A. Cross-border banking flows

We use data on cross-border claims from the BIS’ LBS to test the international bank lending channel of monetary policy in systemically important countries. This dataset provides a geographical breakdown of reporting banks’ counterparties and the information about the currency composition of their balance sheets. The LBS dataset captures outstanding claims and liabilities of internationally active banks located in reporting countries against their counterparties residing in more than 200 countries. The data is compiled following the residency principle that is consistent with the BoP (Balance of Payments) statistics.

As the conventional bank lending channel relates to conditions in the location where banks make funding and lending activities, the residency principle has a conceptual appeal over the nationality principle used in Consolidated Banking Statistics (CBS). Moreover, to the extent to which foreign affiliates are subject to host-country regulation or have access to local bank liquidity facilities (Avdjiev et al., 2018), the residency principle is more appropriate than the nationality principle in identifying the international bank lending channel of monetary policy.

The major advantage of the BIS LBS data, compared to the banking flows collected from the BoP statistics, is the detailed breakdown of the reported series by recipient countries.⁷ Banks record their positions on an unconsolidated basis, including intragroup positions between offices of the same banking group. Currently, banking offices located in 46 countries, including many offshore financial centers, report the LBS. The LBS dataset captures around 95 percent of all cross-border interbank business (Bank for International Settlement, 2017). The bulk of cross-border bank claims and liabilities takes a form of loans and securities of the domestic banking sector vis-à-vis all counterparty sectors (including banks and non-banks, and the private and public sector).

Another advantage of the BIS LBS dataset is that the currency composition of cross-border claims and liabilities is available so that cross-border banking flows expressed in U.S. dollars are adjusted for movements in exchange rates. The adjustment for exchange rate movements turns out to be crucial in our setup since fluctuations in the exchange rate, which are influenced by monetary policy shocks, also affect cross-border bank lending. The availability of a currency breakdown enables the BIS to calculate break- and exchange rate- adjusted changes in amounts outstanding. Such adjusted changes approximate underlying flows during each quarter.⁸

The adjusted change is calculated by first converting U.S. dollar-equivalent amounts outstanding into their original currency using the end-of-period exchange rates, then calculating the difference in amounts outstanding in the original currency, and finally converting the difference into a U.S. dollar-equivalent change using the average period exchange rates.⁹ As the BIS LBS only report the exchange rate-adjusted flows, we construct the exchange rate-adjusted stock of the cross-border claims from country i to country j as the cumulated sum of the exchange rate-adjusted flows, where the initial value of the exchange rate-adjusted stock is set equal to the exchange rate-unadjusted claims— directly available from the BIS LBS.¹⁰

The time-series coverage of LBS database varies significantly across countries. Some advanced economies such as the U.S. have reported these statistics since 1977, while some emerging market economies started reporting the statistics only after the 2000s. We first analyze the international bank lending channel of U.S. monetary policy using data from 1990Q1 to 2012Q4 due to its extensive availability of cross-border banking flow data and well-identified exogenous monetary policy shock as well as its dominance in shaping international capital flows and the special role of U.S. dollars in the international financial system.¹¹

Throughout the analysis, we drop offshore financial centers using the IMF classification from our sample because their behaviors might differ substantially from the rest of the sample. Following Correa et al. (2017) and Choi and Furceri (2019), we further drop observations with the size of cross-border positions less than $5 million, or with negative total outstanding claims. Observations of the dependent variable in the upper and lower one percentile of the distribution are excluded from the sample to reduce the influence of outliers.¹²

Table A.1 in the appendix lists the final sample of countries used in the analysis, together with their income status, an indicator whether they belong to the euro area, and their average status regarding the exchange rate regime, monetary policy independence, and capital account openness during the sample period using the trilemma index constructed by Aizenman et al. (2013).¹³ The details on this index will be discussed in the online appendix B. We use these country-specific characteristics to investigate factors affecting the international bank lending channel of monetary policy.

To provide a first look at the pattern of cross-border bank lending, we present the size of total cross-border claims and liabilities as a share of the GDP in 2010Q4 for the 9 reporting countries in Table A.2 in the appendix. When normalized to the size of domestic GDP, the predominant role of European countries in the cross-border banking system is apparent. Cross-border claims of global banks in advanced economies are, on average, larger than liabilities, suggesting that they are net lenders in this market.

We further illustrate the bilateral structure of the data by presenting examples of bilateral cross-border claims between the U.S. and six countries in Figure A.2 in the appendix. Given the confidentiality of the data, we do not reveal the identity of the individual counterparties, but the first four recipient countries are advanced economies and the last two recipient countries are emerging market economies. Some observations stand out from the figure. First, the different scales of the y-axis in these graphs re-emphasize the dominance of advanced economies in accounting for these flows. Second, the pattern of cross-border lending is quite heterogeneous across countries.

B. Identification of monetary policy shocks

As discussed earlier, the proper identification of the causal effect of monetary policy actions on cross-border bank lending requires using monetary policy actions that are orthogonal to current and expected future macroeconomic conditions (Ramey, 2016). This is the main novelty of the paper compared to many existing studies on the spillover effects of monetary policy through cross-border banking flows.

Measures of exogenous U.S. monetary policy shocks. In the baseline analysis, we use the exogenous monetary policy shock series constructed by Coibion (2012) who extends the monetary policy shocks identified by Romer and Romer (2004) using a narrative approach. Romer and Romer (2004) extract measures of the change in the Fed’s target interest rate at each meeting of the Federal Open Market Committee (FOMC) and regress these policy changes on the Fed’s real-time forecasts of relevant macroeconomic variables in the Greenbooks prior to each FOMC meeting. Then, they identify exogenous monetary policy shocks as the residuals from this regression. Specifically, they estimate the following equation:

where m denotes the FOMC meeting, ffb_m is the target federal funds rate going into the FOMC meeting, F_mΔy_mX is the Greenbook forecast from meeting m of real output growth in quarters around meeting m, F_mπ_m,i is the Greenbook forecast of GDP deflator inflation, and F_mun₀ is the Greenbook forecast of the current quarter’s average unemployment rate.

The estimated residuals $${\widehat{ϵ}}_m$$ are then defined as exogenous monetary policy shocks, purged of anticipatory effects related to economic conditions. Our quarterly measure of monetary policy shocks comes from summing the orthogonalized innovations to the Federal funds rate from each meeting within a quarter. As a robustness check, we also use the identification strategy of Gertler and Karadi (2015) based on high-frequency data used as external instruments (see next section).

Measures of exogenous monetary policy shocks in other advanced economies. We follow the methodology of Furceri et al. (2018) to construct quarterly measures of exogenous monetary policy shocks for other eight advanced economies. They identify monetary policy shocks in two steps, which closely follow the work by Auerbach and Gorodnichenko (2013) in identifying fiscal shocks in advanced economies. First, they compute the unexpected changes in policy rates (proxied by short-term rates) using the forecast errors of the policy rates provided by Consensus Economics.¹⁴ Second, they regress for each country the forecast errors of the policy rates on similarly-computed forecast errors of inflation and output growth and identify the shocks as the residuals of this regression. We follow this approach, but we further purify these surprises of any predictable components by projecting it on current and lagged GDP growth and inflation, in order to eliminate any remaining endogeneity issue. Specifically, we estimate

where $F{E}_{i,t}^X(X=r, \Delta y,\pi )$ is the unexpected changes in policy rates (proxied by short-term rates), real GDP growth, and the inflation rate, respectively—defined as the difference between the actual value at the end of the quarter and the value expected by analysts as of the beginning of the last quarter for each country i. Δy_i,t and π_i,t are corresponding actual real GDP growth and the inflation rate. The estimated residuals are then defined as exogenous monetary policy shocks for advanced economies other than the U.S.¹⁵

As discussed by Furceri et al. (2018), this methodology has two main advantages. First, it overcomes the issue of “policy foresight” (Leeper et al., 2013) where economic agents receive news about possible changes in monetary policy and alter their behavior before the actual changes in policy happen. Changes in actual policy rate cannot capture this policy foresight of economic agents, leading to inconsistent estimates of the effect of monetary policy shocks on cross-border bank lending. Our approach, on the contrary, is free from this issue since it uses forecast errors which already reflect policy foresight by its construction. Second, this methodology reduces endogeneity issues as the shocks are orthogonal to unexpected changes in economic activity as well as to current and lagged endogenous variables.

Figure A.3 plots the distribution of the exogenous monetary policy shocks we constructed at a quarterly frequency. Table A.3 in the appendix shows the standard deviation of the country-specific exogenous monetary policy shock series, together with its correlation with the U.S. monetary policy shocks constructed by Furceri et al. (2018) and Coibion (2012), respectively. The correlation between the U.S. monetary policy shock series from Furceri et al. (2018) and that from Coibion (2012) is 0.62 for the overlapped sample. The correlation between the country-specific monetary policy shock series with the U.S. is typically small except for Canada, ensuring that the identification of international bank lending channel of monetary policy from other advanced economies is unlikely confounded by the effect of U.S. monetary policy on the short-term rates in these economies.¹⁶

A. Local projection method

We use Jordà (2005)’s local projection method to estimate the dynamic effect of monetary policy shocks on cross-border bank lending. The local projection method has been advocated by Auerbach and Gorodnichencko (2012) and Ramey and Zubairy (2018), among others, as a flexible alternative to VAR specifications without imposing the pattern generated by structural VARs. In the bilateral panel data setting, we adopt the local projection method over commonly used VAR models for the following specific reasons.¹⁷

First, the exogenous shocks we use are already orthogonalized to contemporaneous and expected future macroeconomic conditions. For this reason, we do not need to further identify monetary policy shocks using restrictions in VAR models—a common approach in many empirical analyses in both domestic and international setups.

Second, our estimation entails a large international panel dataset with a constellation of the fixed effects, which makes a direct application of standard VAR models more difficult. In addition, the local projection method obviates the need to estimate the equations for dependent variables other than the variable of interest, thereby significantly economizing on the number of estimated parameters.

Third, the local projection method is particularly suited to estimating nonlinearities (for example, how the effect of monetary policy shocks differs during expansions and recessions in the source economy), as its application is much more straightforward compared to non-linear structural VAR models, such as Markov-switching or threshold-VAR models. Moreover, it allows for incorporating various time-varying features of source (recipient) economies directly and allow for their endogenous response to monetary policy shocks.

Lastly, the error term in the following panel estimations is likely to be correlated across countries. This correlation would be difficult to address in the context of VAR models, but it is easy to handle in the local projection method by either clustering standard errors by time period or using the Driscoll-Kraay standard errors allowing for arbitrary correlations of the errors across countries and time (Driscoll and Kraay, 1998).

Despite the advantages mentioned above, the local projection method has some drawbacks compared to structural VARs. First, since the iterated VAR method produces more efficient parameter estimates than the local projection method, the impulse response function estimated by local projections is often associated with large confidence intervals. This problem of less precise estimates is exacerbated as a forecast horizon increases due to the decreasing sample size in each estimation. Thus, we report both 68% and 90% confidence intervals in the following analyses.

Second, compared to a single equation framework in the local projection method, structural VARs allow tracing the dynamic endogenous response of various macroeconomic variables in the system to monetary policy shocks, which in turn can also affect the dynamics of cross-border bank lending. We enhance the credibility of the identified shock by analyzing separately the effect of U.S. monetary policy shocks on domestic economic variables including domestic bank lending. Figure B.1 in the online appendix confirms that output and investment decline, while the nominal exchange rate appreciates in response to the exogenous monetary policy tightening. Although we find a weak price puzzle, the effect on CPI is not statistically significant. Most importantly, we also find that domestic bank lending decreases following an increase in bank funding costs, consistent with the bank lending channel of monetary policy in the domestic context.

B. International bank lending channel of U.S. monetary policy shocks

The local projection method simply requires the estimation of a series of regressions for each horizon h for each variable. Following Auerbach and Gorodnichencko (2012) and Ramey and Zubairy (2018), we run a series of regressions for different horizons, h = 1,2, ...H as follows:

where y_j,t is the log of exchange rate-adjusted cross-border bank claims from the U.S. to borrowers in countries j in time t; ${\alpha }_j^h$ is a recipient country-fixed effect, which controls unobserved time-invariant characteristics specific to a country j; MPshock_t is the measure of exogenous U.S. monetary policy shocks; X_J,t is a set of control variables including lags of the dependent variable and the monetary policy shocks, and various control variables in the recipient country j (for example, real GDP growth, the short-term interest rate, inflation, and the nominal exchange rate growth vis-à-vis the U.S.) and their lags.

While the impulse responses generated by the local projections are not an estimate of the total effects of U.S. monetary policy—due to a constellation of fixed effects—, the exogeneity of the monetary policy shock and controlling for the demand-side factors allow us to investigate the spillover channel of monetary policy. In the baseline analysis, we use four lags of control variables in X_j,t (i.e., n = 4), however, the selection of the lag length does not affect our findings.¹⁸

We estimate equation (3) using OLS, which would result in the inconsistency of the least-squares parameter estimates due to the combination of lagged dependent variables and fixed effects (Nickell, 1981). However, because the time-series dimension of the panel dataset is quite large, the inconsistency is unlikely a major concern. Following Auerbach and Gorodnichencko (2012), standard errors are clustered by time to account for the fact that the shock is identical to all recipient countries in any given period. Equation (3) is estimated for h=0, 1, 2,..., 7 so that we trace the dynamic effect of monetary policy shocks over two years. After dropping outliers and missing observations following the criterion explained above, our baseline estimation of the U.S. monetary policy shocks covers cross-border lending to 45 recipient countries.

Baseline results. Figure 1 presents the dynamic response of cross-border bank lending to exogenous U.S. monetary policy shocks. The results provide evidence of a significant negative effect which is consistent with the cross-border bank lending and the risk-taking channels of monetary policy. In particular, a 100 basis-point (bp) exogenous tightening is found to lead to more than a 10 percent decline in cross-border bank lending after two quarters, which is not only statistically but also economically significant.¹⁹

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Effect of a U.S. monetary policy shock on cross-border bank lending

Citation: IMF Working Papers 2019, 234; 10.5089/9781513518770.001.A001

Note: The graph shows the response of cross-border bank lending to a 100 bp U.S. monetary policy shock and their 68% and 90% confidence bands. Horizon h=0 captures the impact of the shock, and the units are in percentage.

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Table 1 summarizes the full estimation results using exogenous U.S. monetary policy shocks above. ²⁰ The coefficients on the lagged dependent variable are negative and highly statistically significant, suggesting that the growth rate of the cross-border bank lending is mean-reverting. The coefficients on a recipient country’s real GDP growth are positive, although they are not statistically significant in most cases.²¹

Table 1.

Baseline estimation results from a dynamic framework

Note: Estimates are based on equation (3). Autocorrelation and heteroskedasticity-consistent standard errors are clustered at the time levels. *** denotes 1% significant level, ** denotes 5% significance level, and * denotes 10% significance level. While we control for the four lags of the variables, we only report the estimation results up to one lag to save space here.

Table 1.

Baseline estimation results from a dynamic framework

	h=0	h=1	h=2	h=3	h=4	h=5	h=6	h=7
Log cross-border claims (-1)	-22.059*** (2.950)	-25.402*** (2.885)	-33.609*** (3.712)	-28.551*** (2.953)	-30.937*** (3.413)	-39.549*** (3.706)	-40.845*** (3.460)	-33.768*** (3.820)
Monetary policy shock	-2.429 (3.625)	-10.953** (4.282)	-12.077** (4.890)	-7.190 (4.624)	-3.251 (6.368)	-3.059 (6.090)	-0.464 (7.450)	2.391 (6.299)
Monetary policy shock (-1)	-5.659* (3.376)	0.279 (4.616)	5.548 (5.060)	9.389* (5.620)	6.796 (7.433)	8.397 (7.612)	8.043 (7.167)	11.608* (6.353)
Recipient GDP growth	0.809* (0.486)	0.415 (0.643)	0.807 (0.734)	0.886 (0.869)	0.619 (0.874)	1.256 (0.996)	1.352 (1.057)	0.574 (1.136)
Recipient GDP growth (-1)	-0.333 (0.502)	0.349 (0.536)	-0.021 (0.736)	-0.305 (0.732)	0.670 (0.905)	0.738 (1.025)	0.217 (1.039)	0.551 (0.995)
Recipient interest rate	0.042 (0.132)	0.025 (0.141)	-0.139 (0.238)	0.167 (0.174)	-0.109 (0.207)	-0.013 (0.227)	-0.196 (0.243)	-0.307 (0.269)
Recipient interest rate (-1)	-0.051 (0.154)	-0.196 (0.179)	0.228 (0.196)	-0.303 (0.187)	0.178 (0.234)	0.047 (0.247)	0.099 (0.282)	-0.095 (0.304)
Recipient exchange rate	-0.292** (0.113)	-0.373** (0.158)	-0.633*** (0.175)	-0.829*** (0.184)	-0.542** (0.226)	-0.567** (0.263)	-0.643*** (0.243)	-0.748*** (0.272)
Recipient exchange rate (-1)	-0.233** (0.115)	-0.191 (0.137)	-0.285* (0.149)	-0.047 (0.181)	-0.100 (0.226)	-0.344 (0.228)	-0.307 (0.240)	-0.232 (0.260)
Recipient inflation rate	0.307 (0.448)	0.114 (0.579)	0.832 (0.834)	1.517** (0.643)	0.328 (0.705)	1.109 (0.884)	0.483 (0.703)	0.755 (0.770)
Recipient inflation rate (-1)	0.272 (0.507)	0.626 (0.778)	0.131 (0.896)	-1.243* (0.728)	0.114 (0.919)	-1.403 (1.072)	0.059 (0.845)	0.813 (0.829)
Obs	3,064	3,018	2,979	2,934	2,889	2,855	2,807	2,770
R-squared	0.09	0.11	0.14	0.14	0.16	0.20	0.23	0.24
Recipient country-fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Note: Estimates are based on equation (3). Autocorrelation and heteroskedasticity-consistent standard errors are clustered at the time levels. *** denotes 1% significant level, ** denotes 5% significance level, and * denotes 10% significance level. While we control for the four lags of the variables, we only report the estimation results up to one lag to save space here.

View Table

Table 1.

Baseline estimation results from a dynamic framework

	h=0	h=1	h=2	h=3	h=4	h=5	h=6	h=7
Log cross-border claims (-1)	-22.059*** (2.950)	-25.402*** (2.885)	-33.609*** (3.712)	-28.551*** (2.953)	-30.937*** (3.413)	-39.549*** (3.706)	-40.845*** (3.460)	-33.768*** (3.820)
Monetary policy shock	-2.429 (3.625)	-10.953** (4.282)	-12.077** (4.890)	-7.190 (4.624)	-3.251 (6.368)	-3.059 (6.090)	-0.464 (7.450)	2.391 (6.299)
Monetary policy shock (-1)	-5.659* (3.376)	0.279 (4.616)	5.548 (5.060)	9.389* (5.620)	6.796 (7.433)	8.397 (7.612)	8.043 (7.167)	11.608* (6.353)
Recipient GDP growth	0.809* (0.486)	0.415 (0.643)	0.807 (0.734)	0.886 (0.869)	0.619 (0.874)	1.256 (0.996)	1.352 (1.057)	0.574 (1.136)
Recipient GDP growth (-1)	-0.333 (0.502)	0.349 (0.536)	-0.021 (0.736)	-0.305 (0.732)	0.670 (0.905)	0.738 (1.025)	0.217 (1.039)	0.551 (0.995)
Recipient interest rate	0.042 (0.132)	0.025 (0.141)	-0.139 (0.238)	0.167 (0.174)	-0.109 (0.207)	-0.013 (0.227)	-0.196 (0.243)	-0.307 (0.269)
Recipient interest rate (-1)	-0.051 (0.154)	-0.196 (0.179)	0.228 (0.196)	-0.303 (0.187)	0.178 (0.234)	0.047 (0.247)	0.099 (0.282)	-0.095 (0.304)
Recipient exchange rate	-0.292** (0.113)	-0.373** (0.158)	-0.633*** (0.175)	-0.829*** (0.184)	-0.542** (0.226)	-0.567** (0.263)	-0.643*** (0.243)	-0.748*** (0.272)
Recipient exchange rate (-1)	-0.233** (0.115)	-0.191 (0.137)	-0.285* (0.149)	-0.047 (0.181)	-0.100 (0.226)	-0.344 (0.228)	-0.307 (0.240)	-0.232 (0.260)
Recipient inflation rate	0.307 (0.448)	0.114 (0.579)	0.832 (0.834)	1.517** (0.643)	0.328 (0.705)	1.109 (0.884)	0.483 (0.703)	0.755 (0.770)
Recipient inflation rate (-1)	0.272 (0.507)	0.626 (0.778)	0.131 (0.896)	-1.243* (0.728)	0.114 (0.919)	-1.403 (1.072)	0.059 (0.845)	0.813 (0.829)
Obs	3,064	3,018	2,979	2,934	2,889	2,855	2,807	2,770
R-squared	0.09	0.11	0.14	0.14	0.16	0.20	0.23	0.24
Recipient country-fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Note: Estimates are based on equation (3). Autocorrelation and heteroskedasticity-consistent standard errors are clustered at the time levels. *** denotes 1% significant level, ** denotes 5% significance level, and * denotes 10% significance level. While we control for the four lags of the variables, we only report the estimation results up to one lag to save space here.

The coefficients on the recipient country’s short-term interest rate are not statistically significant. While this finding is in contrast to Bruno and Shin (2015a), who find that a higher interest rate in a recipient country increases cross-border bank lending toward this country, it is mostly driven by the emerging market recipient economies in the sample where the interest rates have been typically countercyclical. Indeed, when we restrict the set of recipient countries to advanced economies, we find a positive and statistically significant coefficient on the recipient country’s short-term interest rate, consistent with the finding that interest rate differentials are a strong pull factor of cross-border banking flows.

The results on the exchange rate suggest that a depreciation of the local currency is associated with a decline in cross-border bank lending toward the recipient country, which is consistent with the findings from the existing studies (Cerutti et al., 2017; Correa et al., 2017; Choi and Furceri, 2019). Bruno and Shin (2015b) also show that U.S. dollar appreciation induces a contraction of cross-border bank lending due to an increase in the risk of global banks’ balance sheets, suggesting a balance sheet channel of U.S. monetary policy spillovers. Overall, the signs of coefficients of recipient country variables are consistent with the literature on push vs. pull factors of cross-border banking flows.

Comparison with the literature. Previous studies have typically examined the response of cross-border bank lending to changes in the monetary policy instrument, using a static framework. To compare our results with those, we perform two exercises. First, we regress the quarterly growth of cross-border bank lending on the lagged level of the Federal funds rate, which follows closely the static specification in Correa et al. (2017).²² Column (I) in Table 2 summarizes the estimation results. Consistent with many existing studies, an increase in the U.S. monetary policy rate is associated with an increase in cross-border bank lending. Column (II) provides the estimation results using the changes in the Federal funds rate instead of its lagged level, which deliver similar results although the estimated coefficient of interest is not statistically significant. Interestingly, the sign of the estimated coefficient of interest switches its sign when employing the exogenous U.S. monetary policy shocks (column III).²³

Table 2.

Results using a static framework

Note: The dependent variables are the growth rate of exchange rate-adjusted U.S. bilateral cross-border claims. The measure of monetary policy shocks is the lagged Federal funds rate in column (I) and (IV), the changes in the Federal funds rate in column (II) and (V), and the exogenous monetary policy shocks from Coibion (2012) in column (III) and (VI). Heteroskedasticity-robust standard errors are clustered at the time levels. *** denotes 1% significant level, ** denotes 5% significance level, and * denotes 10% significance level.

Table 2.

Results using a static framework

Independent variables	(I)	(II)	(III)	(IV)	(V)	(VI)
The lagged Federal funds rate	0.714** (0.301)			0.616** (0.285)
Changes in the Federal funds rate		0.083 (1.529)			0.120 (1.489)
Monetary policy shock			-0.349 (3.212)			-0.323 (3.183)
Lagged GDP growth (U.S.)	0.636 (1.434)	0.970 (1.651)	0.996 (1.527)	0.516 (1.427)	0.847 (1.625)	0.884 (1.516)
Lagged stock returns (U.S.)	0.190 (0.133)	0.178 (0.135)	0.180 (0.134)	0.195 (0.132)	0.184 (0.133)	0.186 (0.133)
Lagged inflation rate (U.S.)	-3.257 (1.964)	-2.433 (1.862)	-2.421 (1.858)	-3.318* (1.913)	-2.504 (1.802)	-2.489 (1.799)
Lagged GDP growth (recipient)	-0.560 (0.629)	-0.435 (0.629)	-0.427 (0.633)	-0.341 (0.605)	-0.300 (0.603)	-0.293 (0.606)
Lagged short-term interest rate (recipient)	0.005 (0.094)	0.071 (0.091)	0.071 (0.091)	0.036 (0.080)	0.077 (0.080)	0.077 (0.080)
Lagged inflation (recipient)	0.257 (0.449)	0.219 (0.454)	0.216 (0.456)	0.254 (0.404)	0.168 (0.406)	0.164 (0.409)
Lagged exchange rate growth (recipient)	-0.370*** (0.129)	-0.337** (0.128)	-0.334** (0.131)	-0.372*** (0.129)	-0.338** (0.128)	-0.336** (0.132)
Obs	3,281	3,281	3,281	3,281	3,281	3,281
R-squared	0.02	0.02	0.02	0.01	0.01	0.01
Recipient country-fixed effect	Yes	Yes	Yes	No	No	No

Note: The dependent variables are the growth rate of exchange rate-adjusted U.S. bilateral cross-border claims. The measure of monetary policy shocks is the lagged Federal funds rate in column (I) and (IV), the changes in the Federal funds rate in column (II) and (V), and the exogenous monetary policy shocks from Coibion (2012) in column (III) and (VI). Heteroskedasticity-robust standard errors are clustered at the time levels. *** denotes 1% significant level, ** denotes 5% significance level, and * denotes 10% significance level.

View Table

Table 2.

Results using a static framework

Independent variables	(I)	(II)	(III)	(IV)	(V)	(VI)
The lagged Federal funds rate	0.714** (0.301)			0.616** (0.285)
Changes in the Federal funds rate		0.083 (1.529)			0.120 (1.489)
Monetary policy shock			-0.349 (3.212)			-0.323 (3.183)
Lagged GDP growth (U.S.)	0.636 (1.434)	0.970 (1.651)	0.996 (1.527)	0.516 (1.427)	0.847 (1.625)	0.884 (1.516)
Lagged stock returns (U.S.)	0.190 (0.133)	0.178 (0.135)	0.180 (0.134)	0.195 (0.132)	0.184 (0.133)	0.186 (0.133)
Lagged inflation rate (U.S.)	-3.257 (1.964)	-2.433 (1.862)	-2.421 (1.858)	-3.318* (1.913)	-2.504 (1.802)	-2.489 (1.799)
Lagged GDP growth (recipient)	-0.560 (0.629)	-0.435 (0.629)	-0.427 (0.633)	-0.341 (0.605)	-0.300 (0.603)	-0.293 (0.606)
Lagged short-term interest rate (recipient)	0.005 (0.094)	0.071 (0.091)	0.071 (0.091)	0.036 (0.080)	0.077 (0.080)	0.077 (0.080)
Lagged inflation (recipient)	0.257 (0.449)	0.219 (0.454)	0.216 (0.456)	0.254 (0.404)	0.168 (0.406)	0.164 (0.409)
Lagged exchange rate growth (recipient)	-0.370*** (0.129)	-0.337** (0.128)	-0.334** (0.131)	-0.372*** (0.129)	-0.338** (0.128)	-0.336** (0.132)
Obs	3,281	3,281	3,281	3,281	3,281	3,281
R-squared	0.02	0.02	0.02	0.01	0.01	0.01
Recipient country-fixed effect	Yes	Yes	Yes	No	No	No

Note: The dependent variables are the growth rate of exchange rate-adjusted U.S. bilateral cross-border claims. The measure of monetary policy shocks is the lagged Federal funds rate in column (I) and (IV), the changes in the Federal funds rate in column (II) and (V), and the exogenous monetary policy shocks from Coibion (2012) in column (III) and (VI). Heteroskedasticity-robust standard errors are clustered at the time levels. *** denotes 1% significant level, ** denotes 5% significance level, and * denotes 10% significance level.

Second, we examine the dynamic effect by re-estimating equation (3) using changes in the Federal funds rate as a measure of monetary policy shocks. Also, in this case, we do not find evidence of negative effects of monetary policy: the effect is not statistically significant over the all estimation horizons with a smaller magnitude and mixed signs (Figure 2).²⁴ These results suggest that simply controlling for domestic variables that might affect cross-border bank lending is not sufficient to identify the causal effect of monetary policy and test which channel of monetary policy is at play (the international bank lending and risk-taking vs. the portfolio rebalancing channel). Once the endogeneity in changes in the monetary policy stance is accounted for, even global banks are not insulated from domestic monetary policy. In addition, the responses of bank lending to monetary policy shocks are better captured by a dynamic framework than a static one mixing short- and medium-term effects.

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Effect of a change in the Federal funds rate on cross-border bank lending

Citation: IMF Working Papers 2019, 234; 10.5089/9781513518770.001.A001

Note: The graph shows the response of cross-border bank lending to a 100 bp increase in the Federal funds rate and their 68% and 90% confidence bands. Horizon h=0 captures the impact of the shock, and the units are in percentage.

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High-frequency identification with external instruments. We verify the validity of our baseline results by further exploiting an alternative identification strategy based on high-frequency data used as external instruments (Gertler and Karadi, 2015; Cloyne et al., 2018, Miranda-Agrippino and Rey, 2019). This hybrid approach, proposed by Gertler and Karadi (2015), combines the high-frequency identification widely used in the finance literature (Kuttner, 2001; Gürkaynak et al., 2005) with external instrument methods (Mertens and Ravn, 2013). The main idea behind this approach is that changes in the Federal funds futures in a narrow window (e.g., 30 minutes) around the FOMC monetary policy announcements capture the unexpected Fed policy actions. The key assumption is that these financial market surprises are uncorrelated with shocks other than the monetary policy ones. Since these changes are a noisy measure of the monetary policy structural shock, Gertler and Karadi (2015) used them as instruments in a proxy-SVAR framework.²⁵

One drawback of this approach is that it is not immune to policy foresight issues coming from the mismatch of the information set of the private agents and the policymakers. In other words, the FOMC may have additional information about the future path of the economy. Without accounting for these different information sets in the VAR, the shock may incorporate the endogenous response of the policy instrument to the expected future path of macroeconomic variables (Ramey, 2016). Furthermore, these shocks can be correlated with the Fed’s real-time forecasts of relevant macroeconomic variables, and therefore not exogenous.²⁶ In contrast, the narrative approach used in the baseline analysis does not suffer from these problems, as the shocks are orthogonal to the Fed’s real-time forecasts of relevant macroeconomic variables.

We estimate Gertler and Karadi (2015)’s monthly reduced-form VAR over the period 1990M1–2012M12. The VAR includes U.S. industrial production and the consumer price index (both in logarithm), the government bond yields at different maturities (the policy indicator), and the excess bond premium constructed by Gilchrist and Zakrajšek (2012), which is an indicator for refinancing conditions on the secondary corporate bond markets. We then apply Ramey (2016)’s three-step approach to extract the structural monetary policy shocks, and, following Cloyne et al. (2018), we aggregate these surprises at a quarterly frequency and use them as instruments in our analysis of cross-border banking flows. Specifically, we modify equation (3), substituting the Romer and Romer (2004) monetary policy shock with the policy instrument i_t (Treasury bond yield) instrumented with the VAR-estimated monetary policy shock:

In terms of covariates, X_j,t consists of the same set of control variables as in equation (3), namely the lags of the dependent variable, the lags of the estimated monetary policy shocks, and other control variables in the recipient country j. As any other instrumental variable framework, additionally to the exogeneity condition (i.e., the external instrument must be uncorrelated with the other structural shocks), the instrument must satisfy the relevance condition—namely, it should be contemporaneously and highly correlated with the instrumented variable. Following Gertler and Karadi (2015), we test different combinations of policy indicators (three and six-month government bond yields as well as one/two/three/five/seven/ten-year government bond yields) and instruments (six/nine-month and one-year ahead on three-month Eurodollar deposits, current/one/two-month Fed funds futures). Based on the F-test of the joint model (the F-statistic on the first stage) and the Stock and Yogo (2005) critical values, we chose the combinations that pass the weak instrument test.

Figure 3 presents the results obtained using the two-year government bond yield as a policy indicator and surprises in the three-month ahead futures as an instrument. As argued by Gertler and Karadi (2015), the two-year government bond yields better capture monetary policy actions during forward guidance. The F-statistic far exceeds the Stock and Yogo (2005) critical values and the results confirm that exogenous U.S. monetary policy tightening leads to a statistically and economically significant decline in cross-border bank lending.²⁷ Figure A.5 in the appendix shows the evolution of the structural shocks from the proxy-SVAR over the sample period, using the above-mentioned combination of policy indicator and instrument, and compares it with the shock identified through the narrative approach and the changes in the Federal funds rate. Our finding is robust to a larger set of non-weak instruments and treasuries yield combinations (see Figure A.6 in the appendix) as well as restricting the sample to 2008Q4.²⁸

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Effect of a U.S. monetary policy shock (proxy-SVAR structural shock) on cross-border bank lending

Citation: IMF Working Papers 2019, 234; 10.5089/9781513518770.001.A001

Note: Response of cross-border bank lending to a 100 bp U.S. monetary policy structural shock identified via proxy-SVAR and their 68% and 90% confidence bands. Horizon h=0 captures the impact of the shock, and the units are in percentage.

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Robustness checks. In this section, we further test the robustness of our baseline findings to various sensitivity tests: (i) including domestic control variables, (ii) using different lag length selections, (iii) using an alternative way of computing standard errors, and (iv) controlling for time-varying country-pair variables such as bilateral trade flows. To save space, the results from robustness checks are reported in the appendix.

First, since our measures of monetary policy shocks are exogenous, we do not control for any other macroeconomic variables in the U.S. economy in the baseline analysis. Indeed as shown in Panel A in Figure A.7 in the appendix, the inclusion of additional control variables (U.S. real GDP growth, inflation rate, and stock returns) does not result in any material changes in the estimated impulse response functions.

Second, we have used four lags of the dependent variable and the control variables in the baseline analysis. We demonstrate that our findings do not depend critically on the selection of lags. Panel B in Figure A.7 shows that our results hardly change with the selection of eight lags.

Third, while we have clustered standard errors at the time level in the baseline specification, we test the robustness of our findings by clustering standard errors at the recipient country level or at the recipient country-time level. We also compute Driscoll-Kraay standard errors that allow arbitrary correlations of the errors across countries and time. We only report the results from using Driscoll-Kraay standard errors in Panel C in Figure A.7 to save space, but the results obtained using standard errors clustered at the recipient country level and at the recipient country-time level are similar to those clustered at the time level. In sum, the statistical significance of our findings does not hinge on the way we account for the correlations in the error term.

The use of the recipient country-fixed effects and a recipient country’s macroeconomic variables cannot fully control for potential time-varying factors affecting cross-border banking flows at the bilateral level. One obvious candidate for such factors is bilateral trade flows between the U.S. and its counterpart countries. This variable is particularly relevant for the study of international capital flows, as the current account and the financial account are tightly related by the accounting identity. While banking flows correspond to only a subset of total capital flows, therefore mitigating this problem, we test the robustness of our findings by controlling for bilateral trade flows.²⁹ We take bilateral trade flow data from the IMF Directions of Trade Statistics. We add the growth of U.S. imports and exports (and their lags) from and to country j and re-estimate equation (3). Panel D in Figure A.7 shows that our results are nearly identical when controlling for bilateral trade flows.

Controlling for global financial and liquidity risks. We test whether our findings are robust to controlling for two additional global factors: financial and liquidity risks. First, given the importance of the VIX in driving the price of risky assets and international capital flows worldwide (Rey, 2013), a natural question is whether our findings are biased due to the omission of this global factor. In a related context, Bekaert et al. (2013) find that U.S. monetary policy and the VIX are systematically related. Moreover, the VIX has been recently found to be an important driver of cross-border bank lending regardless of its precise interpretation as global uncertainty, risk appetite, or financial distress (Bruno and Shin, 2015a; Cerutti et al., 2017; Correa et al., 2017; Wang, 2018; Choi and Furceri, 2019). Second, the recent empirical literature has also documented an important role of liquidity shocks in driving cross-border banking flows (Cetorelli and Goldberg, 2011; Schnabl, 2012; Correa et al., 2015; Shim and Shin, 2018). Following Correa et al. (2015), we use the LIBOR-OIS spread—the rate at which banks use lending to one another—to measure liquidity conditions in interbank markets.³⁰

To address these issues and disentangle the importance of U.S. monetary policy shocks in driving cross-border banking flows from that of global financial and liquidity shocks, we augment equation (3) with the log level of the VIX (and their four lags) and the level of the LIBOR-OIS spread (and their four lags), respectively. Figure A.8 in the appendix confirms that the effect of U.S. monetary policy shocks on cross-border bank lending is independent of global financial and liquidity risks.³¹

State-dependency in the international bank lending channel of U.S. monetary policy. There exists vast empirical evidence that the effect of monetary policy shocks on real and financial variables is state-dependent (Tenreyro and Thwaites, 2016). The average response of cross-border bank lending presented in the previous section may mask substantial heterogeneity depending on the underlying economic regime at the time of the monetary policy shock (expansions vs. recessions). To examine this possibility, we estimate the following equation in which the dynamic response is allowed to vary with the state of the economy:

where z_t is an indicator of the state of the economy normalized to have zero mean and unit variance. The estimated parameters depend on the average behavior of the economy in the historical sample between t and t+h, given the shock, the initial state, and the control variables.

Since the parameter estimates on the control variables incorporate the average tendency of the economy evolving between the states, the estimates incorporate both natural transitions and endogenous transitions from one state to the other that occur in the data. The indicator of the state of the economy is the five-quarter moving average of real GDP growth and F(z_t) is a smooth transition function used to estimate the effect of monetary policy shocks in expansions vs. recessions.³² We choose θ = 1.5 following Auerbach and Gorodnichencko (2012) so that the economy spends about 20 percent of the time in a recessionary regime.³³ As shown in Figure A.9 in the appendix, the probability of a recession regime we estimate using a smooth transition function captures well the official NBER recession dates.

This approach is equivalent to the smooth transition autoregressive model developed by Granger and Teräsvirta (1993) and has the following advantages. First, compared with a model in which each dependent variable would interact with a measure of the business cycle position, it permits a direct test of whether the effect of monetary policy shocks on cross-border banking flows varies across different regimes. Second, compared with estimating structural VARs for each regime, it allows the effect of monetary policy shocks to change smoothly between recessions and expansions by considering a continuum of states to compute the impulse response functions, thus making the response more stable and precise. Third, we can use the full sample for estimation, which makes our estimates more precise.

The coefficients $${\beta }_E^{h}and{\beta }_R^h$$ trace the dynamic response to monetary policy shocks when the economy is in expansions and recessions, respectively. Figure 4 presents the dynamic responses during recessions and expansions using exogenous monetary policy shocks and changes in the Federal funds rate. We do not find a meaningful state-dependent effect of monetary policy shocks on cross-border bank lending in the former, although the response is weaker and less precisely estimated during recessions (top panel). In contrast, the sign of the response of cross-border bank lending in the latter differs between expansions and recessions (bottom panel). While an increase in the Federal funds rate is associated with a decrease in cross-border banking flows in expansions, the effect is positive in recessions. This difference further suggests an identification issue when using changes in the policy rate to measure monetary policy shocks.

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Effect of a U.S. monetary policy shock on cross-border bank lending: expansions vs. recessions

Citation: IMF Working Papers 2019, 234; 10.5089/9781513518770.001.A001

Note: The top panel shows the response of cross-border bank lending to a 100 bp U.S. monetary policy shock and their 68% and 90% confidence bands during expansions (left) and recessions (right). The bottom panel shows the response of cross-border bank lending to a 100 bp increase in the Federal funds rate and their 68% and 90% confidence bands during expansions (left) and recessions (right). Horizon h=0 captures the impact of the shock, and the units are in percentage.

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The effect of U.S. monetary policy on cross-border banking flows may also depend on global financial conditions. To test this hypothesis, we repeat a similar exercise to the one for the state of the business cycles but identifying global financial condition regimes based on the VIX.³⁴ Figure 5 shows that the international bank lending channel of U.S. monetary policy tends to be weaker during the high-uncertainty period (high-VIX period), consistent with the recent findings that heightened uncertainty reduces the effectiveness of monetary policy (Aastveit et al., 2017; Castelnuovo and Pellegrino, 2018).

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Effect of a U.S. monetary policy shock on cross-border bank lending: low uncertainty vs. high-uncertainty periods

Citation: IMF Working Papers 2019, 234; 10.5089/9781513518770.001.A001

Note: The graph shows the response of cross-border bank lending to a 100 bp U.S. monetary policy shock and their 68% and 90% confidence bands during low-uncertainty (left panel) and high-uncertainty (right panel) periods using the exogenous monetary policy shocks. Horizon h=0 captures the impact of the shock, and the units are in percentage.

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Monetary policy tightening vs. easing. The effect of monetary policy on economic activity is found to be larger for monetary policy tightening than easing (Cover, 1992; Tenreyro and Thwaites, 2016). To test for a similar asymmetry on its effects on cross-border banking flows, we estimate the following specification:

where D_t is a dummy variable that takes a value of one for monetary policy tightening and zeroes otherwise, and $${\beta }_{+}^{h}and{\beta }_{-}^h$$ capture the effect of a monetary tightening and easing, respectively. The results presented in the top panel of Figure A. 10 in the appendix do not point to significant evidence of asymmetric effects of monetary policy shocks on cross-border bank lending in this case.

While the previous exercise implicitly assumes that the occurrence of monetary policy tightening and easing does not depend on the underlying state of the business cycles, it is likely that easing (tightening) is more common during recessions (expansions). Thus, we further consider the underlying state of the business cycles and the type of shocks jointly. The bottom panel in Figure A. 10 in the appendix compares the effect of monetary tightening during expansions with that of monetary easing during recessions. If anything, monetary policy tightening during expansions seems to have a more persistent effect than monetary easing during recessions, consistent with the asymmetry found in the domestic effect of monetary policy shocks.

Borrower country riskiness and international portfolio rebalancing channel of U.S. monetary policy. In this section, we investigate whether borrower country riskiness affects the effect of U.S. monetary policy on cross-border bank lending by utilizing the heterogeneity in the pool of foreign borrowers. Correa et al. (2017) and Argimon et al. (2019) argue that domestic monetary policy tightening may increase cross-border bank lending because tighter monetary policy actions, by eroding the net worth and collateral value of domestic borrowers, can lead to a reallocation of lending toward relatively safer borrowers abroad. If this mechanism is at work, we would observe a larger decline in cross-border bank lending toward relatively riskier recipient countries. Since measuring ex-ante riskiness of borrowers at a country level is a challenging task we take two complementary approaches to enhance the credibility of our results. First, we adopt the Political Risk Index of ICRG (International Country Risk Guide) and separate the recipient countries into two groups based on their sample average of the total ICRG index. A country with higher than the above median value is considered as a safe borrower, and vice versa. Second, we simply group the recipient countries into advanced and emerging market economies based on their IMF classification.

Figure 6 shows the results using the ICRG index as a proxy for borrower riskiness. The effects on cross-border bank lending are negative and statistically significant in both cases. Moreover, their magnitudes are similar and the difference is not statistically significant. Figure A.11 in the appendix also confirms that the effects are similar when using alternative measures of riskiness.³⁵ Although detailed counterparty-level data is required to draw a more complete picture of the underlying mechanism, the evidence from the recipient-country level data does not support the prediction of the international portfolio rebalancing channel.

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Effect of a U.S. monetary policy shock on cross-border bank lending: safe vs. risky borrower countries

Citation: IMF Working Papers 2019, 234; 10.5089/9781513518770.001.A001

Note: The graph shows the response of cross-border bank lending to a 100 bp U.S. monetary policy shock and their 68% and 90% confidence bands. The left (right) panel shows the response of cross-border bank lending to borrowers in safe (risky) economies based on the ICRG index. Horizon h=0 captures the impact of the shock, and the units are in percentage.

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C. International bank lending channel of monetary policy in other advanced economies

So far, we have focused on the international bank lending channel of U.S. monetary policy. However, a natural question is whether we can generalize the U.S. results to other systemically important advanced economies. Despite its paramount importance in policymaking, the existing studies have not reached a consensus on this issue.

For example, Correa et al. (2017) find that monetary policy tightening—identified by an increase in the policy rate—in a panel of 29 (mostly advanced) economies induces an increase in cross-border bank lending toward recipient countries during the similar sample period to our study and propose the international portfolio rebalancing channel as an alternative explanation. Other studies document an asymmetric effect on cross-border lending of monetary policy between the U.S. and other countries. For example, Avdjiev et al. (2018) find that while U.S. monetary easing fuels cross-border lending in U.S. dollars, a monetary tightening in other lender countries increases international dollar lending, as global banks turn to U.S. dollars for cheaper funding and lend toward borrowers abroad. Argimon et al. (2019) also show that the transmission channel of monetary policy through a banking system in Spain or the Netherlands can be quite different from that in the U.S.

To shed light on this issue, we extend equation (3) to incorporate the bilateral panel structure of the data as follows:

where i and j indicate source and recipient country, respectively; y_i,j,t is the log of cross-border lending from global banks located in country i to borrowers in country j in time t; ${\alpha }_{i,j}^h$ is a source-recipient fixed effect; ${\alpha }_{j,t}^h$ is a recipient-time fixed effect; MPshock_i,t is the measure of exogenous monetary policy shocks in country i described earlier; X_i,j,t is a set of control variables, including four lags of the dependent variable and of the monetary policy shocks.

Equation (7) is estimated using the bilateral cross-border banking data between the eight source countries with the available data on exogenous monetary policy shocks (Canada, Germany, Italy, Japan, the Netherlands, Spain, Sweden, and the U.K.) and their (maximum of) 45 recipient countries from 2001Q1 to 2012Q4. A shorter time span of the other advanced economy data compared to the analysis of the U.S. monetary policy is compensated by a larger cross-sectional dimension of the data. The identification strategy in equation (7) relies on the existence of bank lending from multiple source countries to one given recipient country at a given point in time, which resembles the identification strategy in Cetorelli and Goldberg (2011). The advantages of having a bilateral panel dataset in our context are threefold.

First, it mitigates concerns about reverse causality. While it is difficult to identify causal effects of country-specific shocks using aggregate capital flows, it is much more likely that domestic monetary policy shocks in country i affect cross-border bank lending toward a particular country j than the other way around. Second, the inclusion of the country-pair fixed effects allows us to control for any unobserved time-invariant characteristic between two countries, such as a set of gravity factors.³⁶ The inclusion of recipient country-time fixed effects further allows us to control for any macroeconomic shocks affecting recipient countries, including external and idiosyncratic recipient-specific shocks as well as the indirect impact of monetary tightening through other recipient countries. Together with the exogenous nature of monetary policy shocks in each lender country, the inclusion of these two-way fixed effects gives a clear identification of the international bank lending channel of monetary policy, largely immune to endogeneity issues. Third, the inclusion of recipient country-time fixed effects allows us to maximize the sample coverage of our analysis because some recipient countries might not have sufficient time-series data on some of the control variables.

Baseline results. Figure 7 shows the response of cross-border lending to the exogenous monetary policy shocks in the eight advanced economies for which the exogenous monetary policy shocks are available. To demonstrate the importance of using exogenous monetary policy shocks in identifying the international bank lending channel of monetary policy, we also present the results using changes in the policy rate as a measure of monetary policy shocks. Consistent with the U.S. evidence, we do not find any effect of monetary policy actions in the short run when using changes in the policy rate, whereas we find negative and significant effects of an exogenous monetary tightening on cross-border bank lending, suggesting that there is no asymmetry about the bank lending channel of monetary policy between the U.S. and other advanced economies.

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Effect of a monetary policy shock on cross-border bank lending from eight OECD countries

Citation: IMF Working Papers 2019, 234; 10.5089/9781513518770.001.A001

Note: The graph shows the response of cross-border bank lending to a 100 bp change in the policy rate and their 68% and 90% confidence bands (left) and monetary policy shock (right) in the eight OECD countries. Horizon h=0 captures the impact of the shock, and the units are in percentage.

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A one percent point exogenous tightening in monetary policy leads to an about 5 percent decline in cross-border bank lending after one quarter and the decline reaches more than 10 percent after one year, suggesting that the cross-border bank lending channel of monetary policy is not only statistically but also economically significant for other systemically important economies. While these findings are in sharp contrast to those in Correa at al. (2017) and Avdjiev et al. (2018), they further demonstrate that the identification of the causal effect of monetary policy on cross-border banking flows hinges on separating the exogenous component in policy rates from the endogenous response to changes in the economic environment.

Robustness checks and additional exercises. As for the U.S. analysis, we conduct various robustness checks and additional exercises to confirm the validity of our findings. First, while we use monetary policy shocks identified using forecast data in the eight advanced economies, countries in the euro area are subject to the same policy rate. Thus, heterogeneity in the identified shocks is only driven by forecast errors on economic development, not policy rates, resulting in potential correlation in the error term across countries. To address this issue, we repeat our exercise after dropping the euro-area countries other than Germany from the sample. Second, the inclusion of the two-way fixed effects in equation (7) does not control for economic conditions in lender countries that might affect cross-border bank lending simultaneously. To confirm the exogeneity of the identified monetary policy shocks and disentangle their effect from other confounding factors, we control for domestic macroeconomic variables (real GDP growth, inflation, stock returns, and the growth rate of the exchange rate vis-à-vis the U.S.) in the eight lender countries. Figure A.13 in the appendix confirms that the results are robust to these alternative specifications.

We further conduct the following sensitivity tests: (i) alternative standard error clustering; (ii) including more lags; and (iii) controlling for bilateral trade flows. Our findings are largely unaffected by these alternative specifications, and the results are available upon request. We test whether the spillover effect of monetary policy is asymmetric between the low-uncertainty and the high-uncertainty periods. Consistent with the evidence from the spillover effect of U.S. monetary policy shocks in the previous section, the results tend to be stronger during the former, further suggesting a monetary policy ineffectiveness in the presence of heightened uncertainty (Figure A.14 in the appendix).

Lastly, given the central role of European banks in shaping global banking flows (Cetorelli and Goldberg, 2011; Ivashina et al., 2015), an interesting question is whether the international bank lending channel of monetary policy in the euro area operates differently toward borrowers in the euro and non-euro area. A common monetary policy framework and currency in the euro area might weaken the cross-border effect of monetary policy shocks. To answer this question, we conduct a subsample analysis for cross-border bank lending from euro-area countries to (i) euro-area countries and (ii) non-euro area countries. If anything, the results suggest a stronger spillover toward the non-euro area economies (Figure A.15 in the appendix).