BIS Working Papers
How does the interaction of macroprudential and monetary policies affect cross-border bank lending?
by Előd Takáts and Judit Temesvary
Monetary and Economic Department
JEL classification: D22, D84, E31
Keywords: inflation expectations, firms' survey, new information.
This publication is available on the BIS website (www.bis.org).
© Bank for International Settlements 2019. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated.
ISSN 1020-0959 (print)
ISSN 1682-7678 (online)
How does the interaction of macroprudential and monetary policies affect cross-border bank lending?
by Előd Takáts and Judit Temesvary
Bank for International Settlements
Federal Reserve Board
First draft: December 2018
This draft: May 2019
We combine a rarely accessed BIS database on bilateral cross-border lending flows with cross-country data on macroprudential regulations. We study the interaction between the monetary policy of major international currency issuers (USD, EUR and JPY) and macroprudential policies enacted in source (home) lending banking systems. We find significant interactions. Tighter macroprudential policy in a home country mitigates the impact on lending of monetary policy of a currency issuer. For instance, macroprudential tightening in the UK mitigates the negative impact of US monetary tightening on USD-denominated cross-border bank lending outflows from UK banks. Vice-versa, easier macroprudential policy amplifies impacts. The results are economically significant.
JEL classification: D22, D84, E31
Keywords: inflation expectations, firms' survey, new information.
Central banks and financial regulators use macroprudential tools increasingly frequently after the global financial crisis (IMF-FSB-BIS, 2016). However, our understanding of macroprudential policies, partly because of their short history, is imperfect. For instance, we do not yet understand well how macroprudential policy works together with monetary policy. Given that macroprudential and monetary policies are used in conjunction, understanding their interaction is critical (Yellen, 2010; Claessens, 2013; Praet, 2018). Yet, identifying the interaction between macroprudential and monetary policies is wrought with difficulties, precisely because they are used together: They respond to similar variables, such as credit growth, and often operate through similar channels, such as the cost of bank credit. This makes the identification of the policy interaction particularly challenging.
We apply a novel identification strategy using international data to shed light on the interaction between macroprudential and monetary policies. Our identification relies on focusing on a monetary policy that is exogenous to the macroprudential policy, yet affect the same lending flows. We jointly examine (1) the currency-specific monetary transmission in international bank lending ("currency dimension of the bank lending channel" as detailed in Takats and Temesvary (2016)) and (2) source lending banking system-specific macroprudential policies, in driving cross-border bank lending. In our benchmark specifications, we apply a generalization of the Khwaja and Mian (2008) identification method, including country-time fixed effects to control for demand conditions in borrowers' countries - thereby identifying the policy interaction.
To see how we identify the policy interaction, consider an example of USD-denominated cross-border bank lending from UK banks. The currency dimension of the international bank lending channel posits that US monetary policy affects cross-border bank lending denominated in USD, even if the US is neither the source bank lending system nor the borrowers' country (Takats and Temesvary, 2016). As an example of this channel, US monetary policy tightening would reduce UK-headquartered banks' USD-denominated lending to Malaysia. At the same time UK macroprudential policies also affect this cross-border bank lending. For instance, macroprudential tightening, by making domestic bank lending relatively more expensive, may drive UK banks' lending outward and thereby increase cross-border bank lending. In the context of this example, we investigate how UK macroprudential tools interact with US monetary policy in affecting USD-denominated cross-border bank lending outflows from the UK banking system. The fact that US monetary policy is (almost fully) exogenous to UK macroprudential policy provides an identification which would be impossible to obtain in a singlecountry setup.
We construct a unique dataset from three sources to undertake the identification. We use the "Stage 1 Enhancements" to the Bank for International Settlements' (BIS) International Banking Statistics. This dataset uniquely allows us to identify the currency dimension of the (international) bank lending channel, that is, monetary policy transmission through the currency denomination of cross-border bank lending (Takats and Temesvary, 2016). With the help of this dataset, we examine cross-border bank lending denominated in the three major internationally used currencies: the US dollar (USD), the euro (EUR) and the Japanese yen (JPY). We combine this data with two distinct macroprudential databases from the International Banking Research Network (IBRN) and the International Monetary Fund (Integrated Macroprudential Policy Database - iMaPP). Both databases contain country-specific measures of macroprudential policy actions. Having two distinct sources for macroprudential policies is critical to ensure robustness, because measuring macroprudential policies is still in its infancy.
We conduct our analysis as follows. In the first step, we focus on the period of the effective (zero) lower bound, starting with 2012 Q2 and ending in 2014 Q4, the eve of the year of US monetary policy liftoff (Lhuissier et al, 2019). Given the binding effective lower bound, we use shadow interest rates from Krippner (2016) to capture the stance of post-crisis unconventional policy. For this period, we examine the interaction using regulatory measures from both the IBRN and IMF iMaPP databases. In the second step, we extend our analysis up until 2016 Q4 to study the policy interaction during and after US monetary policy liftoff. For this extended analysis only the IMF iMaPP database is available.
We find consistent evidence that macroprudential measures enacted in source (lending) banking systems significantly interact with changes in the monetary policy associated with the currency of lending. Furthermore, the interaction term is positive. This means that tighter macroprudential policy mitigates the lending impact of monetary policy (irrespective whether monetary policy tightens or eases) - whereas easier macroprudential policy amplifies the lending impact of monetary policy. Referring back to our earlier example, macroprudential tightening in the UK mitigates the negative impact of US monetary tightening on USD-denominated cross-border bank lending outflows from the UK banking system (say, to Malaysia).
The macroprudential-monetary policy interaction that we identify is not only statistically but also economically significant. Given the nature of interactions quantifying the economic impact requires considering both policies simultaneously. As an example, following a 100 basis point monetary tightening over four quarters, cross-border lending outflows decline by around 10 percentage points more in a source banking system that relatively eases macroprudential tools (i.e. India in 2014 Q1) than from a source that relatively tightens such tools (i.e. Netherlands in 2014 Q1). A monetary tightening of 25 basis points over four quarters would still imply in such interaction a 2.5 percentage point decline in lending flows. This impact is substantial in magnitude, relative to the average quarterly growth in bilateral cross-border bank claims of 1.2 percent. Especially so, as this impact comes solely from the interaction, i.e. the effect that we observe in addition to the level effects of monetary and macroprudential policies.
Our findings are robust to a range of alternative specifications. We find significant results in both our short and long sample, and both using the IBRN and iMaPP databases. For completeness, we also examine the potential interactions with macroprudential tools applied in borrowers' countries. However, there we do not find consistently significant results.
The results suggest that the interaction between monetary policy of a currency issuer and the macroprudential policy of major lending baking system jurisdictions materially affects the supply of cross-border bank lending. First, this policy interaction matters for central banks in the countries of borrowers to assess credit supply. Relating back to our earlier example, emerging market central banks would benefit from understanding early how the interaction of US monetary policy and UK macroprudential policy affects cross-border USD loan supply to their economies. An early recognition could help to execute the appropriate domestic macroprudential policy response in time for monetary transmission to take effect. Second, these interactions also matter for regulators of major international banks, when they calibrate macroprudential policies. In our example, understanding the policy interaction allows UK regulators to mitigate unintended policy externalities, which result from reserve-currency monetary policy actions. For instance, UK macroprudential easing can weaken the lending impact of US monetary policy. Furthermore, understanding this interaction also matters for the central banks associated with the major international currencies. In our example, gauging policy interactions could aid US policymakers to more precisely assess potential spillbacks to the US. Paradoxically, to the degree such understanding becomes integrated to monetary and macroprudential policy setting over time, the policies themselves would ultimately become less exogenous.
Last, but not least, the recognition of such positive interaction is also important when thinking about domestic application of monetary and macroprudential policies. While our quantitative results do not necessarily translate to domestic lending, the qualitative results suggest that central banks might want to think about potential interactions between domestic monetary and macroprudential policies.
The paper proceeds as follows. In Section 2 we link our work to the related literature. In Section 3 we describe our data. We present the methodology in Section 4 and detail the results in Section 5. We discuss robustness in Section 6 and conclude with policy implications in Section 7.
Our research focuses on the interaction between macroprudential policies and monetary policies in an international bank lending setup. Hence, it builds on three strands of literature studying the drivers of international bank lending flows: (1) research on the impact of macroprudential policies (2) the relatively new research focusing specifically on the interaction between monetary and macroprudential policies, and (3) research on monetary policy spillovers.
First, the research on macroprudential policies dates back to Crockett (2000) and Borio (2003) and is recently reviewed in detail by Claessens (2015) and Galati and Moessner (2018). Elliott et al (2013) provide of historical overview of such policies in the United States. The policy discussion, as shown for instance in the IMF-FSB-BIS (2016) publication, suggests that macroprudential policies might have an international dimension. From the perspective of borrowers' countries, Houston et al (2012) shows that more strictly regulated jurisdictions receive less cross-border bank credit. Temesvary (2018) and Frame et al (2019) show that banks not only lend less to locations with stricter regulations, but they are also less likely to set up operations there. The body of research in the context of the IBRN's 2016 project (summarized in Buch and Goldberg (2017)) also shows a wide range of evidence on regulatory impact on cross-border bank lending flows. Takats and Temesvary (2019) provide evidence that macroprudential rules can stabilize cross-border lending flows during times of severe financial stress, such as the taper tantrum.
Second, the interaction of monetary and macroprudential policies became one critical focus for policymakers (Yellen, 2010; Claessens, 2013; Claessens and Valencia, 2013; Praet, 2018; Cecchetti et al, 2018) and therefore for economic research. Although earlier literature has addressed such interactions in the domestic context, to the best of our knowledge ours is the first paper to investigate such interaction in the global and cross-border bank lending context. Various models were proposed on how macroprudential policies could interact with monetary policy (Beau et al, 2011, 2012; De Paoli and Paustian, 2013; Brunnermeier and Sannikov, 2014; Smets, 2014; Darracq Paries et al, 2019, Coman and Lloyd, 2019). Broadly, macroprudential and monetary policies aim at different goals: financial stability and stable inflation (or business cycle), respectively. Following the Tinbergen principle the two tools may suffice to reach these two separate goals, but policymakers need to understand the interaction to fine tune the combined policy effects. Yet, the related empirical evidence remains scarce. Based on confidential credit registry data from Latin America Gambacorta and Murcia (2017) argue that macroprudential tools have a greater effect on credit growth when reinforced by the use of monetary policy moving in the same direction. Similarly, Bruno et al (2017) find evidence in the Asian context for the two policies reinforcing each other. Hills et al (2019) investigate this interaction through the external lending of UK banks.
Third, there is also a fast-growing literature showing evidence for international monetary policy spillovers through cross-border bank lending (Cetorelli and Goldberg, 2012; Miranda-Agrippino and Rey, 2012; Forbes and Warnock, 2012; Temesvary et al, 2018). Furthermore, there are several papers showing, in line with our identification approach, that the currency denomination of bank lending acts as a separate dimension for the international bank lending channel (Alper et al, 2016; Ongena et al, 2015; Avdjiev and Takats, 2018; Avdjiev et al, 2016; Takats and Temesvary, 2016). Furthermore, our work also builds on research which argues that national borders and economically relevant decision-making units often diverge (see, for instance, Takats and Temesvary (2016) for a review). The discussion dates back to Fender and McGuire (2010) and Cecchetti et al (2010), who argue that the lending bank's nationality tends to be more relevant than its residence in identifying the decision-making unit. Building on these findings, Avdjiev et al (2015) coin the term of the (absence of) triple coincidence in international finance. This term refers to the phenomenon that national borders, the conventional units of international economic analysis, often do not coincide with the economically relevant decision-making unit. Following these lessons, we focus on "lending banking systems" as opposed to "lending countries", so that we can follow the decision-making unit as precisely as possible.
Our data on country-level regulatory measures come from two sources: the macroprudential database employed by the 2016 IBRN project, also incorporating the 2013 Global Macro Prudential Instruments (GMPI) survey (Cerrutti et al, 2015; Correa et al, 2016; Avdjiev et al, 2017; Berrospide et al, 2017); and the IMF's Integrated Macroprudential Policy Database (iMap). The IBRN database extends on a quarterly frequency up until 2014 Q4, and the IMF iMaPP database is available up to 2016 Q4. The panels in Table A1 summarize and describe these indices.
In our investigation, we focus on strictly macroprudential tools. This distinction matters because both the IBRN and IMF iMaPP databases contain a mix of macroprudential and (micro)prudential measures. Most importantly, both databases contain information on minimum capital requirements. These capital requirements reflect more (micro)prudential considerations, In fact, they quite often reflect the adoption of the Basel III regulatory reform. Therefore, we exclude changes in minimum capital requirements, when we create an index of macroprudential tools.
Importantly, we focus on the overall impact of macroprudential rules, rather than formulating hypotheses around specific tools and their impact on cross-border bank lending in our main analysis.
Therefore, we construct macroprudential policy indices from both databases. In the construction, we follow similar steps as those taken in the IBRN database. The IBRN and IMF iMaPP regulatory databases describe quarterly changes in the stance of individual macroprudential tools, coded as 1 for tightening and -1 for easing. Our macroprudential index in each database is a country index by time t and country i, which equals 1 if the sum of changes in the individual policy tools listed in Table A1 is greater than or equal to 1, equals -1 if the sum of the instruments is less than or equal to -1, and is 0 otherwise.
The two macroprudential databases show a similar, but not identical picture. The correlation across the macroprudential indexes constructed from the two databases is near 0.7. This underlines the importance of investigating interactions using both measures.
1.2. Data on bilateral cross-border bank claims
Cross-border bank claims total around US$ 30 trillion globally. These claims include cross-border bank lending and other claims (such as securities holding). Bank for International Settlement's International Banking Statistics (BIS IBS) provides detailed data about these cross-border claims along several dimensions.
In order to study the interaction between home macroprudential tools and the monetary policy of the currency issuer we need to identify three dimensions of the cross-border bank claim data: (A) the currency composition of cross-border claims; (B) the residence of the borrower and (C) the nationality of the lending banking system. The currency composition (A) is necessary to study the currency-specific monetary policy. The borrowers' residence (B) is necessary to control for credit demand of the borrowers' countries. The nationality of the lending banking system (C) is necessary to identify the home macroprudential agency whose policy we aim to follow. In our leading example these three dimensions enable to investigate how USD-denominated cross-border bank claims from UK-headquartered banks to Malaysia are affected by the interaction of (A) US monetary policy and (B) UK macroprudential policy while controlling for credit demand in Malaysia (C).
In our analysis we use the Stage 1 enhancements of the BIS IBS, because this dataset uniquely allows us to use all three necessary dimensions of the underlying cross-border bank claims data (Table A2). Two main BIS IBS datasets cover cross-border claims: the consolidated and the locational data. The first, consolidated dataset groups claims according to the nationality of banks. It covers residence of borrower (B) and the nationality of the lending banking system (C), but not the currency composition (A). In our case, the consolidated dataset would not allow us to use the currency-specific monetary policy, i.e. to identify the currency dimension of the international bank lending channel.
The second dataset, the locational banking statistics defines creditors and debtors according to their residence, consistently with national accounts and balance of payments principles. It has three main subsets: the residence-based, the nationality-based and the Enhanced Stage 1 data. The residence based data has information on the currency composition (A) and the residence of borrower (B), but not on the nationality of the lending banking system (C). This can be an issue with financial centers. For instance, a UK bank's lending through its Hong Kong subsidiary to Malaysia, would be identified as two separate lending in residence based approach: one loan from the UK to Hong Kong and another one from Hong Kong to Malaysia. In our case, that would mask the impact of the home (i.e. the UK) macroprudential regulator's impact on lending to Malaysia. In contrast, the nationality- based data observes nationality of the lending bank (C) along with the currency denomination (A) - but not the residence of the borrower (B). In our case, not having access to the residence of borrower would preclude controlling for credit demand. Finally, the Enhanced Stage 1 dataset provides all three (A, B and C) dimensions. Therefore, it is the most suitable dataset to study our question.
The Stage 1 Enhancement to the BIS IBS is available by quarterly frequency starting from 2012 Q2 onward both in stocks (levels) and in currency adjusted flows. The stocks and flows are also available by currency denomination, across the major international currencies. We focus on the three main currencies (USD, EUR and JPY) that are the most prevalent in cross-border lending. More precisely, we use quarterly changes in the natural logarithm of bilateral cross-border bank claim stocks denominated in these three currencies. When analyzing the Stage 1 enhanced dataset we use a large cross section that covers 27 lending banking systems and 50 borrowers' countries.
The Stage 1 enhanced IBS data is fairly representative, though not yet fully complete. On aggregate, information on the nationality of lending banks is available for more than 90% of global cross-border claims (Avdjiev and Takats, 2018). However, this ratio varies and tends to be higher for larger counterparty countries.
Since smaller-scale lending flows can be very volatile, we winsorize the observations at the 5th and 95th percentile as is common in related work (Avdjiev and Takats, 2014; Takats and Temesvary, 2016; Avdjiev and Takats, 2018; Takats and Temesvary, 2019).
1.3. Data on monetary policy stance
Our benchmark sample focuses on the period of the binding effective zero lower bound, preceding the liftoff of US monetary policy from the zero lower bound (2012 Q2 - 2014 Q4). In this period, the major central banks, the Federal Reserve, the European Central Bank and the Bank of Japan relied on "unconventional" expansionary monetary policies. As a result, the short-term policy target interest rates set by these three central banks hit the effective lower bound in early 2009 (Figure 1, left panel). Therefore, we use the currency-specific short-term shadow interest rates (as described in Krippner (2013, 2015 and 2016)) to measure the change in monetary policy stance of the three major reserve currencies (Figure 1, right panel). By construction, the short-term shadow interest rates are not subject to the zero lower bound, and are therefore able to capture expansionary monetary policy actions by dipping into the negative range.
Our larger sample extends through end-2016, including the post-liftoff period of conventional monetary policy actions. However, for consistency and comparability, we continue to use the Krippner shadow rates also in this extended sample. This is appropriate, as by construction the Krippner shadow short-term rates are identical with policy interest rates during conventional monetary policy periods. We define the change in the monetary policy stance as the quarterly change (from t-1 to t, in percentage points) in the short-term shadow interest rate that corresponds to the monetary conditions determined by the central bank that issues currency c.
1.4. Additional macro controls
Whenever we do not rely on country*time fixed effects, we control for macroeconomic and financial effects on credit demand in borrowers' countries and credit supply in source bank lending systems. To do so, we add (real) GDP growth and changes in domestic interest rates as controls in specifications where country*time fixed effects are not included. We also add quarterly changes in the exchange rate between the currencies of the source (home) and the borrowers' country, to capture any additional valuation effects which may influence banks' cross-border lending flows. We describe our model variables in detail in Table 1.
We analyze how interactions between macroprudential tools and monetary policy affect bilateral quarterly cross-border lending flows in major currencies.
The main identification issue we face is that the use of macroprudential tools can be endogenous to the use of monetary policy. In a domestic context, policy makers might observe overheating credit markets and react with either macroprudential or monetary tightening - or a combination of the two. In short, the use of the two policies are typically endogenous in a domestic context. Consequently, when we investigate interactions with source macroprudential tools, we need to focus on the effects of a monetary policy that is not linked to the source bank lending system. Similarly, when we extend the analysis to policy interactions with borrowers' country macroprudential tools, then we need to examine a monetary policy that is unrelated to borrowers' country regulatory policies.
We achieve identification by focusing on the currency dimension of the bank lending channel. We use the result that the monetary policy of a currency issuer affects bank lending denominated in that currency, irrespective of the source lending system or the borrower country. For instance, US monetary policy affects cross-border bank lending denominated in USD even from UK banks to Malaysia - although neither the UK nor Malaysia uses the dollar as its own currency. This channel of monetary policy transmission is typically exogenous to source (an also to borrowers') countries. Of course to achieve clear identification, we need to exclude the US both as a source lending banking system and as a country of borrowers, when we investigate USD-denominated lending. Similarly, we exclude euro-area countries and Japan when we analyze EUR and JPY-denominated lending flows, respectively.
1.2. Panel regression setup
Our dependent variable, Aclaims is the quarterly change in the log of bilateral claims between the source lending banking system i and borrowers' country j, denominated in currency c. Our two main explanatory variables are (1) our IBRN and IMF iMaPP indices of applied macroprudential measures (macroprudential) in source bank lending system i as defined in Section 3.1 above, and (2) the change in monetary policy stance (monetary) associated with the major international currencies (USD, EUR, JPY) as measured by the Krippner (2016) shadow rates. Following the standards of the bank lending literature (Kashyap and Stein, 2000; Cetorelli and Goldberg, 2012) in accounting for potential persistence in lending flows, we consistently add the lagged dependent variable to the right-hand side.
To strengthen identification, we restrict all our estimations to exclude both same country lending and own currency lending (in the terminology of Takats and Temesvary (2016)). These two sets of lender-borrower pairs could potentially confuse identification. First, same country lending (e.g. US-owned bank subsidiaries lending back to US-based borrowers) suffer from a more severe endogeneity of monetary and macroprudential policies. Second, as discussed in Section 4.1 on identification, own currency lending (e.g. German bank lending in EUR or US banks' lending in USD) might confound the country and currency-specific impact of monetary policy.
We use six equations throughout the paper. The first regression explains lending flows as a function of macroprudential policies in source bank lending system i (Amacroprudentialit). In addition, we control for macroeconomic variables both in source bank lending system i (Amacroit) and borrowers' country j (Amacrojt). Furthermore we apply fixed effects for each source bank lending system (FEi), borrowers' country (FEj) and currency (FEC) to capture any time-invariant level differences. Finally, we apply time fixed effects for each quarter (FEt) to control for unobserved global factors. Taken all the above together, Equation (1) is formally written as:
In the third regression, we add our main interest: the interaction between macroprudential and monetary policy (Amacroprudentialit * Amonetaryct):
While Equation (3) addresses the policy interaction, a potential identification question remains. Namely, the question is the extent to which the macro controls capture non-policy related changes in credit demand from the borrowers' countries and credit supply from the source bank lending systems. Less than fully controlling for such confounding factors might result in omitted variable bias, which may, in turn, affect our interaction estimates.
To address this potential omitted variable bias, we expand the logic outlined in Khwaja and Mian (2008) to a broader context by adding (1) country*time fixed effects for borrower's country i and (2) currency*time fixed effects for currency c. The borrowers' country-specific fixed effects allow us to control for any potential direct time-varying country-level credit demand shocks in the borrowers' country. Similarly, the currency specific currency*time fixed effect controls for any shocks related to the use of that currency. Consequently, we drop the stand-alone macroprudential and macro terms for borrowers' country j (Amacroprudentialit and Amacroit) and the monetary policy by currency issuer c (Amonetaryct) that would be subsumed by our extensive fixed effects. The resulting Equation (4) is written as:
Next, we add further fixed effects to address potential omitted variables on the credit supply side from source bank lending systems. In other words, we add country*time fixed effects for source bank lending system i so as to focus attention on the interaction of macroprudential and monetary policy. This fixed effect allows us to control for any potential direct time-varying source banking system-specific credit supply shocks. We drop the stand-alone terms for lending system i both for macroprudential policy (Amacroprudentialit) and macro controls (Amacrojt-k) that would now be subsumed. The resulting Equation (5) is written as:
Finally, we address the potential concern that some unobserved structural drivers embedded in the global cross-border bank lending system drive our result. Technically, we introduce a fixed effect for each lending-borrowing pair to assume such structural impact (FEi*j). For instance, in our earlier example the UK-Malaysia link would receive a fixed effect. Given that our identification relies much more on cross-sectional than on time-series variation, this constitutes a demanding specification. In order to avoid overloading the regression with fixed effects, we drop the country*time fixed effects for lending banking systems and borrowers' countries here and reintroduce the macroeconomic controls (Amacroit and Amacrojt). The resulting final Equation (6) is written as:
Importantly, while the extensive use of time-country and time-currency specific fixed effects identifies the policy interaction precisely, it also precludes us from being able to observe the impact of source (home) and borrowers' country policy measures one by one. While Equation (1-3) and partly Equation (4) provide some estimates for such level effects, these results should be treated cautiously due to the identification challenge that the less saturated specifications mentioned above face.
In all estimations we apply two-way clustering of the standard errors across the source (lending) banking system and borrowers' country dimensions.
Our estimates show consistent evidence that the monetary policy of major currency issuers and the macroprudential policies in source bank lending systems interact in a statistically and economically significant way. In our analysis, we start from relatively simple models and gradually develop more sophisticated estimates as we move from Equation (1) to (6) outlined in Section 4.2. We estimate our benchmark set of specifications first over the unconventional monetary policy period of 2012 Q2 - 2014 Q4 using both the IBRN (Table 2) and IMF iMaPP (Table 3) regulatory databases. We then extend our sample through end-2016, using the IMF iMaPP regulatory data (Table 4). We discuss economic significance and interpretation in separate subsections.
1.1. IBRN data (2012-2014)
First, we investigate the policy interaction with the help of the IBRN macroprudential database for the 2012 Q2 - 2014 Q4 period (Table 2). Our first model estimates Equation (1), where only source bank lending system macroprudential policy is included - with currency-specific monetary policy and its interaction omitted (Model 1). We see that the coefficient on macroprudential tightening has a positive and significant coefficient. This is consistent with the assertion that tighter macroprudential regulation increases the costs of lending at the home jurisdiction and thereby makes lending abroad, everything else being constant, more attractive.
We then estimate Equation (2), which also includes the impact of the cumulative shadow interest rates (Model 2). We find a negative, albeit insignificant coefficient for the level impact. This is consistent with the observation that tighter monetary policy of a currency issuer implies lower crossborder bank lending in that currency. Importantly, the estimates on the macroprudential coefficient remains significant and of similar size as in Model 1.
Next, we turn to estimate our main interest by adding the interaction term between monetary and macroprudential policies to our regressions. Formally, we estimate Equation (3). The results show that the interaction is positive and statistically significant (Model 3). That is, macroprudential tightening in a source bank lending system significantly mitigates the negative impact of a monetary tightening of the currency issuer on cross-border bank lending. (We discuss the interpretation of this interaction in more detail in Section 5.5.)
As we discussed in the model setup, omitted variable bias might affect the results of Model (3). That is, there might be some uncontrolled demand or supply factors that could affect our interaction coefficient estimate. We address these concerns by applying a generalization of the Khwaja and Mian (2008)-style identification to address potential non-interaction related demand effects from borrowers' countries (Equation 4). The interaction results stemming from this estimation remain significant and materially unchanged from our earlier estimates (Model 4).
We then extend the Kwaja and Mian (2008)-type of identification from the demand side to the supply side, i.e. to the source bank lending systems. Formally, we estimate Equation (5). The results show that the interaction term remains highly significant and positive (Model 5).
Finally, as a robustness check, we add source*borrower fixed effects to our estimation to estimate Equation (6). That is, we add a time-invariant fixed effect for each pair of source bank lending system i and borrowers' country j in our specification. Though this is a very demanding control, the interaction coefficient estimate remains consistently significant (Model 6). Furthermore, its sign and size also remains in line with our earlier models.
1.2. IMF iMaPP data (2012-2014)
In the next step, we use the IMF iMaPP data for the 2012 Q2 - 2014 Q4 period (Table 3). This setup allows us to broadly compare the IMF iMaPP estimates to the IBRN estimates. We run regressions from Equation (1) to Equation (6) exactly as for the IBRN dataset. A similar picture emerges as before: the interaction term is significant with a positive sign for Models 3, 4 and 6. However, the interaction coefficient estimate becomes insignificant for Model 5. In evaluating the Model 5 results, it is important to emphasize that this specification, which includes the most complete fixed effects on both the source and borrower sides, is extremely demanding of the data.
1.3. IMF iMaPP data (2012-2016)
In the next step, we use the IMF iMaPP data for the 2012 Q2 - 2016 Q4 period (Table 4). This utilizes the most recent regulatory data available. We run regressions from Equation (1) to Equation (6) exactly as before. The results are very close to the earlier findings, in particular to the short sample iMaPP results: the interaction term is significant with a positive sign for Models 3, 4 and 6, while the coefficient estimate remains insignificant in Model 5. Therefore, the results suggest that the statistically significant policy interaction was not only a feature of unconventional monetary policy regimes. Rather, these interaction effects also generalize to the post-US monetary policy liftoff period.
1.4. Economic significance
The coefficient estimates on the interaction terms do not allow for straightforward translation to economic significance. The reason is that both macroprudential and monetary policy stances matter for characterizing the interaction effects. In addition, while we have an intuitive understanding of how significant a given monetary tightening is, it is less clear how to assess the size of change in macroprudential policies. Hence, we use percentile ranks to characterize the magnitude of the effects of macroprudential measures. We compare the interaction effect for a 100 basis points tightening in the shadow interest rates over the course of four quarters, evaluated in a source banking system with a substantial strengthening of regulatory policies (at the 99th percentile of macroprudential policy tightening) vs one with easing macroprudential rules (at the 1st percentile).
The results show that the macroprudential-monetary interaction effects are economically significant (see bottom of Tables 2-4). For instance, our main model estimates show that tighter macroprudential policies in source bank lending systems (comparing the 1st and 99th percentile of macroprudential tightening) mitigate the decline induced by a 100 basis point four-quarter cumulated monetary tightening by around 20 percentage points (Models 3-6 in Table 2). These figures imply an around 5 percentage point mitigating effect for a more moderate, 25 basis point tightening. These estimates are even larger, at around 30 percentage points, when we use the IMF iMaPP data (Tables 3 and 4).
The interaction is also economically significant when we consider somewhat smaller percentile differences across macroprudential policies. For instance, our main model estimates show that tighter macroprudential policies in source bank lending systems (i.e. comparing the 5th and 95th percentile of macroprudential tightening) mitigate the lending decline induced by a 100 basis point four-quarter cumulated monetary tightening by around 10 percentage points (based on Table 2 models). This, for example, is the comparison between India (as the 5th percentile) and the Netherlands (as the 95th percentile) in 2014 Q1. In sum, the estimated interactions are not only statistically, but also economically significant.
1.5. Interpretation of policy interaction effects across policy actions
In this subsection, we detail the interpretation of the interaction of monetary and macroprudential policies. Doing so is instructive because there is no established literature or language on how to think about such interaction. We interpret the interaction effects for cases: the combination of monetary easing and tightening along with macroprudential easing and tightening, as Figure 2 illustrates.
Let's consider first monetary easing (left-hand column in Figure 2). Monetary easing by the currency issuer, in itself, increases cross-border bank lending denominated in that currency. Now, consider the case in which monetary easing coincides with macroprudential easing (Quadrant I in Figure 2). In itself, macroprudential easing tends to reduce cross-border bank lending (as discussed in the Table 2 analysis). Insofar as regulatory easing makes lending in the source country of the international bank less expensive, this may be evidence that banks would substitute cross-border bank lending for lending at home. However, the interaction of these two policies is positive. That is, when macroprudential and monetary easing are combined, their interaction increases cross-border bank lending compared to the two standalone effects. Macroprudential easing, therefore, amplifies the impact of monetary easing.
Next, consider the case when monetary easing is combined with macroprudential tightening (Quadrant II). In this case, both policies have a positive standalone effects on cross-border bank lending outflows. Yet, their interaction, that is the positive interaction of a positive and negative variable, reduces lending outflows compared to the standalone effects. Macroprudential tightening, therefore, mitigates the impact of monetary easing.
When we move to examine the interaction effects under monetary policy tightening we see symmetric impacts (right-hand column in Figure 2). Monetary tightening, in itself reduces cross-border bank lending. Macroprudential easing, in itself, also tends to reduce cross-border bank lending. Furthermore, their positive interaction further decreases cross-border bank lending compared to the two standalone effects (Quadrant III). In other words, macroprudential easing amplifies the negative impact of monetary tightening.
Finally, consider the case when monetary tightening is combined with macroprudential tightening (Quadrant IV on Figure 2). In this case, the policies work in the opposite direction: monetary tightening, in itself, reduces cross-border bank lending while macroprudential tightening, again in itself, increases it. Their positive interaction, however, increases cross-border bank lending compared to the two standalone effects In other words, macroprudential tightening mitigates the negative impact of monetary tightening.
In tying the above discussion together, a clear picture emerges: tighter macroprudential policy mitigates the lending impact of monetary policy - whereas easier macroprudential policy amplifies the cross-border lending impact of monetary policy.
For completeness, we also examine the role of macroprudential tools applied in borrowers' countries. For instance, if the currency issuer tightens monetary policy, policymakers in borrowers' countries might want to limit the subsequent contraction in cross-border lending inflows by loosening macroprudential policies in their economies. In Table 5, Columns 1-2, 3-4 and 5-6 repeat the Model 34 specifications from Table 2, 3 and 4, respectively. We do not find consistently significant evidence of interactions between borrowers' country macroprudential policies and the monetary policy of the currency of borrowing.
This suggests that the monetary-borrower macroprudential interaction is insignificant, or at least much weaker than the monetary-source macroprudential interaction. This is in part because the lending impact of borrowers' country macroprudential actions may depend on the type of action implemented: A tightening of macroprudential tools on banks' clients (that is, on the credit demand side) in borrowers' countries can further reduce credit inflows. This would amplify the contractionary lending impact of monetary tightening of the currency issuer. Conversely, a tightening of macroprudential tools on resident lenders (on the credit supply side) in borrowers' countries can lead borrowers to substitute cross-border for domestic credit. This would mitigate the impact of monetary tightening on inflows. Therefore, at this stage, we would not interpret our results in such a way as to exclude the possibility of policy interaction on the borrowers' country side.
2. Source loan-to value ratio caps
In the next step, we focus on a single macroprudential tool: limits on Loan-to-Value (LTV) ratios. While our initial hypothesis does not concern single tools (and rather focuses on the joint effect of macroprudential tools), the LTV is special for both economic and technical reasons. Economically, the LTV is often perceived to be very effective at constraining demand as it does not have to work through price signals (IMF-FSB-BIS, 2016). Furthermore, Alam et al (2019) show emerging evidence that LTV ratio has a significant lending impact. Technically, the LTV is also directly comparable across the IBRN and IMF iMaPP databases. Insofar as tightening in source LTV limits (a credit demand-side measure) reduces borrowers' credit demand domestically, such tightening would push source banks' lending "outward" into cross-border lending outflows. Hence, such tightening would mitigate the cross-border lending contraction resulting from monetary tightening by the currency issuer.
In analyzing the LTV ratios, we replicate Equations (3) and (4) for both the short and long sample, and for both the IBRN and IMF iMaPP databases. That is, we replicate columns 3 and 4 of Tables 2, 3 and 4 for the LTV ratio (see Columns 1-2, 3-4 and 5-6 of Table 6, respectively). Consistent with our benchmark results, we find significantly positive interactions throughout. That is, tightening source LTV limits significantly mitigates the cross-border lending-reducing impact of a tighter monetary policy.
3. Source FX reserve requirements
Given the significant results on the LTV ratio caps that operate on the credit demand side, we apply our analytic setup on a credit supply-side tool: reserve requirements on banks' FX funds. There is some recent evidence that macroprudential FX regulations impact cross-border lending flows (when enacted on banks in borrowers' countries; Ahnert et al, 2019), and, technically, this tool is also directly comparable across the IBRN and IMF iMaPP databases.
In analyzing the FX reserve requirements, we follow similar steps as in the case of the LTV limits. We again replicate Equations (3) and (4) for both the short and long sample, and for both the IBRN and IMF iMaPP databases. That is, we replicate columns 3 and 4 of Table 2, 3 and 4 focusing on FX reserve requirements as the macroprudential tool of interest (see Columns 1-2, 3-4 and 5-6 of Table A3, respectively). Our results show no significant interaction between monetary policy and source lending system FX reserve requirements in driving cross-border lending flows.
Importantly, the results do not imply that source FX reserve requirements would not work as macroprudential tools. Rather, they merely suggest that FX reserves requirements do not interact with the monetary policy of the currency issuer in affecting cross border bank lending. Yet, the results, in particular when we combine them with those on LTV ratios, might suggest that not all macroprudential policies imposed on source banking systems are equally effective in mitigating monetary policy effects on cross-border lending flows.
4. Level of initial macroprudential stringency
As described above, both the IBRN and IMF iMaPP macroprudential databases provide information on changes in regulatory stringency over time, but not on the level of the policy stance. While focusing on changes in regulatory stringency, as we do, is consistent with the approach taken in the vast literature on the lending impact of policies, a concern remains on potential non-linearity. Thus, the level might be relevant in conjunction with the change for macroprudential policies.
To address this feature, we use the historic macroprudential changes to create a proxy for the level of macroprudential stance by country. We define a new level variable (Level of Initial Macroprudential Stringency) as the cumulative sum of regulatory changes in each source banking system from 2000 Q1 to 2012 Q1. We define this Level of Initial Macroprudential Stringency both for the IBRN and the IMF iMaPP databases. Naturally, this variable should only be seen as a proxy for the unobserved macroprudential stance and be interpreted cautiously.
To examine the impact of this Level of Initial Macroprudential Stringency variable, we horserace its interaction impact with our standard interaction measure (Table A4). More formally, we interact this Level of Initial Macroprudential Stringency with our standard change in Source Regulatory Stringency measure and horserace this interaction with the monetary - macroprudential interaction that has been our main focus. The results confirm that the significance of our monetary - macroprudential change interaction results remains generally robust to controlling for cross-sectional differences across countries in the level of macroprudential stringency.
5. Common IBRN - IMF iMaPP Sample
We address a potential concern about the implication that the differing cross-section coverage of the IBRN and IMF iMaPP databases may have for our results. While in Section 5.2 we have already estimated our interaction results on the same time series for the two databases, we have not yet addressed the potential impact of cross-section heterogeneity across the two databases.
We re-estimate Equations (3), (4) and (6) from Tables 2 and Table 3, restricting the estimation sample to a common set of observations for each model (Table A5). The significance of the policy interaction term remains highly consistent with our main findings.
6. Interaction term implied model restriction
The standard estimation technique implies that all four possible interactions have the same sign and size. However, potentially the four possible interactions (as described in Figure 2 and Subsection 5.5) may differ in size, or at least in size. To control for such implicit model restriction, we separately estimate all four coefficients, i.e. we estimate an interaction coefficient for each quadrant of Figure 2, and test their statistical equivalence. The standard Wald tests cannot refuse the null hypothesis that the interaction coefficient estimates across all four cases are equal, or even that they are pairwise equal. This provides further evidence that our interaction model is well specified.
7. Endogeneity of macroprudential policies to monetary policy
The main advantage of our identification strategy is that macroprudential policies enacted in source lending systems are almost fully exogenous to the monetary policy of the issuers of the three reserve currencies - thereby avoiding the endogeneity pitfall of studying policy interaction effects in a domestic setting. We further ensure the clarity of our identification strategy by excluding "same country lending" and "own currency lending" from our specifications. However, a potential concern that may remain is the extent to which the macroprudential policies of a reserve currency issuer may be endogenous to the monetary policies of other reserve currency issuers.
To address this issue, we exclude completely the US, the euro area and Japan, all three home regions of the issuers of the reserve currencies, from our analysis. Given the limits imposed by the resultant substantial reduction in the cross-section of our dataset, we focus on the long IMF iMaPP (2012-2016) series (i.e. Table 4) in this exercise. Our findings remain robust to this exclusion throughout. In the interest of space, we do not show this table, but make the results available by request.
8. Foreign currency (FX)-based macroprudential tools
An additional noteworthy delineation is the extent to which macroprudential tools applied to banks' domestic vs. cross-border lending may operate differently. An intuitive way to address such potential differences is to examine macroprudential tools on FX lending separately - as cross-border lending flows are overwhelmingly denominated in non-domestic currencies in non-reserve currency issuing source lending systems. The IMF iMaPP database provides additional information on macroprudential tools imposed on FX lending flows - which tend to be credit supply-side measures.
We re-estimate our benchmark specifications using the long IMF iMaPP (2012-2016) dataset, i.e. Table 4, using a newly created macroprudential index encompassing only FX-related macroprudential tools. Our benchmark results remain robust to the use of this new FX lending-focused macroprudential index throughout. In the interest of space, we do not include this table, but make the results available by request.
9. Additional Robustness Checks
Our benchmark results are also robust to (i) using the IBRN pre-defined macroprudential index construction (which includes minimum capital requirements), (ii) excluding the euro area or emerging market borrowers, and (iii) excluding interoffice claims (claims between parent banks and their subsidiaries) or all interbank lending. In the interest of space, we do not include these tables, but make the results available by request.
In this paper, we use a novel identification strategy on a unique dataset to examine the interaction between monetary policy and macroprudential policy in cross-border bank lending. We combine the new BIS Stage 1 enhanced banking statistics on bilateral cross-border lending flows with the IBRN's macroprudential database and the IMF's iMaPP database. We find statistically significant evidence for a positive interaction. This means that tighter macroprudential policy mitigates the lending impact of monetary policy - whereas easier macroprudential policy amplifies the impact of monetary policy. The results are robust to numerous alternative specifications, and are also significant economically.
The policy interaction results are economically important from three distinct perspectives. First, they are relevant in those countries where cross-border bank lending plays a major role in credit supply. Central bankers can study the policy interaction effects to understand what the changes in monetary policy of major currency issuers and macroprudential changes by the regulators of major lending banking systems imply for their economy. For instance, referring back to our example of UK bank lending to Malaysia in USD, interaction effects between US monetary policy (affecting the USD) and the macroprudential policy of the UK affect USD-denominated lending inflows, and are thus economically important for borrowers in Malaysia. Second, the results are important for regulators of major international banks to assess the impact of their macroprudential regulation on lending outflows. For instance, UK policymakers might want to consider the regulatory policy interaction with US monetary policy, and its impact on USD-denominated lending outflows, when formulating macroprudential tools in the UK. Third, the results are also economically significant from the perspective of major currency issuer countries, as our findings allow a more precise gauging of potential spillback effects into these economies and externalities for emerging markets.
The results are also economically significant indirectly. The international identification highlights meaningful interactions between monetary and macroprudential policies on cross-border bank lending. This suggests that there may be a meaningful interaction between these policies in the domestic setting as well - a strand of research which we hope our results will motivate. Finally, we also hope that our research provides a stepping stone for future economic research to better understand the interaction of regulatory and monetary policies in various contexts.
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