Studies, Universidad Com- plutense de Madrid. 28223 Madrid, Spain Corresponding author. Tel.: +34 913 942 342; fax: +34 913 942 591, victoreicaza@yahoo.com Simón Sosvilla-Rivero, Complutense institute for international Studies, Universidad Complutense de Madrid. 28223 Madrid, Spain sosvilla@ccee.ucm.es ISSN: 2530-0849 Acknowledgements: The authors thank Vicente Salas, Massimiliano Caporin and participants at II Workshop on “Macroeconomics, Money and Finance” (Universidad Complutense de Madrid, February 2017) for helpful comments and suggestions. Simon Sosvilla-Rivero thanks the hospi- tality provided by the Department of Economics during a research visit at the University of Bath. Responsibility for any remaining errors rests with the authors. Funding: This work was supported by the Banco de España through [grant from Programa de Ayu- das a la Investigación 2016–2017 en Macroeconomía, Economía Monetaria, Financiera y Bancaria e Historia Económica]; the Spanish Ministry of Education, Culture and Sport [grant PRX16/00261]; and the Spanish Ministry of Economy and Competitiveness [grant ECO2016-76203-C2-2-P]. El ICEI no comparte necesariamente las opiniones expresadas en este trabajo, que son de exclusi- va responsabilidad de sus autores. This paper shows that systemic banks are prone to increase their regulatory capital ratio through a decline in risk-weighted assets density and an intense use of lower level capital. The market access of systemic banks, and the fact that they were singled out for higher capital requirements seem to have biased them towards lower level capital, consistent with the theory that asymmetric information drives capital decisions. These effects are particularly strong for institutions that had a rather low level of capitalization at the start of the period and for those that exhibited a strong use of Additional Tier I capital before the regulatory changes. Strict capital composition require- ments for firms with lower buffers would be an improvement. JEL Classification: G12, G21, G28. Keywords: Contingent capital, banking regulation, risk-taking incentives, asset substitution, sys- temic risk Index 1. Introduction 5 2. The regulation 8 3. Empirical model 9 4. Data and empirical results 9 4.1. Data 9 4.2. Control group 9 4.3. Empirical results 14 5.- Concluding remarks 20 References 21 Appendix: list of GSIFI banks and control group 24 4 5 1. Introduction This paper analyzes the effect of regulatory capital requirements for systemic banks on their capital structure. Since 2008, the regula- tion has tightened across all banks, with the introduction of stricter capital requirements for financial institutions. This was based on the idea that policymakers had that insuffi- cient capital had made firms vulnerable (see, e. g., Admati and Hellwig, 2014; and Kashkari, 2016) and led to public bailouts. One aspect of the regulation that was particu- larly heeded dealt with systemic banks. The failure of these banks can have adverse effects on the overall financial system and spread on to the sovereign (see, e. g., Singh et al., 2016) 1. As a result, they benefit from an implicit protection by the sovereign. Regulators have pushed regulation that improves the resiliency of these financial institutions, increasing their capital requirements (Hanoun, 2010) and thus lowering the probability that they will be bailed out and the eventual size of a bailout package (Calderon and Schaeck, 2016; Duchin and Sosyura, 2014 and Giannetti and Simonov, 2013). We examine how systemic institutions have differed from other institutions in their ap- proach to strengthening their equity ratios. Their approach may differ for several rea- sons. First, capital regulation is stricter with systemic institutions, in particular through the systemic surcharge, which affects only the highest level of capital (Common Equity Tier 1, CET1) and through certain requirements, like the total loss-absorbing capacity (TLAC) that pertain to overall capital and loss absorbing li- abilities. This may lead banks to diversify their sources of capital. In particular, they may want to avoid further dilution of equity holders and so increase the issuance of debt-like capital. This may stem from a willingness to reduce the pressure on their return on equity (ROE). 1 Sovereign risk can also affect bank risk. Based on these linkages, some authors (Brunnermeier et al., 2011 and Reichlin, 2014, among them) have described the devel- opment of a ‘diabolic loop’ as the major cause of the crisis in euro area countries. Secondly, given the implicit bailout from au- thorities, systemic institutions have a ten- dency to use debt like instruments, in which they benefit from a subsidy (see Haldane and Madouros, 2012; Deangelo and Shchultz 2013; or Acharya et al., 2016, among others), lower- ing their cost of debt relative to their cost of capital. This means that, ceteris paribus, sys- temic firms are more likely to use debt like in- struments when they can choose how to boost their regulatory capital. In particular, meeting leverage ratios and TLAC requirements can lead to such an increase, but not particularly for the systemic surcharge. We attempt to test whether systemic banks are more likely to meet their capital require- ments through lower level capital than other banks. Our particular focus is on how banks have reacted to the regulatory changes imple- mented in recent years that have generally led to higher capital ratios. We focus on testing a possible bias to use lower level capital. This is important, as the holders of lower level capital are often other financial institutions (Avdjiev et al., 2013). As a result, from a financial sta- bility perspective, understanding who issues lower capital level, and the effects it has on its behavior is important. We propose a difference in difference esti- mation, using an approach similar to that of Schepens (2016) that was first fleshed out by Rosenbaum and Rubin (1985). A flurry of new regulation has affected banks since 2009. Of particular importance have been the rules of tax deductibility of Contingent convertible capital (CoCo bonds)2, approved in the UK in 2013, and used by many banks in the euro area since then. We use a modified propensity score matching methodology based on a pro- bit model, in order to construct a robust coun- terfactual to the banks that made it into the systemically important financial institution (SIFI) list, which we compare with banks that behaved similarly as determined by the probit model. We also use those banks that had simi- lar government support when the first list of global systemically important banks (GSIFIs) 2 For an excellent primer on the CoCos, see Avdjiev et al. (2013). 6 was announced. This way we obtain a homo- geneous sample. From there, we test how sys- temic banks adapted their capital ratios after their inclusion on the SIFI list and, secondly, to the other regulatory practices, like the change in the tax treatment of CoCo bonds. The GSIFI list and the increase in regulatory capital requirements provide the grounds for an experiment to understand how banks re- act to such shocks. In particular, the fact that certain institutions characterized by their size, complexity and interconnectedness were asked to do more can shed some light on the incentives to use lower level capital and the ef- fect on bank risk taking of these changes. If being systemic, and in particular, being add- ed to the list of GSIFIs confers, first, tougher capital requirements, and second, a certain ad- vantage in issuing debt like instruments, banks will have an incentive to search for lower level capital. If these incentives are strong enough, banks may, as a result, end up being weaker than originally thought, just because they now have to face the effects of lower level capital. Understanding the determinants of the struc- ture of capital is all the more important given the relevance of the issue for financial stability. Most CoCo bonds are held by other banks. Giv- en their asymmetric risk profile, understand- ing what leads institutions to issue them will shed light on one of the factors of instability for the financial system. Such is the purpose of our empirical model. The first main finding of this paper is that ac- counting-based equity ratios at systemic banks have not behaved very differently from overall banks. In fact, the regulation has led them to become lower. However, systemic banks have improved their regulatory ratios through three methods. First, by a reduction in the density of risk weighted assets, which has been particu- larly intense at systemic banks. Secondly, our results suggest a high propen- sity to use lower level capital, and in particular CoCo bonds, which increased when the market was liberalized after 2013 a tax change in Brit- ish law made these more attractive. We find that CoCo bonds issuance has been particu- larly intensive at systemic banks. This pattern can be extended to all lower level capital, such as Tier 2 (T2) capital, which increases at sys- temic institutions since after 2011, specially at firms that had less capital at the beginning of the period. This would be consistent with pecking order theory of Myers (1984), based on the argument in Myers and Majluf (1984) that asymmetric information problems drive the capital structure of firms. Third, we show that various risk measures sug- gest that bank risk increased after the regula- tion was passed, even though the intensity of risk-weighted assets (RWA) decreased. This is particularly true for banks that were relatively low capitalized in 2011. These banks showed, ceteris paribus, a higher propensity to capital- ize through lower level capital. This finding would suggest that one of the unintended con- sequences of the push to increase capital ratios may have been a deterioration of the composi- tion of capital and riskier banks than was in- tended. To the best of our knowledge, it is the first pa- per to study empirically how the regulation affected the decisions of systemic institutions and that looks at the composition of their capital. We contribute to several strands of the literature. First, our finding suggests that systemic banks exploited the leeway in capital requirements to use more debt-like instru- ments, in line with the theory that asymmetry of information and ROE targeting guide bank capital decisions. Both of these theories sug- gest that banks increase the risk of the portfo- lio, particularly if their starting level of capital is low, which is consistent with our results. This paper contributes to the literature on how banks choose their equity ratio and how they respond to the different incentives found in the regulation. The optimal capital struc- ture has been empirically studied by Marcus (1984), Flannery and Rangan (2008), Green- law et al. (2008), Brunnermeier and Pedersen (2009), Gropp-Heider (2010), and Schaeck et al. (2011), among others. The importance 7 of the issue stems from the role of the capital structure in determining bank performance, in particular in crisis times (Berger and Bouw- man, 2013; and Berger et al. 2016). De Jonghe and Öztekin (2015) and Schepens (2016) argue that the way banks adjust their capital ratios depends on both the regulatory environment and the macroeconomic condi- tions. Our framework also allows for sluggish capital adjustment, in line with their results. Acharya et al. (2016) finds that systemic banks benefit from a subsidy, although he finds that as different bank bailouts were implemented, market discipline became laxer for all banks. That large banks are more prone to leverage is a well-established finding in the literature (see, e. g., De Jonghe and Öztekin, 2015; and Schepens, 2016). Systemic banks are prone to high leverage (see, e. g., Haldane, 2013; and International Mon- etary Fund, 2009), and certain debt instru- ments like CoCo bonds are almost exclusively used by these banks. This can be related to the advantages conferred by the size of the banks, which allows them more market access. Given the literature finds that CoCo bonds are priced as debt and do not dilute original shareholders (Berg and Kaserer, 2015), one can expect sys- temic banks to use CoCo bonds heavily. However, what is surprising is that few papers have studied how banks determine the com- position of capital, although one exception is Demirguc-Kunt et al, (2013). This is all the more relevant as a growing body of literature is showing that the composition of capital mat- ters. While CoCo bonds were generally seen as a good idea that would reduce the pro-cyclical- ity of capital regulation (see Flannery, 2010; Calomiris and Herring, 2011; and Coffee, 2011, among others), they have difficulties. Chan and Wjinbergen (2016) have shown that CoCo bonds, which were designed to avoid having firms raise capital in times of stress (see Flan- nery, 2010), can actually have adverse conse- quences on bank health and induce negative incentives. Stockholders may ask for a larger net present value (NPV) to accept projects when close to the trigger point: if they feel they will have to share the returns with new equity holders, they will increase the required return of projects so as to make up for the dilution of capital. This lowers bank incentives to lend in difficult times. Secondly, equity holders at banks will be will- ing to accept greater asset volatility, as they know that any losses will be carried on at least partially by the holders of CoCo bonds. As a re- sult, banks that issue many CoCo bonds will be riskier. As Chan and Wjinbergen (2016) and Berg and Kaserer (2015) show, this depends crucially on the contractual design of the Coco. Worryingly, they find that the contractual design of most CoCo bonds does generate perverse incentives. That the systemic capital requirements led to more intense use of CoCo bonds is, therefore, a point of concern. Our results are consistent with different chan- nels through which capital can affect risk tak- ing. Horvath et al. (2014) point out that higher leverage induces higher risk taking through moral hazard and asymmetric information. In his view this is due to the fact that debt holders have less ability to monitor managers than eq- uity holders. In his model, there are signs that banks tamper with RWA density. Stricter re- quirements lower RWA density but other risk measures suggest that they do not change the risk profile of the bank. This is line with our finding that while stricter requirements led to lower RWA density, they did not lead to lower risk at these banks. Laeven and Levine (2009) suggest stricter reg- ulations can lead to higher bank risk depend- ing on bank characteristics. This finding is con- sistent with the different reaction to a rise in capital requirements for banks. Banks that, ex ante had less capital are likely to remain less capitalized than peers after the regulation of stricter requirements is passed. We also provide evidence that supports the 8 finding by Louri and Pagratis (2014) that banks target a certain level of ROE (see also Haldane, 2013). In such a context, banks tend to over- shadow the effect of higher requirements in making banks safer and the buffers available, and react increasing the risk of the portfolio in an effort to maintain their ROE. This would suggest that banks react to higher capital re- quirements trying to minimize the dilution of existing stockholders. The rest of the paper is organized as follows. Section 2 reviews recent regulatory initiatives that pertain to systemic banks´ capital ratio. Section 3 introduces our empirical model. The data and empirical results are reported in Sec- tion 4. Finally, Section 5 offers some conclud- ing remarks. 2. The regulation The new capital regulation forces banks to in- crease their capital ratios. Since the financial crisis, several initiatives have led to these high- er capital requirements. The various initiatives are designed to tackle different aspects of bank regulation. The systemic surcharge specifies that banks must increase the CET1 capital ratio. The mag- nitude of the increase can be up to 2 percent- age points. The list of systemic banks affected by the regulation is published yearly since 2011. Banks are affected gradually by this, and need to increase their CET1 ratio by 2019. Other initiatives that do not differentiate amongst levels of capital but rather set an overall requirement is the TLAC. The TLAC sets an overall level of loss absorbing liabili- ties that the GSIFIs must hold. These liabilities include capital and long term unsecured debt. The level of these instruments is based on risk- weighted assets and the range is 16% to 20%. The leverage ratio, binding for all institutions, also does not differentiate across capital qual- ity. Finally, there are requirements in terms of the ratio that each of the capital tranches must meet. As a result, banks have in general, and particularly for systemic institutions, to in- crease their capital ratios substantially since the post crisis period. However, they have lee- way in terms of the pace and the quality of the capital they will increase. Some banks have increased their capital ratios through the use of AT1 capital. This capital bucket is composed mainly of contingent con- vertible capital, which counts toward the Tier 1 (T1) capital. This is debt issued by banks, which can be converted into capital at a pre specified conversion ratio once a trigger, gen- erally set at a solvency ratio. In order to be con- sidered AT1, the solvency ratio must be above a certain threshold. CoCo bonds have also shown to be subject to substantial regulatory uncertainty, which ex- plains some the volatility in their prices. This uncertainty pertains both to coupon payments and to their placement in the capital structure once resolution is implemented. The first was related to the uncertainty regarding whether CoCo coupon payments would be subject to maximum distributable amount (MDA) re- quirements, which would have rendered more difficult the payment of coupons of firms in dif- ficulty. A second source of uncertainty stems from the difficulty for investors to estimate how close banks are to the trigger point. In particular, given that the trigger point is usually a level of T1 capital, and that firms are not forces to make their levels of Pillar 2 capital required public, it is theoretically difficult for holders of CoCo bonds to estimate how close they are to triggering MDA restrictions Part of this was solved when the requirements for the MDA were clarified (Chance, 2016). However, an essential element of uncertainty stems from the fact that banks are not forced to disclose their Pillar 2 capital. As a result, a coco holder can have trouble understanding how far it is from the trigger point. This makes pricing of CoCo bonds difficult, and so prone to volatility. Large banks have been the largest issuers 9 of hybrid instruments in general. This is ex- plained by both the need for a certain size of the issuer that can mitigate some of the risks that arise from the hybrids and, secondly, the fact that systemic banks had a larger regula- tory pressure to strengthen their capital ratios. 3. Empirical model We run a regression of the form shown below using the generalized method-of-moments (GMM) estimator proposed by Blundell and Bond (1998). The basic regression model is the following: (1) where ETAi,t is the equity ratio of bank i at time t, defined as equity over total assets; Treatedi,t is a dummy that equals one for a bank in the GSIFI list each year (treatment group indica- tor); Postt is a dummy indicator equal to one in the post-treatment period (2013–2015 in our main regression); and Xi,t represents a set of explanatory variables that have been consis- tently seen as important bank capital structure determinants. We will introduce variations to the left-hand side variable. In particular we will test how the different components of ETA can be affected by the new regulation. As a result, we will gain in- sight into how the components of ETA changed. This way we can test whether systemic banks had a bias to adjust their ETA through the dif- ferent components of the ration. In particular, from the decomposition of the ETA ratio, one can see that the ratio can be adjusted through an increase of different components of equity, altering the share of the regulatory capital cov- ered by equity (E/CR), a change in the inten- sity of Risk-Weighted assets (RWA/A). ETA = E/RC * RC/RWA * RWA/A 4. Data and empirical results 4.1. Data The data consists of 260 listed financial insti- tutions in Europe. The sample is from 2000 to 2015. Some regulatory ratios are not available for the whole sample, which limits the samples used in some cases. 4.2. Control group For the appropriate estimation of the differ- ence in differences model, the selection of the control group is fundamental. The parallel path hypothesis states that the control group must be such that if there had been no policy inter- vention, then both groups would have evolved in the same manner. Our control group is, ideally, those institutions perceived by the market to have the same backing as the government, but which were later not classified as systemic and so did not have to increase their capital ratios so much. We would expect these institutions to benefit from the subsidy initially, but not after the list was made public. They can also be expected to have less pres- sure in increasing their capital ratios, given that they are not subject to the requirements that were specific to systemic institutions. And the fact that they were not classified as sys- temic means that while they may be similar to systemic institutions, when they had to raise their capital ratios, they did not have the bias towards debt issuance, as they had less pres- sure from equity holders. We employ two methods to determine the con- trol group, which yield similar results. First, as shown in Table 1, we run a probit model, where the dependent variable is a dummy that is 1 when the bank is included in the GSIFI list. The explanatory variables s of this probit mod- el are the characteristics that should define systemic banks: size (total assets), complexity (proxied by the weight of non-interest income in total income) and connectedness [calculat- ing the conditional value at risk (CoVaR) of the firm: its contribution to the overall value at 10 risk (VaR) of the system]. Finally, we introduce interest income over total income as a mea- sure of a bank´s complexity (IRINCOME). Fol- lowing Schepens (2016), we take the nearest neighbor of each of the systemic institutions, selecting two institutions for each systemic one (see the table in the appendix for a list of the systemic banks and the control group). Secondly, and as a robustness check, we use as a control group those institutions that in 2011 (when the first list of GSIFI was made public) had the highest Fitch support rating. Various tests suggest that the control group meets the parallel path hypothesis. We employ two methods to test this. First, the residuals of the probit regression for institutions in our control group is not signifi- cantly different from 0, suggesting that by re- ducing the sample we are using a group that is rather similar to the systemic institutions. Secondly, the statistics on the control group provide evidence that our selection algorithm is effective in creating a control group. The pre 2011 level of most of the variables of interest [in particular additional Tier 1 (AT1) securi- ties)] is not significantly different from that on the GSIFI list, even though the overall sample is (see Table 2). This is true for the main vari- ables of interest. However, while there is a re- maining significant difference in terms of size. Yet as the box plots show, this difference is greatly reduced when we consider only our control group. Figure 1 depicts how the distri- bution of different variables changes according to the sample. In particular, for each variable of interest (the ETA ratio, AT1 to total assets, RWA intensity, the log of total assets, the log of Tier 2 to total assets ratio, the log of hybrids to total assets and the return on average assets (ROAA)), we present two charts. The chart on the left compares the distribution of that vari- able for the whole sample of banks and those in the SIFI list. The chart on the right compares the distribution of that variable for our control group and those in the SIFI list. As can be seen, there is much more overlap with the control group than with the overall sample of banks, providing further evidence that our control group renders a better counterfactual than the overall sample. Table 1: Probit results Dependent Variable: SYSTEMIC Variable Coefficient Std. Error z-Statistic Prob. COVAR 2.3 0.35 6.53 0.05 LASSETS 1.6 0.16 9.75 0 IRINCOME -0.4 0.07 -6.66 0 C -32.1 3.24 -9.91 0 R^2 0.60 Total obs 640 Sample 2011-2015 Method ML Notes: The dependent variable is a dummy that equals 1 for each bank each year that it is included in the G-SIFI list. COVAR indicates the CoVar of each firm. LASSETS the log of total assets and IRINCOME interest income over total in- come. Table 2: Equality of means test for systemic institutions and non-systemic ones AT1 ETA LASSETS LT2 LHYBRID RWAINT ROAA FULL SAMPLE 0 0 0 0 0 0 0.05 CONTROL GROUP 0.19 0.72 0 0 0.3 0.09 0.97 Notes: Based on pre-2011 data. The number shown is the p-value of the test. 11 Figure 1: Box plots (2000-2011) a. ETA Non- systemic Systemic SystemicNon- Systemic All Banks Control Group b. AT1 ratio Non- systemic Systemic SystemicNon- Systemic All Banks Control Group 12 c. Risk-Weighted assets over total assets Non- systemic Systemic SystemicNon- Systemic All Banks Control Group d. Log of Total assets Non- systemic Systemic SystemicNon- Systemic All Banks Control Group 13 e. Log of Tier 2 capital to total assets Non- systemic Systemic SystemicNon- Systemic All Banks Control Group f. Log of hybrid instruments to total assets Non- systemic Systemic SystemicNon- Systemic All Banks Control Group 14 Finally we introduce controls for the macro- economic context (GDP and CPI), which should also affect bank capital decisions. We use different time dummy variables to test how the process of adjusting equity ratios changed during the different steps of regula- tion. While the first SIFI list was published in 2011, we focus on the 2013-2015 period. This is meant to capture how the regulatory chang- es that took part in 2013 (regarding the fiscal treatment of hybrid instruments) changed the way SIFIs built up capital. This way, we show the new regulations regarding TLAC, and the ease in issuing CoCo bonds from the changes in its tax treatment were used by systemic firms3. The coefficients of interest are those associ- ated with Post, Treated and the interaction term. These coefficients will indicate whether systemic institutions, had a certain bias in the way they determined their equity ratio of the different types of capital: We use two separate dates, to check the effect of different regula- tory initiatives. First, 2011, after the first list of GSIFIs was made public, and, second, 2014. The latter reflects the legal changes to which CoCo bonds were subject in many jurisdic- tions, which equated their tax treatment and equated them to debt, thus increasing their use. 4.3. Empirical results Results on the determination of bank capital The main results are shown in Tables 3 and 4. Table 3 reports the results of the regression when the control group is determined by the results of the probit model. Table 4 presents the results when the control group is com- posed of the banks that had the highest sup- port rating by Fitch. We find that the systemic regulation did not induce higher ETA ratios. In fact, some specifications suggest that the sys- temic regulation led to a lowering of ETA ratio. However, overall solvency ratios may have in- creased by the reduction in RWA intensity. This 3 We also run robustness checks using a dummy vari- able from 2011 onward. These additional results are not shown here to save space, but they are available from the authors upon request. We estimate equation (1) with a lagged depen- dent variable to correct for autocorrelation, making used of the Blundell-Bond`s GMM es- timator to correct for the possibility of a bias from using a lagged dependent in a panel set up. Furthermore, we employ random cross section effects, in line with the results of a Haussman test. Regarding Xi.t, we have considered the follow- ing variables: First, the lagged equity to assets ratio. This is in line with the use in the litera- ture that banks adjust their capital ratios slug- gishly, as they use a mix of passive adjustment (through retained benefits) and active adjust- ment (through equity raising or changes in risk weight assets, be it through changes in the average risk weight or on assets). Secondly, we use as a control banks profit- ability, proxied by the return on average as- sets (ROAA). The coefficient on this variable is uncertain. On one hand, higher profitability should lead to higher capital ratios, as retained earnings lead to passive increases in the capi- tal ratios. On the other, more profitable banks have a lower probability of distress and higher taxable income. This will increase their incen- tives to use more debt, which should be rela- tively cheaper than capital. Risk is captured by the ratio of risk weighted assets to total assets (RWAINT), and in other specifications by the standard deviation of ROAA and the z-score. Generally, riskier banks should have higher capital ratios. This is be- cause debt should be relatively more expensive for the riskier banks, and also because these banks will face higher capital requirements. Thirdly, bank size will also determine the eq- uity ratio through different channels. A priori, these channels point in different directions: on one hand, larger banks can diversify. This lowers the riskiness of their portfolio and so allows them to use less capital. On the other hand, larger banks are well known, and so may have access to larger pools of investors. As a result, they should have a lower cost of capital and so a larger equity ratio. We use the log of total assets (LASSETS) as a proxy for bank size. Table 3: Regression results using the nearest neighbor criteria for determining the counterfactual ETA LASSETS LT2 LT1 LT2 Hybrid AT1 LAGGED DEP 0.87 *** 0.95 *** 0.93 *** 0.80 *** 0.94 *** 0.94 *** ROAA 0.31 *** 0.03 *** 0.03 *** 0.06 *** 0.02 *** -0.01 *** GDP -0.33 *** 0.01 *** 0.14 *** -0.02 0.12 *** 0.04 *** CPI -0.25 *** 0.015 *** 0.02 *** -0.02 *** 0.02 0.02 *** RWAINT 0.01 *** 0.00 *** 0.00 *** 0.00 *** 0.00 *** Lassets -0.01 *** 0.11 *** 0.19 *** 0.11 *** 0.01 *** POST2013 0.22 ** -0.02 *** -0.05 0.03 -0.08 ** 0.00 SYSTEMIC 0.17 0.01 *** -0.02 0.02 -0.03 -0.04 POST2013*SYSTEMIC -0.39 ** -0.12 *** 0.18 *** -0.03 0.16 * 0.20 *** C 4.35 0.84 *** -2.67 *** -0.38 -2.59 *** -0.56 *** Number of observarions 447 447 447 447 447 447 R squared 0.7 0.85 0.72 0.88 0.75 0.65 Bank RE Yes Yes Yes Yes Yes Yes Method GMM GMM GMM GMM GMM GMM Control group PROBIT PROBIT PROBIT PROBIT PROBIT PROBIT Note: LAGGED DEP is the lagged dependent variable. The different determinants are the levels of capital. ETA shows the equity to assets ratio, LT2 the log of Tier 2 capital, LT1 the log of Tier 1 capital, LT2 hybrid the use of Tier 2 hybrid instruments and AT1 the level of additional Tier 1 capital Table 4: Regression results using the Fitch support rating criteria for determining the counterfactual ETA LASSETS LT2 LT1 LT2 Hybrid LAGGED DEP 0.85 *** 0.97 *** 0.95 *** 0.999 *** 1.02 *** ROAA 0.54 *** 0.09 *** 0.13 *** 0.16 *** 0.16 *** GDP -0.85 ** -0.03 *** -0.08 -0.05 -0.06 CPI -0.3 *** 0.007 *** -0.03 -0.03 *** 0 *** RWAINT 0.01 *** 0.001 *** 0 *** 0.01 *** 0.005 * Lassets 0.06 *** 0.19 *** 0.36 *** -0.1 POST2013 0.08 *** 0.0001 *** -0.23 *** -0.06 *** -0.2 ** SYSTEMIC 0.1 *** 0.011 *** -0.08 -0.09 ** -0.23 POST2011*SYSTEMIC -0.46 *** -0.022 *** 0.44 *** 0.06 0.388 ** C 10.14 0.84 1.67 0.66 Number of observarions 280 280 280 280 155 R squared 0.7 0.85 0.74 0.88 0.65 Bank FE Method GMM GMM GMM GMM GMM Control group FITCHSUPPORT1 FITCHSUPPORT1 FITCHSUPPORT1 FITCHSUPPORT1 FITCHSUPPORT1 Note: LAGGED DEP is the lagged dependent variable. The different determinants are the levels of capital. ETA shows the equity to assets ratio, LT2 the log of Tier 2 capital, LT1 the log of Tier 1 capital, LT2 hybrid the use of Tier 2 hybrid instruments and AT1 the level of additional Tier 1 capital. The sample here is the systemic institutions and those institutions that, before 2011, had the largest support rating by Fitch. 17 finding is robust to the use of different control groups (i.e. by government support in 2011, by the probit result or by size). The similarity in findings for both groups can be found for the main results. Secondly, systemic banks were more likely to increase the use of lower level capital. This in- cludes T2 capital, hybrids and AT1 capital. This result is consistent with our hypothesis that systemic banks had a comparative advantage on these levels of capital and that they used it to boost their ratio. The latter effect is found when we run the ex- ercise setting the treatment period to start in 20114, but becomes particularly strong for the period after 2013, consistent with the effect of the regulatory change mentioned before, which equated AT1 issuance to debt issuance for banks in the European Union. This find- ing is consistent with that of Schepens (2016), which suggests that firms adjust their financial structure in reaction to the tax treatment of 4 These additional results are not shown here to save space, but they are- available from the authors upon re- quest. the different instruments. In order to gain more insight on the bank characteristics that lead to differences in the composition of capital, we use quantile regres- sions to test which banks were more strongly affected by the regulation. While standard linear regression techniques summarize the average relationship between a set of regres- sors and the outcome variable based on the conditional mean function E(yǀx), quantile re- gressions allow to describe the relationship at different points in the conditional distribu- tion of y. In particular, the quantile regressions show whether the reaction to the regulation was different for different levels of lower level capital. Figure 2 shows the results of the same regression as (1) for different quantiles of the dependent variable (AT1). As can be seen, the effects of the regulatory changes were stron- gest in those banks that already had a high AT1 level. This result further suggests that the new regulation exacerbated the ex-ante differences across banks in the quality of capital. Finally, we find evidence that systemic banks Figure 2: Quantile regression results -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0.05 0.2 0.35 0.5 0.65 0.8 0.95 The chart above shows the coefficient of the systemic*post2013 interaction term for different quantiles of the dependent variable 18 were more likely to decrease assets in the pe- riod analyze, which served as another means to boost capital ratios. Results regarding risk-taking decisions The essential question that remains is what the effect of these changes was on bank risk. On one hand, higher AT1 issuance should lead to greater asset volatility (Berg and Kaserer, 2015) and induce bank weakness in times of stress. On the other, the decline in RWA inten- sity would suggest that bank risk declined. We obtain some evidence that this has in- creased the risk of banks, as shown in Table 5. Risk factors like the SD of ROAA seem were negatively affected by the regulatory change. However, we find no increase of a worsening in the z score, defined as the ratio of profitability (measured as the Return On Average Assets, ROAA) over the standard deviation of ROAA for systemic institutions. As a result, overall, we find an inconclusive effect on bank risk. Systemic regulation may not have been effec- tive in reducing bank risk. However, this result is likely to depend on the level of ex ante capital at these institutions. We now test, with the same framework, how the results change when we consider the level of capital of institutions in the beginning of the period, by introducing a normalized ETA ratio (ET) as an interaction term. This is shown in Table 5 by the last term, which shows the inter- action of the post 2013 dummy, the systemic banks dummy and ET, which shows the equity to assets ratio in 2013. We find that higher capitalized used less AT1. This suggests that the drive to higher capital perpetuated the ex-ante differences in banks safety. The coefficient on AT1 is always at about 0.5, showing that systemic banks were more prone to use AT1. AT1 is defined as a ratio on total assets. We find low capitalized systemic banks were even more likely to use lower level capital (AT1, T2). The reduction of RWA density was particularly important for lower capitalized institutions, as shown in Table 5. This suggests that these in- stitutions tried to boost their capital ratios this way. This is not consistent with the increase in the standard deviation of ROAA, pointing to greater risk being taken on, in spite of the lower RWA intensity at the lower capitalized firms. We also find evidence that they reduced assets, and this was particularly true of the lower cap- italized banks, consistent with lower capital- ized banks being stingier with credit, and with the higher use of CoCo bonds. This is only true for the lower capitalized banks, which do show a decrease in their z score. It suggests a decline in RWA intensity was not a genuine decline in the risk of their portfolio. Our results suggest that recent regulatory changes, combined with prevailing market ac- cess by these institutions has led to a sharp in- crease in the risk profile of their capital base, in particular for those banks that started off in a relatively weak capital position. Indeed the result of the tightening in regulation was an increase in bank risk for the lower capitalized institution. Comparison with previous results in the litera- ture The results are broadly in line with the litera- ture. For instance, in terms of deleveraging without reducing assets for the whole sample are consistent with those of De Jonghe 2015. Our findings also suggest there are lags in the correction of ETA ratios (consistent also with De Jonghe, 2015) and that these depend on both macroeconomic and bank specific charac- teristics. That lowly capitalized institutions re- act differently to deleveraging has been docu- mented by Schepens (2016), and is confirmed by our results. Less capitalized firms used lower level capi- tal when asked to increase their capital ra- tios. This is consistent with these firms facing a higher cost of capital. It is consistent with Schepens (2016) finding that lower capitalized firms have a tendency to increase risk, as they are more prone to use debt. We add to those Table 5. Additional regression results using the Fitch support rating criteria for determining the counterfactual RWAINT LASSETS AT1 SDROAA ZSCORE LT2 LAGGED DEP 0.95 *** 0.98 *** 0.9 *** 0.77 *** 0.08 *** 0.76 *** ROAA 0.02 *** -0.0009 *** 0 -0.39 *** -0.07 * GDP -0.23 *** -0.01 *** 0.14 *** -0.19 *** -1.76 *** -0.55 * CPI -0.38 *** 0.015 *** 0.04 *** 0.021 -0.88 *** -0.03 RWAINT -0.07 *** Lassets 0.04 *** -0.03 *** *** 0.18 *** POST2013 0.39 *** -0.02 *** 0.08 *** -0.11 -1.848 *** -0.23 *** SYSTEMIC 0.36 *** 0.01 *** 0.007 0.12 -0.044 -0.02 POST2013*SYSTEMIC -1.06 *** -0.02 *** 0.47 *** 0.12 *** 0.05 0.52 *** POST2013*SYSTEMIC*ETN 0.2 *** 0.2 *** -0.04 *** 0.011 0.15 *** -0.1 *** C 4.27 *** 0.84 *** -1.59 *** 0.66 19.6 5.79 ** Number of observarions 525 525 525 525 525 525 R squared 0.7 0.85 0.74 0.88 0.65 0.65 Method GMM GMM GMM GMM GMM GMM Control group FITCHSUPPORT1 FITCHSUPPORT1 FITCHSUPPORT1 FITCHSUPPORT1 FITCHSUPPORT1 FITCHSUPPORT1 Note: LAGGED DEP is the lagged dependent variable. The different determinants are the levels of capital. ETA shows the equity to assets ratio, LT2 the log of Tier 2 capital, LT1 the log of Tier 1 capital, LT2 hybrid the use of Tier 2 hybrid instruments and AT1 the level of additional Tier 1 capital. The sample here is the systemic institutions and those institutions that, before 2011, had the largest support rating by Fitch. 20 findings, by showing that not only are they prone to issue more debt, but rather, they also use lower level capital, which adds more risk to their financial structure in times of stress. In line with the literature, lower capitalized firms are found to be more prone to reduce lending when asked for higher capital require- ments. They decreased the RWA intensity (more so than all systemic institutions, which did that too), however, the higher capital also led to lower z-score. 5. Concluding remarks The drive to build up stricter capital require- ments after the crisis should have positive ef- fects. These requirements build up the buffers available to banks, lowering the risk of default. However, when dealing with systemic institu- tions, there is a substantial gap in terms of how these institutions react to more stringent capi- tal requirements. Some of the perverse effects identified in the literature become particularly acute at these institutions. In particular, we provide evidence that they prone to using lower level capital, consistent with pecking order theory and ROE targeting by firms. As a result, bank weakness may actually have been enhanced by the new regulation. Further research is needed to understand the systemic effects of lower level capital. Their pricing, and in particular the spillovers to equity pricing can bring further difficulties. In addition, since most hybrid holders are banks themselves, the effects on financial stability can be worsened. In this context, a further avenue for research involves the effects of capital requirements. Tight capital requirements, like for instance basing the requirements only ion CET1 can bring the advantage that the destabilizing ef- fects are avoided. However, if the effect that dominates is the ROE stabilization, then this effect may be counterproductive. From a wel- fare perspective, it should be important to un- derstand which of the effects dominates. The premium is then on regulators. Debt like capital has certain benefits for financial stabil- ity over CET1, but the fact is that their design has been bad for welfare and it seems to have been used by riskier firms. The latter need not be bad. However, firms that are badly capital- ized should not use lower level capital, com- pensating it with more risk. In order to achieve this, a combination of larger buffers for riskier firms, more transparency regarding the key elements of capital, and strict regulation on the design and the magnitude of CoCo bonds would be a solution. 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Deutsche Pfandbriefbank AG Nordea Bank AB (publ) Dexia SA Royal Bank of Scotland Group Plc Erste Group Bank AG Société Générale SA FinecoBank SpA Standard Chartered Plc HSBC Trinkaus & Burkhardt AG UBS Group AG KBC Group NV UniCredit SpA Lloyds Banking Group Plc Nationwide Building Society Natixis SA Raiffeisen Bank International AG Svenska Handelsbanken AB (publ) Svenska Handelsbanken AB (publ) Swedbank AB (publ) Všeobecná úverová banka, a.s. Všeobecná úverová banka, a.s. Credit Mutuel Intesa San Paolo Rabobank Group DZ Bank KfW Group ABN AMRO Group NV Últimos títulos publicados WORKING PAPERS WP05/17 Álvarez, Ignacio; Uxó, Jorge. y Febrero Eladio.: Internal devaluation in a wage-led economy. The case of Spain. WP04/17 Albis, Nadia y Álvarez Isabel.: Estimating technological spillover effects in presence of knowl- edge heterogeneous foreign subsidiaries: Evidence from Colombia. 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WP 04/05 Álvarez, Isabel; Fonfría, Antonio; Marín Raquel: The role of networking in the competi- tive-ness profile of Spanish firms. WP 03/05 Kausch, Kristina; Barreñada, Isaías: Alliance of Civilizations. International Security and Cos- mopolitan Democracy. WP 02/05 Sastre, Luis: An alternative model for the trade balance of countries with open economies: the Spanish case. WP 01/05 Díaz de la Guardia, Carlos; Molero, José; Valadez, Patricia: International competitiveness in services in some European countries: Basic facts and a preliminary attempt of interpreta-tion. WP 03/04 Angulo, Gloria: La opinión pública española y la ayuda al desarrollo. WP 02/04 Freres, Christian; Mold, Andrew: European Union trade policy and the poor. Towards im-proving the poverty impact of the GSP in Latin America. WP 01/04 Álvarez, Isabel; Molero, José: Technology and the generation of international knowledge spillovers. An application to Spanish manufacturing firms. OCCASIONAL PAPERS OP01/16 Borrell, Josep; Mella, José María; Melle, Mónica; Nieto, José Antonio. “¿Es posible otra Euro- pa? Debate abierto.” POLICY PAPERS PP 01/15 De la Cruz, C.: Cambio, Poder y Justicia de Género en la Agenda 2030: Reflexiones para no perdernos en el camino. PP 01/14 Luego F.; Vicent L.: Encrucijadas de la moneda única. Algunas claves para una reflexión desde la periferia. PP 01/11 Monedero J.C., Democracia y Estado en Améríca Latina: Por una imprudente reinvención de la política. PP 02/10 Alonso, José Antonio; Garcimartín, Carlos; Ruiz Huerta, Jesús; Díaz Sarralde, Santiago: Strengthening the fiscal capacity of developing countries and supporting the international fight against tax evasión. PP 02/10 Alonso, José Antonio; Garcimartín, Carlos; Ruiz Huerta, Jesús; Díaz Sarralde, Santiago: For- talecimiento de la capacidad fiscal de los países en desarrollo y apoyo a la lucha internacional contra la evasión fiscal. 29 PP 01/10 Molero, José: Factores críticos de la innovación tecnológica en la economía española. PP 03/09 Ferguson, Lucy: Analysing the Gender Dimensions of Tourism as a Development Strategy. PP 02/09 Carrasco Gallego ,José Antonio: La Ronda de Doha y los países de renta media. PP 01/09 Rodríguez Blanco, Eugenia: Género, Cultura y Desarrollo: Límites y oportunidades para el cambio cultural pro-igualdad de género en Mozambique. PP 04/08 Tezanos, Sergio: Políticas públicas de apoyo a la investigación para el desarrollo. Los casos de Canadá, Holanda y Reino Unido. PP 03/08 Mattioli, Natalia Including Disability into Development Cooperation. Analysis of Initiatives by National and International Donors. PP 02/08 Elizondo, Luis: Espacio para Respirar: El humanitarismo en Afganistán (2001-2008). PP 01/08 Caramés Boada, Albert: Desarme como vínculo entre seguridad y desarrollo. La reintegración comunitaria en los programas de Desarme, desmovilización y reintegración (DDR) de com- batientes en Haití. PP 03/07 Guimón, José: Government strategies to attract R&D-intensive FDI. PP 02/07 Czaplińska, Agata: Building public support for development cooperation. PP 01/07 Martínez, Ignacio: La cooperación de las ONGD españolas en Perú: hacia una acción más estratégica. PP 02/06 Ruiz Sandoval, Erika: Latinoamericanos con destino a Europa: Migración, remesas y code- sa-rrollo como temas emergentes en la relación UE-AL. PP 01/06 Freres, Christian; Sanahuja, José Antonio: Hacia una nueva estrategia en las relaciones Un- ión Europea – América Latina. PP 04/05 Manalo, Rosario; Reyes, Melanie: The MDGs: Boon or bane for gender equality and wo-men’s rights? PP 03/05 Fernández, Rafael: Irlanda y Finlandia: dos modelos de especialización en tecnologías avan-zadas. PP 02/05 Alonso, José Antonio; Garcimartín, Carlos: Apertura comercial y estrategia de desarrollo. PP 01/05 Lorente, Maite: Diálogos entre culturas: una reflexión sobre feminismo, género, desarrollo y mujeres indígenas kichwuas. PP 02/04 Álvarez, Isabel: La política europea de I+D: Situación actual y perspectivas. PP 01/04 Alonso, José Antonio; Lozano, Liliana; Prialé, María Ángela: La cooperación cultural españo- la: Más allá de la promoción exterior. DOCUMENTOS DE TRABAJO “EL VALOR ECONÓMICO DEL ESPAÑOL” DT 16/11 Fernández Vítores, David: El papel del español en las relaciones y foros internacionales: Los casos de la Unión Europea y las Naciones Unidas. DT 15/11 Rupérez Javier: El Español en las Relaciones Internacionales. DT 14/10 Antonio Alonso, José; Gutiérrez, Rodolfo: Lengua y emigración: España y el español en las migraciones internacionales. DT 13/08 de Diego Álvarez, Dorotea; Rodrigues-Silveira, Rodrigo; Carrera Troyano Miguel: Estrate- gias para el Desarrollo del Cluster de Enseñanza de Español en Salamanca. 30 DT 12/08 Quirós Romero, Cipriano: Lengua e internacionalización: El papel de la lengua en la interna- cionalización de las operadoras de telecomunicaciones. DT 11/08 Girón, Francisco Javier; Cañada, Agustín: La contribución de la lengua española al PIB y al empleo: una aproximación macroeconómica. DT 10/08 Jiménez, Juan Carlos; Narbona, Aranzazu: El español en el comercio internacional. DT 09/07 Carrera, Miguel; Ogonowski, Michał: El valor económico del español: España ante el espejo de Polonia. DT 08/07 Rojo, Guillermo: El español en la red. DT 07/07 Carrera, Miguel; Bonete, Rafael; Muñoz de Bustillo, Rafael: El programa ERASMUS en el mar- co del valor económico de la Enseñanza del Español como Lengua Extranjera. DT 06/07 Criado, María Jesús: Inmigración y población latina en los Estados Unidos: un perfil socio- demográfico. DT 05/07 Gutiérrez, Rodolfo: Lengua, migraciones y mercado de trabajo. DT 04/07 Quirós Romero, Cipriano; Crespo Galán, Jorge: Sociedad de la Información y presencia del español en Internet. DT 03/06 Moreno Fernández, Francisco; Otero Roth, Jaime: Demografía de la lengua española. DT 02/06 Alonso, José Antonio: Naturaleza económica de la lengua. DT 01/06 Jiménez, Juan Carlos: La Economía de la lengua: una visión de conjunto. 31