BIS Working Papers
Macroprudential policy and bank risk
by Yener Altunbas, Mahir Binici and Leonardo Gambacorta
Monetary and Economic Department
JEL classification: E43, E58, G18, G28
Keywords: macroprudential policies, effectiveness, bank risk
BIS Working Papers are written by members of the Monetary and Economic Department of the Bank for International Settlements, and from time to time by other economists, and are published by the Bank. The papers are on subjects of topical interest and are technical in character. The views expressed in them are those of their authors and not necessarily the views of the BIS.
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ISSN 1020-0959 (print)
ISSN 1682-7678 (online)
Macroprudential policy and bank risk
Yener Altunbas , Mahir Binici and Leonardo Gambacorta
This paper investigates the effects of macroprudential policies on bank risk through a large panel of banks operating in 61 advanced and emerging market economies. There are three main findings. First, there is evidence suggesting that macroprudential tools have a significant impact on bank risk. Second, the responses to changes in macroprudential tools differ among banks, depending on their specific balance sheet characteristics. In particular, banks that are small, weakly capitalised and with a higher share of wholesale funding react more strongly to changes in macroprudential tools. Third, controlling for bank-specific characteristics, macroprudential policies are more effective in a tightening than in an easing episode.
JEL classification: E43, E58, G18, G28.
Keywords: macroprudential policies, effectiveness, bank risk.
Prior to the global financial crisis (GFC) financial stability was mainly considered from a microprudential perspective. The aim of supervisory policy was to reduce the risk that individual institutions would fail, without any explicit regard for their impact on the financial system as a whole or on the overall economy. Lehman Brothers' default reminded us that financial stability has a macroprudential or systemic dimension that cannot be ignored. Treating the financial system as merely the sum of its parts leads one to overlook the system's historical tendency to swing from boom to bust. Nowadays, financial stability is considered from a macroprudential perspective.
However, the implementation of a new macroprudential framework for financial stability raises a number of challenges. A first challenge is the evaluation of the effectiveness of macroprudential policies, especially when more than one tool is activated. Moreover, effectiveness should be analysed with respect to the specific goal that macroprudential policies are designed to achieve; that is, to increase the resilience of the financial system, or, more ambitiously, to tame financial booms and busts. At the moment, the evidence is mixed and most research focuses on analysing the impact of macroprudential tools on bank lending (as an intermediate target), not directly on bank risk (the limitation of which is the ultimate goal). For instance, recent evidence suggests that debt-to-income ratios and, probably to a lesser extent, loan-to-value ratios are comparatively more effective than capital requirements as tools for containing credit growth (Claessens et al, 2013). Indeed, the recent activation of the Basel III countercyclical capital buffer to risk-weighted domestic residential mortgages in Switzerland, though having had some effect on mortgage pricing, seems to have had little impact on credit extension (Basten and Koch, 2015). But the main goal of the Basel III buffers is to increase the resilience of the banking system, not to smooth the credit cycle. Restraining the boom is perhaps no more than a welcome, potential side effect (Drehmann and Gambacorta, 2012).
A second challenge pertains to the varied nature of macroprudential objectives and instruments. In this area, there is no one-size-fits-all approach. Which tools to use, how to calibrate them and when to deploy them will all depend on how the authorities view the vulnerabilities involved and how confident they are in their analysis. The legal and institutional setup will also be relevant. A given instrument's effects depend on a variety of factors, which have to be assessed against the chosen objective. Some instruments may work better to achieve the narrow aim of increasing financial system resilience rather than the broader aim of constraining the cycle. For instance, countercyclical capital buffers aim to build cushions against banks' total credit exposures, whereas loan-to-value ratio caps only affect new borrowers (and usually only those that are highly leveraged). This argues in favour of capital buffers if the objective is to improve overall resilience. However, loan-to-value ratios may be more effective if the aim is to curb specific types of credit extension.
Third, most macroprudential policies aim at containing systemic risk, a type of risk that is by nature endogenous. By using macroprudential tools, policymakers aim at limiting bank risk-taking and the probability of the occurrence of a financial crisis. This means that - ideally - we should also be interested in how these policies influence a bank's contribution to systemwide risk. Measurement of systemic risk is, however, still rudimentary, although some concepts have been developed (measures such as CoVaR, stress testing and Shapley values).
A first step could be to evaluate how macroprudential tools impact specific measures of bank risk, such as the expected default frequency (EDF) or the Z-score. The calculation of the EDF indicator requires bank issuance of equity on the stock market, while the Z-score is an indicator of the probability of default which relies on balance sheet variables.
This paper complements other studies on the effectiveness of macroprudential policies.1 Its main contribution is to analyses the effectiveness of such policies on bank risk in a comprehensive way, exploiting the cross-sectional dimension among countries. Interestingly, the more advanced economies tended to ignore the macroprudential dimension in the runup to the crisis. Emerging market economies (EMEs) were generally better aware of the need to think about the financial system as a whole, and more willing to intervene in response to evidence of a build-up of imbalances and risks (Figure 1). All this means that it is necessary to pool information for a large number of banks operating in both advanced countries and EMEs, and to control for different institutional setups and time-specific factors affecting the risk- taking channel. In other words, pooling information regarding countries with different experiences in the use of macroprudential tools greatly reduces concerns about possible omitted variables (Demirguc-Kunt et al, 2013).
Using information for 3,177 banks operating in both advanced economies and EMEs over the period 1990-2012, we find that macroprudential tools - both those focusing on dampening the cycle (ie loan to value ratios, reserve and currency requirements) and those specifically designed to enhance banks' resilience (ie capital requirements) - have a significant impact on bank risk. We also find that the responses to changes in macroprudential tools differ among banks, depending on their specific balance sheet characteristics. In particular, banks that are small, weakly capitalised and with a higher share of wholesale funding react more strongly to changes in macroprudential tools. Finally, macroprudential policies are more effective in a tightening than an easing cycle.
The remainder of this paper is organised as follows. The next section discusses how macroprudential policies can impact bank risk. Section III describes the identification strategy and data used in our analysis, while Section IV and V present the main results and robustness checks. The last section summarises our main conclusions.
2. Macroprudential policy and bank risk
Following a widely accepted definition, "macroprudential policies are designed to identify and mitigate risks to systemic stability, in turn reducing the cost to the economy from a disruption in financial services that underpin the workings of financial markets - such as the provision of credit, but also of insurance and payment and settlement services" (FSB/IMF/BIS, 2009). However, providing a framework for the relationship between macroprudential policies and systemic risk is not straightforward. The need for macroprudential policies arises from two dimensions of systemic risk: the time and cross-sectional dimensions.
The time dimension represents the need to constrain financial booms (Borio, 2014). Such financial booms can originate from both the supply and demand sides of agents, and financial intermediary behaviour. For example, the amplification mechanism known as "financial accelerator" is mainly related to the demand side (Claessens et al, 2013). But other mechanisms are related to the supply side, as in the model of Adrian and Shin (2010, 2014), where an initial positive shock that boosts the value of a bank's assets, such as loans and securities, could induce a further increase in debt if the bank targets a certain leverage ratio. Banks' decisions on leverage and the composition of assets and/or liabilities could make them more vulnerable to future negative shocks through balance sheet mismatches.
The second feature of systemic risk is its cross-sectional dimension, which is mainly related to the interconnectedness of financial institutions. This aspect became the focus of policy discussion after the GFC as specific shocks to some institutions were heavily amplified by spreading across financial markets and countries. The new Basel III regulatory framework, for instance, which targets systemically important financial institutions (SIFI) with specific capital surcharges, aims to reduce negative externalities stemming from interconnectedness.
The risk-taking behaviour of banks, thus, could be mitigated by the active use of macroprudential policies. For instance, capital-based instruments, such as capital conservation buffers, would allow institutions to accumulate capital in good times, which could then be used to absorb losses in stress periods. Similarly, the countercyclical capital buffer could be actively used to "achieve the broader macro-prudential goal of protecting the banking sector from periods of excess credit growth." (BCBS, 2010, pp 5). In addition, provisioning requirements, such as the dynamic provisioning tool used in Spain, also require banks to adjust the total amount of loss provisions when their profits are growing, with the aim of being able to draw on these provisions during an economic downturn. Therefore, the collective use of capital-based requirements could mitigate bank risk by requiring higher buffers during an upturn. Bank risk could be further mitigated by the use of other macroprudential tools during an upturn. For instance, increasing liquidity requirements and imposing stringent currency instruments could minimise bank risk emanating from repricing and liquidity gaps, as well as exchange rate fluctuations. Therefore, single or multiple uses of macroprudential instruments are expected to have an impact on the EDF or Z-score of banks, two alternative measures or bank risk used in this study.
Besides the direct effect of macroprudential tools on bank risk, monetary policy also has an impact on risk-taking and financial stability (Gambacorta, 2009; Borio and Zhu, 2014; Altunbas et al, 2014; Dell'Ariccia et al, 2010). A prolonged period of low interest rates could impact risk-taking in two different ways. The first is through the search for yield (Rajan, 2005). Low interest rates may increase incentives for asset managers to take on more risks for contractual, behavioural or institutional reasons. For example, in 2003-2004, many investors shifted from low-risk government bonds to higher-yielding but also to riskier corporate and EME bonds. A similar mechanism was detected in the theoretical model designed by Dell'Ariccia et al (2010): monetary easing leads to a reduction in the interest rate on bank loans, which, in turn, reduces a bank's gross return, conditional on its portfolio. This reduces the bank's incentive to monitor its loans, and the real yield on safe (monitored) assets, thus banks will typically increase their demand for risky assets.
The second way in which low interest rates could encourage banks to take on more risk is through their impact on valuations, incomes and cash flows.2 A reduction in the policy rate boosts asset and collateral values, which in turn can modify bank estimates of probabilities of default, losses given default and volatilities. For example, by increasing asset prices, low interest rates tend to reduce volatility and thus risk perceptions: since a higher stock price increases the value of equity relative to corporate debt, a sharp increase in stock prices reduces corporate leverage and could thus decrease the risk of holding stocks.3 This example can be applied to the widespread use of Value-at-Risk methodologies for economic and regulatory capital purposes (Danielsson et al, 2004). As volatility tends to decline in rising markets, it releases the risk budgets of financial firms and encourages position-taking. A similar argument is made in the model of Adrian and Shin (2009), who stress that changes in measured risk determine adjustments in bank balance sheets and leverage conditions, and, in turn, amplify business cycle movements.
Macroprudential tools could, in principle, be used to moderate the risk-taking incentives arising from monetary policy decisions. For instance, Igan and Kang (2011) argue that the impact of a tightening of monetary policy on defaults can be contained by having in place conservative limits on debt-to-income (DTI) ratios. On the other hand, macroprudential measures, such as limits on LTV ratios, can reduce vulnerabilities under the condition that accommodative monetary policy is driving up asset prices. Additionally, higher capital requirements (including countercyclical) or tighter leverage and liquidity ratios may help contain increases in bank risks in response to expected lax monetary policy (see Farhi and Tirole, 2012; IMF, 2013). To complement the theoretical discussions outlined above and individual country studies, the analysis in this paper controls for monetary policy conditions and a broader set of country- and bank-specific characteristics.
3. Model, identification strategy and data
The baseline empirical model is given by the following equation, adapted from Altunbas et al (2014):
with i=1,..., N , k= 1, ...,K and t=1, ..., T, where i is the bank, k is the country and t is time. Table 1 reports the summary statistics for the variables used and the relevant sources. The final database includes 3,177 banks headquartered in 61 countries.5 More information at the country level is provided in Annex A.
In the baseline equation (1), the annual change of the risk measure (ARisk) for bank i, headquartered in country k, in year t, is regressed on its own lag and EDF change for the non- financial sector in country k (AEDF_NF). This variable aims at filtering out the effects of changes in the market price of risk due to the business cycle. MP indicates the change in the macroprudential tool, which could be the change in an aggregate index, as in Cerutti et al (2017), or a complete vector of macroprudential tools. BSC and MC represent, respectively, additional bank-specific characteristics and macro variables that are introduced to disentangle the risk-taking channel from other mechanisms at work. In particular, the vector MC includes a measure for the monetary policy stance (DIFF, the difference between the real interest rate and the natural rate) and the growth rate of nominal GDP (AGDP).6 We also include time invariant bank fixed effects (9i) and a dummy variable (Kkt) that takes the value of 1 in those specific years in which countries experienced a banking crisis and zero elsewhere (Valencia and Laeven, 2012).
3.1 Measurement of bank risk
By setting macroprudential tools, policymakers aim to limit bank risk-taking and the probability of the occurrence of a financial crisis. This means that - ideally - we should measure how macroprudential policies influence a bank's contribution to system-wide risk. Measurement of systemic risk is, however, still rudimentary, although some concepts have been developed (CoVaR, stress testing and Shapley value measures). A compromise could be to evaluate how macroprudential tools impact specific measures of bank risk.
In the baseline model, the dependent variable is given by the change in the EDF (AEDF), representing the probability that a bank will default within a given time horizon (typically one year). EDF is a well-known, forward-looking indicator of risk, computed by Moody's KMV, which builds on Merton's model to price corporate bond debt (Merton, 1974). The EDF value, expressed as a percentage, is calculated by combining banks' financial statements with stock market information and Moody's proprietary default database. We also checked the robustness of the results by using change in the Z-score as an alternative measure of bank risk. 7 We mitigate the effects of outliers by dropping the first and the last percentile of the distribution of the variables. Figure 2 shows that the cross-sectional dispersion of banks' EDFs and Z-scores (both measured by means of the coefficient of variation) is not concentrated in the period of the GFC. This means that there were already significant differences in bank risk at the cross-sectional level prior to the crisis. Interestingly the cross-sectional dispersion of the Z-score is also very high in relation to the early 1990s' recession and associated banking crisis.
In Table 2, banks are grouped depending on their specific risk position, using one-year EDF values. For the bank-specific characteristics, we use bank-level data from BankScope, a commercial database maintained by Fitch and Bureau van Dijk. A "high-risk" bank has the average EDF of banks included in the tenth decile (ie in the 10% of the riskier banks with an average EDFH equal to 7.4%); a "low-risk" bank has the average EDF of the banks in the first decile (EDFL is equal to 0.07%). The first part of the table shows that high-risk banks are less strongly capitalised. The lower level of capitalisation appears to be consistent with the higher perceived risk of these banks. Additionally, low-risk banks make relatively more loans than high-risk banks, and are more efficient (have a lower cost-to-income ratio).
Bank profitability, measured by return on assets (ROA), is higher and more stable for low- risk banks. This result is probably due to the inclusion of the GFC period in the sample. The coefficient of variation of the ROA, calculated using information for the four quarters ahead, for low-risk bank is indeed half (one quarter) with respect to high-risk banks, considering the EDF (Z-score) as a measure of risk.
It is worth noting that banks with a lower Z-score are more risky, while banks with a lower EDF are less risky. To compare the signs of the coefficients in the regressions, we therefore multiply the Z-score by -1. Using this approach, a higher level of the two indicators (Z-score and EDF) is always associated with more risky banks.
3.2 Macroprudential policy indicators
The construction of macroprudential policy indicators involves a number of steps. First, we consider an aggregate index that allows us to evaluate the overall effectiveness of macroprudential tools when more than one measure is activated. This aggregate index represents a very rough approximation because macroprudential tools may be very different in nature. For example, we may need to consider a case where the minimum loan to value ratio was increased while, contemporaneously, reserve ratios were reduced. To deal with this kind of situation, we first consider a dummy that takes the value of +1 if a given macroprudential tool was tightened and -1 if it was eased, leaving zero elsewhere. Then, following Kuttner and Shim (2013), we calculate an aggregate macroprudential indicator (MP_indexk,t) that sums up all the different dummies for the various macroprudential tools. This means that, if multiple actions in the same direction are taken within a given year, the variable could take on the values of 2 or -2, or even 3 and -3. It also means that a tightening action and a loosening action taken within the same year could cancel each other out. This indicator weights each tool in the same way and will be considered in our baseline regression.
Second, we recognise that the macroprudential toolkit tends to be large, as it combines an array of different instruments. In particular, we distinguish them according to the following five categories: a) capital-based instruments; b) liquidity-based instruments; c) asset-side instruments; d) reserve requirements; and e) currency requirements. Table 3 provides an overview of these categories (with further information in Annex B).
Third, the purpose of the various policies could differ. For instance, some instruments are intended to increase directly the financial sector's resilience, while others focus on dampening the cycle as an intermediate target. In that respect, the effects of specific macroprudential tools on credit growth and bank risk can be different. Borio (2011) and Claessens et al (2013) distinguish between the goals and the types of policy that are commonly used. Macroprudential tools with the main objective of enhancing the financial sector's resilience include countercyclical capital requirements, leverage restrictions, general or dynamic provisioning, and the establishment of liquidity requirements, among others. Within the category of macroprudential tools aimed at dampening the credit cycle, Claessens et al (2013) include changes in reserve requirements, variations in limits on foreign currency mismatches, cyclical adjustments to loan-loss provisioning, and margins or haircuts. Other macroprudential policy aims include reducing the effects of contagion or shock propagation from SIFIs or networks. This group might also include policies, such as capital surcharges linked to systemic risk, restrictions on asset composition or activities.
Using the categorisation presented in Borio (2011) and Claessens et al (2013), we classify policies according to their purpose. In particular, policies to dampen the cycle - ie those used by authorities countercyclically to dampen an expected credit boom or credit crunch - are identified with by term cyclical (we refer to the categories (c), (d) and (e) in Table 3). Macroprudential tools with a more structural objective, which are intended to increase the resilience of the financial sector (such as capital, liquidity or provisioning requirements), are identified with by the term resilience (categories (a) and (b) in Table 3).
The chart pie on the left-hand side of Figure 3 splits the different types of macroprudential policy adopted in the period 1990-2014. Interestingly, only one quarter of policies are aimed at improving the resilience of the financial sector using capital, liquidity of provisioning requirements (slices in blue colour). By contrast, the vast majority have the purpose of dampening the cycle - ie those used by the authorities countercyclically to dampen an expected credit boom or credit crunch. More than half are represented by changes in reserve requirements.
Finally, we split the changes in macroprudential tools into easing and tightening cases. In this way, we can verify the asymmetric effects of each tool. The chart pie on the right-hand side of Figure 3 shows that in three quarters of cases macroprudential tools were tightened. The dummy MP_easing (MP_tightening) takes a value of 1 if the macroprudential tool was eased (tightened) in a given year and zero elsewhere. This specification is particularly important to check our results against the existing literature. Cerutti et al (2017), for example, find some evidence of the asymmetric impact of macroprudential policies, claiming that those policies seem more effective when credit growth rates are very high, but have a less positive impact during busts. Similarly, Claessens et al (2013) find that macroprudential policies help mitigate asset growth, with the effects largely present during the boom (implying that the tightening measures are more effective). Finally, Kuttner and Shim (2013) find that three of the four macroprudential policies analysed in their study have statistically significant effects on housing credit when measures are tightened but not loosened. However, they find similar but weaker asymmetric responses when they assess the impact of macroprudential policies on house prices.
3.3 Bank-specific characteristics
In order to discriminate between loan supply and demand movements, the bank lending channel literature has focused on cross-sectional differences across banks. This strategy relies on the hypothesis that certain bank-specific characteristics (for example, bank size, liquidity, capitalisation and funding composition) only influence loan supply while a bank's loan demand is largely independent of these factors. Broadly speaking, this approach assumes that, after a monetary tightening, the drop in the total availability of funding, which affects banks' ability to make new loans or their ability to shield their loan portfolios, differs among banks.
Drawing on this literature, we analyse macroprudential tools in the same way as monetary policy changes. Using the BankScope database, we therefore include four bank-specific characteristics that could influence bank supply shifts in the case of macroprudential policy changes. The first three are: bank size, proxied by the logarithm of a bank's total assets (SIZE), the liquidity ratio (LIQ) and the capital to asset ratio (CAP). These give insightful information, not only on banks' ability to insulate loan supply from monetary and macroprudential shocks (Kashyap and Stein, 2000; Kishan and Opiela, 2000; Gambacorta, 2005) but also control for "too big to fail" considerations, differences in business models and capital regulation effects. The fourth indicator is the share of deposits over total liabilities (DEP), a measure of a bank's contractual strength. Banks with a large amount of deposits will adjust their deposit rates by less (and less quickly) than banks whose liabilities are mainly composed of variable rate bonds that are directly affected by market movements (Berlin and Mester, 1999). Intuitively, this should mean that, in view of the presence of menu costs, it is more likely that a bank will adjust its terms for passive deposits if the conditions relating to its own alternative form of refinancing (ie bonds) change. Moreover, a bank will refrain from changing deposit conditions because, if the ratio of deposits to total liabilities is high, even small changes to their price will have a substantial effect on total interest rate costs. By contrast, banks that use relatively more bonds than deposits for financing purposes come under greater pressure because their costs increase contemporaneously with market rates (and to a similar extent). Finally, the ratio of bank deposits over total liabilities is also influenced by the existence of deposit insurance, which makes this form of funding more stable and less exposed to the risk of a run. Acharya and Mora (2015) report that banks may actively manage the deposit to total funding ratio by changing deposit rates.
To draw a parallel with the bank lending channel literature, it is interesting to investigate whether the responses to macroprudential shocks differ by type of bank. To test for this, we introduce interactions terms that are the products of a macroprudential indicator and bank- specific characteristics (MPkt *BSCikktt_i):
Similarly, with the approach used by the bank lending channel literature, the relevant test is on the significance of. Broadly speaking, this approach assumes that after a monetary tightening episode (macroprudential tightening in our case), the ability to shield loan portfolios is different across banks. In particular, small and less strongly capitalised banks, which suffer from a high degree of informational frictions in financial markets, face a higher cost in raising non-secured deposits and are forced to reduce their lending by more than other banks; illiquid banks have fewer options for shielding themselves from the effect of a prudential policy tightening on lending simply by drawing down cash and securities. Therefore, this literature does not analyse the macroeconomic impact of the "bank lending channel" on loans but asserts the existence of such a channel, based on the fact that different responses of lending supply among banks are detected. All bank-specific characteristics have been "demeaned" so that the coefficients X and S can be considered to be the effects on the average bank.
3.4 Endogeneity issues
One possible limitation of the suggested empirical strategy is that, in principle, the situation of the banking sector could also have an impact on macroprudential policy decisions. In order to mitigate endogeneity problems, we use the dynamic Generalised Method of Moments (GMM) panel methodology to obtain consistent estimates of the relationship between macroprudential policy and bank risk. This methodology was first described by Holtz-Eakin et al (1988), and Arellano and Bond (1991), and further developed by Blundell and Bond (1998). The use of this methodology reduces any endogeneity bias that may affect the estimation of the regression parameters. It also takes into account the heterogeneity of the data caused by unobservable factors affecting individual banks.
We use the instruments as defined by Blundell and Bond (1998). According to these authors, the exogenous variables, transformed in first differences, are instrumented by themselves, while the endogenous regressors (also transformed in first differences) are instrumented by their lags in levels. As a final precaution, we consider all bank-specific characteristics at t-1.
The main results are reported in Tables 4 to 7. The S-GMM estimator ensures efficiency and consistency, provided that the residuals are not subject to serial correlation of order two (AR(2) test), and that the instruments used are valid (Hansen test). Neither test (as reported at the bottom of each table) should fail to reject the null hypothesis.
Table 4 presents the baseline regression results of specifications (1) and (2) using the MP_index. The table is split into two parts: the first two columns use the EDF as dependent variable, while the last two columns use the Z-score. To make results comparable, we multiply the Z-score by -1. In this way larger values of both the EDF and Z-score indicate higher risk.
The coefficients on the MP_index are negative and significant, indicating that a tightening (easing) of macroprudential policies reduces (increases) bank risk. All coefficients for bank- specific indicators are highly significant for the EDF and Z-score in our baseline model.
The interaction terms between the MP_index and bank specific characteristics in column (2) and (4) indicate that the impact of macroprudential policies on bank risk is stronger for banks that are weakly capitalised, smaller, with low liquidity buffers and with a higher incidence of wholesale funding (fewer deposits). These results are in line with Gambacorta and Shin (2016): well capitalised banks are considered as less risky by the market and pay less - other things being equal - on their debt funding. Moreover, banks with a large proportion of deposits are considered safer because of the presence of deposit insurance.
Figure 4 summarises the effects of macroprudential tools for banks with different levels of capital. The estimates roughly imply that a tightening of macroprudential tools leads to a decline in the expected default probability of around 0.7 percent for the average bank. The effect is higher for weakly capitalised banks (-0.9 percent) than for strongly capitalised ones (-0.4 percent), which have better access to markets for non-reservable liabilities. It is worth remembering that testing the null hypothesis that macroprudential policies effects are equal among banks with different capital ratios is identical to testing for the significance of the interaction between capital and the macroprudential policy indicator (see the coefficient on MP_indext*CAP t-1 in the second column of Table 4). Similar results are detected considering the Z-score as indicator of bank risk (see Figure 4 and the fourth column of Table 4).
The analysis of the other control variables also provides interesting insights. The positive value of the lagged dependent variable indicates persistence in the adjustment process of risk. Changes in the EDF of the non-financial sector are positively linked to banks' EDF and Z- scores. This implies that the risks affecting financial firms are driven by broad movements in risk that are related to the overall behaviour of the economy (captured by non-financial sector risk). As indicated by the risk-taking channel, the monetary policy indicator (the difference between the real interest rate and the natural rate) is negatively correlated with bank risk. This means that a less restrictive monetary policy is associated with a higher level of bank risk. The state of the business cycle (growth rate of nominal GDP) is also negatively correlated with changes in bank risk-taking. However this effect is statistically significant only when the Z- score is used as a risk-taking measure.
Table 5 presents the results of model (2) where the MP_index is divided in two separate indices, one for macroprudential tools aimed at dampening the credit cycle (MP_cyclical index for categories (c), (d) and (e) in Table 3) and another one for macroprudential tools whose main objective is to enhance the financial sector's resilience (MP_resilience index for categories (a) and (b) in Table 3).
We also find in this case the expected negative sign on the two macroprudential indices: a tightening (easing) of macroprudential policies reduces (increases) risk for the average bank (remember that all bank-specific characteristics are demeaned). The interaction of the two macroprudential indices with bank-specific characteristics confirms that the impact of macroprudential policies on bank risk is stronger for banks that are weakly capitalised, smaller, with low liquidity buffers and with a higher incidence of wholesale funding. Interestingly, the magnitude of the interaction terms is greater for macroprudential tools whose main objective is to enhance the financial sector's resilience. This is not surprising as these tools (capital and liquidity requirements) have a more direct impact on banks' credit supply.
In the first column of Table 6, we extend the analysis in two ways. First, we consider the possibility of asymmetric effects for a tightening and an easing of macroprudential policies. Second, we consider the five macroprudential categories described in Table 3. We find, in general, the expected signs. In the majority of cases, macroprudential tightening has a negative and significant impact on bank-risk, while easing has a positive effect. Similar results are obtained in the first column of Table 7 where we use changes in the Z-score as dependent variable rather than changes in the EDF. There are, however, cases (depending on the measure of bank risk used) in which some macroprudential tools do not produce significant effects on a bank's risk. These cases have to be further investigated because the effect may not be homogenous among banks, ie affecting some banks with certain specific characteristics more than others.
Another finding is that the effects are not always symmetric in magnitude for the average bank. However, the difference between the coefficients MP_easing and MP_tightening are in most cases not statistically significant. There is a slight tendency for asset class measures (such as changes in LTV or debt to income ratios) and, to some extent, currency tools to be more effective in an easing than in a tightening. On the contrary, reserve requirements seem more effective in a tightening but only when EDF is considered as a bank risk indicator. Moreover, in this case, the asymmetry needs further analysis by considering how banks with different characteristics react to changes in macroprudential tools.
To this end, we extend the model by inserting interaction terms that are the products of macroprudential indicators and bank-specific characteristics (see equation (2)). As pointed out in section 3, this is similar to the approach taken by the bank lending channel literature, which identifies shifts in the supply of loans by considering a different reaction of banks to monetary policy shocks depending on their characteristics. In columns (II) to (V) of Table 6, we report estimation results for equation (3) for each bank-specific characteristic, one at a time. Table 7 does the same but considers the Z-score as a dependent variable. Three main results emerge.
First, many interaction terms (17 out of 40 for EDF; 24 out of 40 for Z-score) are statistically significant, indicating that macroprudential policies have heterogeneous effects across banks. Table 6 results indicate that the significance and sign of the coefficients for the five groups of macroprudential tools in specifications (II) to (V) are consistent, in general, with those expected from theory.
In particular, banks that are small, weakly capitalised and with a low proportion of deposit funding (more wholesale funding) react more strongly to macroprudential shocks. Given that small and less strongly capitalised banks suffer from a higher degree of informational friction in financial markets, and face higher costs in raising non-secured deposits, then macroprudential measures would be expected to have a larger impact on their risk-taking capacity. Liquidity does not seem to affect significantly a bank's risk response to macroprudential changes, with the notable exception of a tightening of reserve requirements, against which liquid banks seem perfectly insulated.
Third, controlling for bank characteristics, macroprudential tools are more effective in a tightening than in an easing episode. For instance, in Table 6 for EDF, 11 out of 20 interaction terms are significant at a conventional level for tightening measures, while it is 6 out of 20 for easing interactions. A similar pattern of interaction terms exists for the Z-score in Table 6 (14
out of 20 for tightening; 10 out of 20 for easing). Heterogeneity is particularly evident when considering banks with a different degree of leverage, in particular for the Z-score and deposit ratio for EDF results. The higher effectiveness of tightening measures when bank-specific interactions are considered is in line with Claessens et al (2013), Cerutti et al (2017) and McDonald (2015).
5. Robustness checks
In this section, we perform a number of tests to check the robustness of our results to: i) the presence of possible heterogeneity in the effectiveness of macroprudential tools caused by different stages of economic and financial development across countries; ii) the effects of the GFC in the last part of the sample; iii) the effects of global macroeconomic and financial conditions; and iv) possible limits in our data coverage.
Regarding possible difference in the effectiveness of macroprudential tools across jurisdictions, we divided the sample (3,177 banks and 20,870 observations) between advanced economies (2,286 banks and 15,144 observations) and EMEs (891 banks and 5,756 observations).10 This test is particularly relevant as the data for EMEs typically contain more gaps and are only available for a limited number of financial intermediaries. The results reported in Tables C1-C4 in Annex C indicate that in both groups of countries, macroprudential policies have a significant impact on banks' risk-taking. Figure 5 reports the average effect of a macroprudential policy tightening, distinguishing those tools aimed at dampening the cycle (Cyclical) from those whose main objective is to enhance the financial sector's resilience (Resilience). For example, on average, macroprudential tightening reduces the probability of a bank's default by 0.35% (first histogram in the left-hand panel). The effect is higher in advanced economies (-0.47%) than in EMEs (-0.15%). Similar effects can be detected in terms of Z-scores.
In addition, risk for banks that are small, less well capitalised and with a higher share of wholesale funding reacts more strongly to changes in macroprudential tools aggregated into an MP index (Tables C1 and C3). In both groups of country, the tools that primarily aim at enhancing resilience (MP resilience index) and those that focus above all on taming financial booms and busts (MP_cyclical index; see Tables C2 and C4) have an impact on bank risk.
In the second robustness test, we limit our analysis to the pre-crisis period. Table C5 and C6 report the results for the baseline regressions estimated over the period 1990-2007. Even after losing one third of the observations, the results are qualitatively very similar.
In the third test, we add to the equations a complete set of time dummies to capture changes in global macroeconomic and financial conditions. The results reported in Tables C7 and C8 remains very similar. The robustness of the results is also confirmed when we include in the specification a full set of country*time fixed effects (and drop the macroeconomic controls): sign and significance of the interaction terms between macroprudential index and bank-specific characteristics remain qualitatively very similar (see Table C9).
The above results may also be influenced by differences in the intensity of bank supervision, unrelated to macroprudential policies, which could have an impact on the amount of risk undertaken (Beltratti and Stulz, 2012). In a fourth test, we therefore verify whether more permissive legislation on bank activities could have led financial intermediaries to take more risks. Following Karolyi and Taboada (2015), we construct a Regulatory Strength Index (RSI) using the logarithm of the sum of four indices that measures the quality of bank regulation from Barth et al (2013). As the index is available only for the period 1999-2012, the number of observations drops to 16,615. Tables C10 and C11 report the results including this indicator. We also report, for completeness, the coefficient of the crisis dummy Kkt (used in all specifications) which takes the value of 1 in those specific years during which countries experienced a banking crisis and zero elsewhere (Valencia and Laeven, 2012). Interestingly, the dummy that captures regulatory strength is negatively correlated with both measures of bank risk. At the same time, the dummy that captures the crisis picks up the effect of financial distress on banks. All other results remain practically unchanged.
The use of the EDF measure as dependent variable for risk reduces the number of observations because this indicator is available only for a limited number of banks. As a final robustness check, we run the baseline regressions on a larger sample of financial intermediaries using only the Z-score. This allows us to increase the number of banks from 3,177 to 17,963, and the number of observations from 20,870 to 115,611. The results reported in Table C12 are qualitatively very similar.
The global financial crisis highlighted the importance of financial stability, and hence the need for macroprudential policies to achieve that objective. Particularly during and after the crisis, many countries began to implement various macroprudential tools to deal with financial vulnerabilities and mitigate systemic risk. Recent theoretical and empirical literature has focused on various aspects of macroprudential policies, including the effectiveness of those policies and their implications for business and financial cycles.
This paper fills an existing gap in the literature. In particular, while other studies focus on the impact of such policies on bank lending, our paper analyses their effectiveness on bank risk. We do this in a comprehensive way, exploiting the cross-sectional dimension of countries for a large panel of banks operating in 61 advanced economies and emerging market economies over the period ranging from 1990 to 2012.
The paper presents three main results. First, it provides evidence suggesting that macroprudential tools are effective in modifying bank risk-taking. Second, the responses to change in macroprudential tools differ among banks depending on their balance sheet characteristics. In particular, banks that are small, weakly capitalised and with a higher share of wholesale funding react more strongly to changes in these tools. Third, macroprudential policies seem more effective in a tightening than in an easing phase.
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Annex B: Additional details on the construction of the database on macroprudential tools
Our primary data sources for macroprudential measures are Shim et al (2013) and Lim et al (2011, 2013).
The first data base covers policy actions on housing markets for 60 economies worldwide from January 1990 (or earliest date available) to June 2012. It draws on a variety of sources including official documents from central banks' and regulatory authorities' annual reports, financial stability reports, monetary policy bulletins. Shim et al (2013) complement and cross-check official sources and documents with Borio and Shim (2007), survey by the Committee on the Global Financial System (CGFS) on macroprudential policy conducted in December 2009, Hilbers et al (2005), Crowe et al (2011), Lim et al (2011), and Tovar et al (2012). Thus, the database covers a wide range of countries and measures, as well as a long time span on macro-prudential measures. The policy actions in the database are categorized under general and targeted credit policy measures including minimum reserve requirements, liquidity requirements and limits on credit growth, maximum loan-to-value ratios, maximum debt-service-to-income ratios, risk weights on housing loans, provisioning requirements (general loan-loss provisioning ratios and specific provisioning ratios applied to housing loans) and exposure limits on banks to the housing sector. While the main aim of Shim et al (2013) is to document policy actions related to the housing market, they also include measure in the data set even if a central bank or another other authority changes policy decision for reasons other than the state of the housing market. Thus, their data set contains prudential measures taken from both microprudential and macroprudential perspectives.
The second study that we use to construct macro-prudential measures is Lim et al (2013) which is based on the 2010 IMF survey on Financial Stability and Macroprudential Policy. Lim et al (2013) update and extend the survey to assess if institutional arrangements can affect the timely use of macroprudential policy instruments by evaluating policy response time under different institutional arrangements for a sample of 39 countries. Another study that also uses the IMF survey in 2010 is Lim et al (2011) that provides a comprehensive empirical study on the effectiveness of macroprudential instruments by using data from 49 countries over the period of 2000-2010. The IMF survey and both reference studies identify 10 instruments that have been most frequently applied to achieve macroprudential objective that is "to limit the risk of widespread disruptions to the provision of financial services and thereby minimize the impact of such disruptions on the economy as a whole." (IMF, 2011, pp 7). These instruments are classified as credit-related ( ie, caps on the loan-to-value ratio, caps on the debt-to-income ratio, caps on foreign currency lending and ceilings on credit or credit growth); liquidity-related (i.e. limits on net open currency positions/currency mismatch, limits on maturity mismatch and reserve requirements), and capital-related (i.e. countercyclical/time-varying capital requirements, time-varying/dynamic provisioning, and restrictions on profit distribution) measures.
Using the two sources above we construct a database of macro-prudential measures for 64 countries from 1990 to 2012. The macro-prudential measures are classified under 10 categories including credit growth limits (Credit), liquidity requirements (Liq), maximum debt-service-to-income ratio and other lending criteria (DSTI), capital requirement/risk weights (RW), provisioning requirement (Prov), limits on banks' exposure to the housing sector (Expo), reserve requirement (RR), maximum loan- to-value ratio and loan prohibition (LTV), limits on net open position (NOP), and foreign currency lending limits (FCL). If a policy action is covered by both Shim et al (2013) and Lim et al (2013), then we compare and cross-checks both data bases, and include additional measures such as limits on net open positions and foreign currency lending as these instruments are not directly or indirectly aiming policy actions on housing markets. The set of instruments covered here would capture broad categories of systemic risk that could be linked to risk-taking channel such as risks arising from strong credit growth and credit-driven asset price inflation, excessive leverage and the consequent deleveraging, systemic liquidity risk, and risks related to large and volatile capital flows, including foreign currency lending.
Given the heterogeneity across policy instruments and actions, we follow Kuttner and Shim (2013) to create monthly variables that take on three discrete values: 1 for tightening actions, -1 for loosening actions and 0 for no change. Since the frequency of bank level and macroeconomic data used in our study is annual, then the monthly observations are summed to create yearly time series. Thus, both policy action's intensity and their directions would be captured by summing monthly data over each year which could take value of -1 or 1; -2 or 2; -3 or 3, and up to -12 or 12 or more. When macroprudential index is constructed for each instrument, and tightening and easing actions are aggregated over the year, then actions in opposite directions may cancel each other leaving with no net change. In final analysis, we investigate also the impact of easing and tightening separately using indicator variable 0 or 1 (no change or change in policy measure).
Based on our coding we observe 1,047 policy actions associated with ten different types of macroprudential tools (see Table 3). Among these policy measures reserve requirements are the most frequently used ones followed by loan-to-value ratio and capital requirements/risk weights. The reason for the frequent changes in reserve requirements is that this policy tools could be used for broad purposed, and could directly influence the liquidity conditions in the market. Additionally, in same cases in emerging economies, it also serves as capital flow management device by setting higher rates on foreign currency and external short term funding of the banking sector or longer maintenance period for certain types of liabilities.