In the face of spillovers : prudential policies in emerging economies.
Date: 2020-08-20
Institutions: European Central Bank
Subjects: United States, credit policy, economic cycle, financial supervision, macroeconomics, monetary policy, newly industrialised country

Andra Coman, Simon P. Lloyd 

Working Paper Series 

In the face of spillovers:  
prudential policies in  
emerging economies 

No 2339 / December 2019 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Abstract

We examine whether emerging market prudential policies help to reduce the macro-

ﬁnancial spillover effects of US monetary policy. We ﬁnd that emerging markets with tighter

prudential policies face signiﬁcantly smaller, and less negative, spillovers to total credit

from US monetary policy tightening shocks. Loan-to-value ratio limits and reserve require-

ments appear to be particularly effective prudential measures at mitigating the spillover

effects of US monetary policy. Our ﬁndings indicate that domestic prudential policies can

dampen emerging markets’ exposure to US monetary policy and the associated global ﬁ-

nancial cycle, even when accounting for capital controls, suggesting they may be a useful

tool in the face of international macroeconomic policy trade-offs.

JEL Codes: E52, E58, E61, F44.

Key Words: International spillovers; Local projections; Policy Interactions; Monetary policy;

Prudential policy.

ECB Working Paper Series No 2339 / December 20191Non-Technical Summary

Emerging markets (EMs) are often disproportionately hit by spillovers from US monetary

policy, in part due to their exposure to the ‘global ﬁnancial cycle’. This poses challenges for pol-

icymakers, who can only pursue independent monetary policy with recourse to capital controls

or prudential policies. We show that, in the face of global spillovers, prudential policies in EMs

can help to offset US monetary policy spillovers, helping to resolve policymakers’ ‘dilemma’.

We use data measuring a range of prudential policy actions in 29 EMs from 2000 to 2017,

spanning capital and reserve requirements, LTV ratio limits, interbank exposure limits and

concentration limits. Combined with a measure of unexpected US monetary policy shocks,

we estimate the interaction between US monetary policy and EM prudential policies within a

panel local projections setup. We examine how domestic lending (total and bank credit) and

house prices in EMs respond to US monetary policy shocks, and how this differs depending on

the prudential policy actions taken by each EM.

We ﬁnd that EMs with tighter prudential policy face signiﬁcantly smaller, and less negative,

spillovers to total domestic credit from US monetary policy tightening shocks. Importantly,

this result is robust to accounting for interactions from other factors, such as capital controls.

A +1pp exogenous tightening of US monetary policy leads to around 7% fall in total credit

and 5% fall in bank credit in EMs on average. However, an EM with an additional (a one

standard deviation) prudential policy tightening action faces a substantially smaller spillover,

facing reductions in total and bank credit of around 5.6% (3.5%) and 4% (2.5%), respectively.

These results indicate that an additional (a one standard deviation) prudential policy tightening

can offset the monetary policy spillover by around 20% (50%)—an economically signiﬁcant

amount. Loan-to-value ratio limits and reserve requirements appear to be particularly effective

prudential measures at mitigating the spillover effects of US monetary policy.

Our analysis provides novel empirical evidence on the interactions between monetary and

prudential policy in a global context that complements existing analyses of the direct and indi-

rect effects of prudential policies on macro-ﬁnancial stability. Our main ﬁnding—that pruden-

tial policies can help to offset the spillover effects of US monetary policy, even when accounting

for capital controls—indicates that prudential policies may be an important and useful tool for

EM policymakers looking to gain monetary policy autonomy in the face of spillovers and the

global ﬁnancial cycle.

ECB Working Paper Series No 2339 / December 201921

Introduction

Prudential policies—both micro and macro in nature—have been widely used to address ﬁnan-

cial stability concerns since the 2007-09 global ﬁnancial crisis. Yet their effects are still debated.

On the one hand, they are seen to contain risks and contribute to macroeconomic stability

(Galati and Moessner, 2018); on the other, some have suggested they could harm macroeco-

nomic activity (S´anchez and R ¨ohn, 2016). We contribute to this debate from a novel angle,

assessing the extent to which domestic prudential policies interact with the spillovers from

foreign shocks.

This paper focuses speciﬁcally on prudential policies in emerging markets (EMs). EMs are

often disproportionately hit by spillovers from shocks emanating from advanced economies

(Bernanke, 2017) and their ‘spillbacks’ are a growing concern for developed countries (Car-

ney, 2019). The sensitivity of EMs to foreign shocks is, in part, related to the well-documented

‘global ﬁnancial cycle’ (Passari and Rey, 2015), characterised by a high degree of cross-border

co-movement in capital ﬂows, asset prices and credit growth in the world economy. The inﬂu-

ence of US monetary policy on the global cycle (Miranda-Agrippino and Rey, 2019), alongside

the dominant role of the US dollar in global trade and ﬁnancial markets (Boz, Casas, D´ıez,

Gopinath, Gourinchas, and Plagborg-Møller, 2019; Maggiori, Neiman, and Schreger, 2018), en-

sures that US monetary policy is a timeless concern amongst policymakers in EMs.

In the face of the global ﬁnancial cycle, Rey (2015) argues that policymakers face a dilemma:

domestic policymakers can pursue independent monetary policy if they have recourse to capi-

tal controls or prudential policies. In this paper, we assess whether domestic prudential policies

are an effective tool for helping to offset the spillover effects of US monetary policy and, in turn,

dampen the cyclical macro-ﬁnancial ﬂuctuations associated with it. In doing so, our analysis

provides novel empirical evidence on the interactions between monetary and prudential pol-

icy in a global context, that complements existing analyses of the direct and indirect effects of

direct prudential policies on macro-ﬁnancial stability.

Speciﬁcally, we ask three questions. First, to what extent do EM prudential policies offset

the spillover effects of US monetary policy? Second, which speciﬁc prudential policies are most

effective at doing so? Third, to what extent do other factors in an EM inﬂuence the size of the

interaction between domestic prudential policy and US monetary policy? Our questions are at

the heart of contemporary policy debates, contributing to a broader assessment of the optimal

ECB Working Paper Series No 2339 / December 20193policy mix for EMs facing external pressures. For instance, while Blanchard (2017) suggests that

EMs are equipped with policy instruments to deal with foreign shocks, Rajan (2015) states that

“macroprudential policies have little traction” against cross-border capital ﬂows. Our paper

contributes to this debate from a novel, but speciﬁc, angle, focusing exclusively on the extent

to which prudential policies help to shield EMs against foreign monetary policy shocks, and

accounts for other factors—such as capital controls—that could also reduce spillovers. While

beyond the scope of this paper, a complete assessment of the appropriate EM policy mix should

account for all the costs and beneﬁts these policies could have—for instance, their direct and

indirect effects on domestic real activity and ﬁnancial stability.

Using a panel dataset summarising prudential policy actions in EMs, we show that the

macro-ﬁnancial spillovers from US monetary policy shocks differ depending on the prudential

policies enacted by EMs. In particular, we ﬁnd that an EM with tighter prudential policies faces

signiﬁcantly smaller reductions in total credit and bank credit—common indicators of ﬁnancial

(in)stability—following a US monetary policy tightening shock. A +1pp exogenous tightening

of US monetary policy leads to around 7% fall in total credit and 5% fall in bank credit in EMs

on average, after around 15 months. But an EM with an additional (a one standard deviation)

prudential policy tightening action faces a substantially smaller spillover, facing reductions in

total and bank credit of around 5.6% (3.5%) and 4% (2.5%), respectively. These results indicate

that an additional (a one standard deviation) prudential policy tightening can offset the mon-

etary policy spillover by around 20% (50%)—an economically signiﬁcant amount—implying

that national prudential policies help to offset some of the spillovers of US monetary policy by

partially insulating an EM’s ﬁnancial sector against global ﬁnancial market moves and limiting

the cyclicality of credit growth and leverage.

Our empirical study builds on a long-standing literature quantifying the spillover effects

of US monetary policy to EM economies. We extend a local projection-based empirical setup

for monetary policy spillovers to study the interactions of US monetary policy with prudential

policy in EMs. To attain unbiased estimates of coefﬁcients of interest, we identify exogenous

US monetary policy shocks via external instruments (G ¨urkaynak, Sack, and Swanson, 2005;

Gertler and Karadi, 2015). To measure prudential policies, we use data spanning 64 coun-

tries, including 29 EMs, from 2000:Q1 to 2017:Q4 summarising prudential policy actions from

Cerutti, Correa, Fiorentino, and Segalla (2017b). The dataset covers changes in several widely

used prudential tools, with both micro- and macro-prudential objectives, speciﬁcally: capi-

ECB Working Paper Series No 2339 / December 20194tal buffers, loan-to-value (LTV) ratio limits, reserve requirements, interbank exposure limits,

and concentration limits. Although the dataset captures prudential policy actions within a

given quarter, we cumulate actions—over a two-year period in our baseline speciﬁcation, and

a one-year period in robustness analysis—to proxy the prudential policy actions relevant for

monetary policy spillovers, accounting for implementation lags, activation lags, transmission

lags and the persistence of policies. Importantly, our estimates for the interaction between US

monetary policy spillovers and EM prudential policies are robust to the inclusion of potential

competing explanations for the heterogeneous transmission of US monetary policy to EMs—

including their degree of capital controls, exchange rate regime, housing market structure and

measures of underlying country vulnerabilities.

Using the granularity of the prudential policy dataset, we ﬁnd that LTV ratio limits and

reserve requirements are particularly effective at partially mitigating the spillover effects of

US monetary policy shocks, thereby dampening a country’s exposure to cyclical ﬂuctuations

driven by US monetary policy. LTV ratio limits most strongly help to offset the response of EM

house prices to US monetary policy shocks, reﬂecting their application to real estate transac-

tions. Reserve requirements have a broad-based effect on credit.

Our analyses of the economic factors inﬂuencing the size of the policy interaction indicates

regional differences. Latin American economies—which are both close to the US geographi-

cally and are highly dollarised—have prudential policies that most strongly offset the spillovers

from US monetary policy, although regional differences are not statistically signiﬁcant. We also

ﬁnd that prudential policies—especially LTV ratio limits—are particularly effective at damp-

ening cyclical ﬂuctuations in EMs with higher home ownership shares, while differences in the

interaction appear more limited across ﬁxed and ﬂoating exchange rate regimes.

The remainder of the paper is structured as follows. Following a short literature review,

Section 2 describes the empirical speciﬁcation and data. Section 3 brieﬂy presents evidence

on US monetary policy spillovers to EMs, placing our main results on policy interactions into

context. Section 4 presents estimates of the interaction between EM prudential policies and US

monetary policy. Section 5 discusses the economic determinants of the interaction, assessing

the drivers of heterogeneity in policy interactions across countries. Section 6 concludes.

Related Literature There is a large literature studying the spillover effects of advanced econ-

omy shocks to EMs, much of which has focused on the effects of US monetary policy from

ECB Working Paper Series No 2339 / December 20195both empirical (e.g. Banerjee et al., 2016; Rey, 2016; Br¨auning and Ivashina, 2018; Iacoviello

and Navarro, 2018) and theoretical (e.g. Akinci and Queralto, 2018) standpoints. Alongside

this, a sizable literature has amassed studying the international spillover effects of prudential

policies (e.g. Aiyar, Calomiris, Hooley, Korniyenko, and Wieladek, 2014; Berrospide, Correa,

Goldberg, and Niepmann, 2017; Buch and Goldberg, 2017; Hills, Reinhardt, Sowerbutts, and

Wieladek, 2017) and their potential unintended consequences (e.g. Ahnert, Forbes, Friedrich,

and Reinhardt, 2018).

Our work contributes to a growing literature on interactions between monetary and pru-

dential policies. Unlike our paper, much of this literature has focused on within-country policy

interactions, with theoretical (e.g Angelini, Neri, and Panetta, 2014; Chen and Columba, 2016)

and empirical (e.g. Bruno, Shim, and Shin, 2017) contributions.

In the context of contemporary debates around the optimal mix policy tools in EMs, these

literatures complement our work in uncovering the numerous beneﬁts and costs of pruden-

tial policies. Our work focuses on a speciﬁc beneﬁt of prudential policy, namely helping to

shield EMs against foreign shocks, primarily contributing to a comparatively limited litera-

ture studying cross-border interactions of monetary and prudential policies. Using BIS data on

bilateral cross-border lending ﬂows, Takats and Temesvary (2019a) ﬁnd that macroprudential

measures applied in emerging market economies prior to the 2013 ‘Taper Tantrum’—linked

to US monetary policy—directly stabilised domestic ﬁnancial systems and make cross-border

bank lending ﬂows less volatile. Concurrent work by Hills, Lloyd, Reinhardt, and Sowerbutts

(2019) and Takats and Temesvary (2019b) studies the interaction of monetary and macropru-

dential policies via cross-border bank lending. Garcia-Lazaro, Ozkan, Puspitarasia, and Unsal

(2019) provide complementary theoretical analyses. In comparison to this existing literature,

the contribution of our paper is twofold. First, we focus on spillovers to macro-ﬁnancial vari-

ables. Second, we consider a broad range of US monetary policy shocks and not just the US

Taper Tantrum.

2 Empirical Speciﬁcation

We use the local projection methodology (Jord`a, 2005) to estimate the interaction of US mone-

tary policy shocks with EM prudential policies. For our purposes, there are two major advan-

tages to adopting a local projection setup relative to vector autoregressive methods. First, as

ECB Working Paper Series No 2339 / December 20196we demonstrate, a standard local projections spillover framework can be parsimoniously ex-

tended to account for prudential policy interactions. Second, compared to alternative empirical

speciﬁcations, the local projection setup is more robust to misspeciﬁcation, a pertinent concern

when studying the effect of various heterogeneous prudential policy instruments.

Monetary Policy Spillovers To provide context to our analysis of policy interactions, we ﬁrst

document some general features of US monetary policy spillovers to EMs. We model the im-
pact of a US monetary policy shock in quarter t, M P $

t , on the variable of interest yi,t+h in

country i at quarter t + h using the following local projection speciﬁcation:

yi,t+h − yi,t−1 = αh + βh

mpM P $

t + γh(cid:48)

Xi,t−1 + θh(cid:48)

Gt−1 + f h

i + εi,t+h

(1)

for h = 0, 1, ..., H. Xi,t−1 is a K × 1 vector of control variables known prior to the US monetary

policy shock, with associated coefﬁcients γh. Country ﬁxed effects f h

i capture potentially con-

founding factors that are speciﬁc to countries, but ﬁxed over time. Because the US monetary
policy shock M P $
t

is the same for all countries, we cannot include time ﬁxed effects in (1) as

these would absorb all variation in the explanatory variable of interest. As a consequence, we

account for variables summarising the global cycle in Gt−1, a J × 1 vector with associated co-

efﬁcients θh. Assuming the conditional mean can be linearly approximated, βh

mp measures the

average effect of a period-t US monetary policy shock on yi,t+h at t + h.

We explore a range of dependent variables, primary amongst which are aggregate mea-

sures of ﬁnancial stability in a country: total credit and bank credit. A large body of work has

highlighted a role for credit growth as a leading indicator of ﬁnancial crises (e.g. Schularick

and Taylor, 2012), motivating our focus on these variables. In addition, we use data on house

prices, as a number of prudential policies—such as LTV ratio limits—have been applied with a

focus on housing sector risks. We include two lags of output growth, inﬂation and the depen-

dent variable (quarterly changes) in the set of country-varying controls Xi,t−1 to capture the

prevailing macroeconomic state ahead of a US monetary policy innovation. The global con-

trols Gt−1 include two lags of US output growth, VIX and past US monetary policy shocks,

reﬂecting global economic and ﬁnancial conditions. Our macro-ﬁnancial dataset spans 29 EMs,

reﬂecting country coverage in the prudential policy actions dataset.1

1For reference, we also compare our monetary policy spillover ﬁndings to results from a panel of 35 advanced

economies, also covered in the prudential policy actions dataset.

ECB Working Paper Series No 2339 / December 20197Interactions of Spillovers with Receiving-Country Prudential Policy The monetary policy

spillover regression marks the point of departure for our empirical speciﬁcation. To analyse

how prudential policies in EMs interact with spillovers from US monetary policy shocks we

adapt (1) to account for country-i prudential policy P rui,t using the following setup:

yi,t+h − yi,t−1 =αh + δh (cid:16)

(cid:17)

+ βh

pruP rui,t−1 + γh(cid:48)

Xi,t−1

t × P rui,t−1

M P $
Γi,t−1 + ϑh(cid:48) (cid:16)

+ θh(cid:48)

M P $

t × Γi,t−1

(cid:17)

+ f h

i + f h

t + εi,t+h

(2)

where P rui,t−1 represents an indicator of prudential actions, taking positive values for a (net)

tightening and negative for a (net) loosening. In comparison to (1), we include time ﬁxed effects

f h
t to account for potentially confounding factors that are the same for all countries in a given

time period—for example, the state of the global ﬁnancial cycle. Because the time ﬁxed effects

f h
t account for all observed and unobserved global factors that vary over time, we exclude Gt−1
from (2).2

Our controls Xi,t−1, which vary by time and country, are the same as in (1). In robustness

analyses, we extend the controls to include other factors that could plausibly interact with

US monetary policy spillovers, aside from prudential policies, in order to rule out competing

hypotheses. To account for them, we deﬁne a Γi,t−1 as a (set of), possibly time-varying, country

characteristic(s) that could potentially inﬂuence the size of US monetary policy spillovers to

EMs—such as a country’s capital controls. We then include the interaction of this variable with
the contemporaneous US monetary policy shock, M P $

t × Γi,t−1, in addition to its lagged level

Γi,t−1 in our set of time- and country-varying controls Xi,t−1.3
t also absorb all variation in M P $

The time ﬁxed effects f h

t , explaining why this is not in

(2). Nevertheless, the sign of coefﬁcient estimates from (1) can help to interpret results from

(2).4 The coefﬁcient of interest in the latter, capturing policy interactions, is δh. If, for a given

2Because the time ﬁxed effects f h

t capture all observed and unobserved time-varying factors, (2) is our preferred
speciﬁcation for statistical inference. However, to illustrate the economic signiﬁcance of our ﬁndings and compare
mp and ˆδh—we also estimate a hybrid spec-
monetary policy spillovers with prudential policy interactions—i.e. ˆβh
iﬁcation of (1) and (2) that includes the monetary policy term M P $
t , its interaction with lagged prudential policy
M P $
t to avoid absorbing
variation in M P $
t .

t × P rui,t−1 and lagged observed global factors Gt−1, but excludes time ﬁxed effects f h

3In some cases, we classify the variable using an indicator variable. To do this, we deﬁne a (potentially) time-
varying indicator variable 1g,i,t−1 ≡ 1 [Γi,t−1 ∈ g] where 1 is an indicator function equal to 1 if the country charac-
teristic falls into a particular ‘bin’ of the distribution, a country’s group g = 1, ..., G.

4A direct quantitative comparison of coefﬁcients from (1) and (2) is not possible, because the former speciﬁcation

excludes time ﬁxed effects f h

t while the latter includes them.

ECB Working Paper Series No 2339 / December 20198dependent variable yi,t+h, monetary policy spillovers are negative ˆβh
mp < 0, then a positive
interaction coefﬁcient ˆδh > 0 implies that tighter prudential policy helps to offset some of the

negative spillover effects of a US monetary policy tightening. In contrast, if the interaction
coefﬁcient is negative ˆδh < 0, tighter prudential policy does not mitigate the negative spillover
effects of tighter US monetary policy.5 The sequence {ˆδh}H

h=0 can thus be interpreted as the

average interactions associated with a US monetary policy impulse at time t.

Importantly, we include prudential policy with a lag in (2) to avoid accounting for policy

actions that could possibly occur in response to a US monetary policy shock, or simultaneity

of economic conditions and domestic prudential policy. By using lagged prudential policy,

we explicitly assess whether prudential policy actions, in advance of a US monetary policy

innovation, can help to offset some of the spillovers effects of the centre country monetary

policy driving the global ﬁnancial cycle.

Assessing the Determinants of Interactions Regression (2) captures the average interaction

between US monetary policy and EM prudential policy, but suppresses potential cross-country
i = δh for all i. To account for this in an eco-

heterogeneity in the interaction by imposing δh

nomically meaningful way, we estimate an extended local projection regression. To do so, we

deﬁne Zi,t−1 as a (set of) country characteristic(s) that could potentially inﬂuence the size of the

interaction, such as the exchange rate regime or home ownership share in a country. We deﬁne
a (potentially) time-varying indicator variable 1g,i,t−1 ≡ 1 [Zi,t−1 ∈ g] where 1 is an indicator

function taking a value of 1 if the country characteristic falls into a particular ‘bin’ of the distri-

bution, which we denote as a country’s group g = 1, ..., G. The general form for this extended

speciﬁcation is:

yi,t+h − yi,t−1 =

G
(cid:88)

g=1

αh
g · 1g,i,t−1 +

(cid:16)

δh
g

G
(cid:88)

g=1

M P $

t × P rui,t−1 × 1g,i,t−1

(cid:17)

G
(cid:88)

+

(cid:16)

˜βh
mp,g

M P $

t × 1g,t−1

(cid:17)

+

G
(cid:88)

g=1

βh
pru,g (P rui,t−1 × 1g,i,t−1)

g=1
+ γh(cid:48)

Xi,t−1 + f h

i + f h

t + εi,t+h

(3)

5The reverse is true if the US monetary policy spillover is positive ˆβh

mp > 0. Then, a negative interaction coefﬁ-

cient ˆδh < 0 reﬂects an offsetting policy interaction, and a positive coefﬁcient ˆδh > 0 a non-offsetting one.

ECB Working Paper Series No 2339 / December 20199By accounting for country characteristics in this way, we estimate separate interaction coef-

ﬁcients δh

g for each group g = 1, ..., G, non-parametrically capturing potential cross-country

heterogeneity in policy interactions.6

Importantly, (3) includes a term for the interaction of the monetary policy shock with the

indicator variable

(cid:16)

M P $

t × 1g,i,t−1

(cid:17)

, with associated coefﬁcient ˜βh

mp,g, reﬂecting the fact that

the product of the monetary policy innovation and the indicator variable can vary along both

country and time-dimensions, so is not fully absorbed by time ﬁxed effects.

In all regressions, we use Driscoll and Kraay (1998) standard errors to account for potential

cross-sectional and temporal dependence in inference, and impulse responses are reported out

to a two-year horizon—i.e. H = 8.

2.1 Prudential Policy Data

We use the prudential policy actions dataset of Cerutti et al. (2017b), constructed for the cross-

country International Banking Research Network (IBRN) project on cross-border spillovers of

prudential policy (Buch and Goldberg, 2017). The dataset spans 64 countries, including the

same 29 EMs in our panel of macro-ﬁnancial data.7 The prudential policy data is quarterly, from

2000:Q1 to 2017:Q4,8 and covers ﬁve types of prudential policy instruments with both micro-

and macro-prudential objectives: capital requirements; interbank exposure limits; concentra-

tion limits; LTV ratio limits; and reserve requirements. The dataset further breaks down some

of these categories, differentiating general capital requirements—which predominantly reﬂect

convergence to Basel Accords—from sectoral capital buffers—such as risk weights on speciﬁc

bank exposures—as well as the currency breakdown of reserve requirements. The availability

of prudential policy data deﬁnes the beginning of our sample period. With forward lags in our

local projection setup, we use macro-ﬁnancial data up to 2018:Q2 on the left-hand side of our

regressions.

The Cerutti et al. (2017b) dataset has been constructed from a range of sources, and obser-

vations in the database were reviewed by staff from national central banks.9 The raw dataset

6Because 1g,i,t−1 is time and country-varying, the product of the indicator with monetary and prudential pol-
t remains well-deﬁned in this

icy measures also varies across time and countries, ensuring the time ﬁxed effect f h
speciﬁcation.

7The 29 EMs in our dataset span 11 Asian economies, 8 in Europe, 7 in Latin America and 3 in Africa.
8The Cerutti et al. (2017b) dataset has recently been extended by its authors, from a 2014:Q4 end date to 2017:Q4.
9The database builds on existing information in Cerutti, Claessens, and Laeven (2017a) which covers a smaller
set of macroprudential policy instruments in 125 countries, as well as secondary sources compiled by Lim et al.
(2011), Akinci and Olmstead-Rumsey (2015), and Reinhardt and Sowerbutts (2015).

ECB Working Paper Series No 2339 / December 201910measures changes in prudential policy instruments within a quarter, assigning a value of +1

to a given prudential policy if it was tightened in a speciﬁc quarter, a value of −1 if it was

loosened, and 0 if no change occurred. For some policy instruments, information about the

intensity of the policy change is retained. For example, for sectoral capital requirements and

reserve requirements, indices ranging from −3 to 5 reﬂect the intensity of policy changes.

To suit our study, we manipulate the raw dataset in the following ways. First, we sum

prudential policy actions over a number of quarters, reﬂecting the fact that changes in pruden-

tial polices in a single quarter are unlikely to solely inﬂuence the spillovers from US monetary

policy shocks accounting for potential implementation lags, activation lags, transmission lags,

and the persistence and level of prudential policies. In our baseline formulation we sum ac-

tions over two years such that the prudential policy measure at time t − 1, P rui,t−1, includes

information on all prudential policy changes from t − 8 to t − 1, inclusive. The choice of a

two-year summation period in our baseline speciﬁcation balances a compromise. On the one

hand, we need a long enough summation period to capture sufﬁcient variation in prudential

policy measures over time, as well as proxy aspects of cross-country differences in their level.

On the other hand, we need to ensure that the summation period is not too long such that it

suppresses variation in the prudential measure because of policy reversals over time. Impor-

tantly, we assess the robustness of our results to different cumulation periods. We report the

results of this analysis in Appendix B.4, where we show that our main results are quantitatively

robust to a shorter one-year summation period for prudential policy. This indicates that even

prudential policies announced just before a foreign shock can help to lessen the spillovers it

generates for EMs, implying that activating prudential measures is never too late to help offset

potential spillovers from center countries.

Second, we construct measures of aggregate prudential policy, by summing cumulated mea-

sures of different instruments. Our baseline measure of aggregate prudential policy actions

includes all prudential policy instruments in the Cerutti et al. (2017b) dataset, with the excep-

tion of general capital requirements in line with Takats and Temesvary (2019a). Like Takats

and Temesvary (2019a), we primarily exclude general capital requirements because they reﬂect

microprudential policy adjustment, such that the remaining instruments in the proxy more

closely match macroprudential measures.10 In addition, Takats and Temesvary (2019a) men-

10We show that our headline results are robust to the inclusion of general capital requirements in the aggregate

prudential policy measure in Appendix B.3.

ECB Working Paper Series No 2339 / December 201911Table 1: Summary statistics for prudential policy proxies constructed by cumulating actions
over a two-year period

Prudential Policy Measure
Aggregate Proxy
Speciﬁc Prudential Instruments
Reserve Requirements
LTV Ratio Limits
Sectoral Capital Buffers
Concentration Ratio Limits
Interbank Exposure Limits

# Obs.
1885

P rui,t
0.255

1885
639
1885
1272
611

0.048
0.078
0.100
0.078
0.083

σ (P rui,t) min (P rui,t) max (P rui,t)

2.517

2.179
0.834
0.675
0.371
0.283

−9

−9
−3
−3
−1
−1

11

11
5
4
2
1

Note: Statistics constructed by pooling observations across the 29 EMs and over full sample period.

tion that these general capital requirements largely reﬂect the adoption of the Basel III regime,

an internationally harmonised and broadly anticipated move resulting in limited cross-country

variation in the speciﬁc series11, mostly capturing differences in prudential regulation.

Our aggregate prudential policy measure includes reserve requirements, including those

levied on both domestic and foreign currency-denominated deposits. Although reserve re-

quirements have been used as instruments to conduct monetary policy in some jurisdictions,

Cordella, Federico, Vegh, and Vuletin (2014) note that these policies have predominantly re-

cently been used as countercyclical macroprudential tools in EMs. Cerutti et al. (2017b) account

for this in the construction of their dataset, ensuring that the reserve requirements they capture

are used to satisfy prudential objectives within a country, warranting their inclusion in our

aggregate prudential policy proxy.

We also present analyses using cumulated measures of speciﬁc prudential policies, to isolate

their differential impacts across dependent variables.

Table 1 presents summary statistics for our two-year cumulated prudential policy proxies,

constructed by pooling observations across the 29 EMs in our dataset and over the full sam-

ple.12 Over the sample, all policy proxies—aggregate and speciﬁc—were, on average and on

net, tightened. Nevertheless, all measures take a range of positive and negative values, with

the aggregate prudential policy measure varying from −9 to 11 with a standard deviation of

around 2.5. In our sample of 29 EMs, reserve requirements and, to a lesser extent, LTV ratio

limits are the most actively used measures, with the widest range and standard deviation.

11Our aggregated measure still includes sectoral capital requirements, for instance those levied on real estate or

consumer credit.

12See Appendix A.2 for additional details and summary statistics on the prudential policy dataset.

ECB Working Paper Series No 2339 / December 2019122.2 Monetary Policy Shocks

A key concern for our analysis is that our measure of US monetary policy M P $
t

is exogenous

in order to attain unbiased estimates of the parameters of interest. This prevents us from us-

ing raw measures of US interest (or shadow) rates to capture changes in US monetary policy.

Of particular concern in our setting are potentially omitted factors, such as a global ﬁnan-

cial moves, that could simultaneously affect the US monetary policy stance as well as macro-

ﬁnancial outcomes in EMs, especially if they have heterogeneous effects across EMs.

Drawing on an extensive literature, we identify monetary policy shocks with the widely

used external instruments VAR approach of Mertens and Ravn (2013) and Stock and Watson

(2018), applied to US monetary policy by Gertler and Karadi (2015). Relative to Gertler and

Karadi (2015), we make one change to our VAR speciﬁcation: estimating it with data up to

the end of 2018 (instead of 2012).13 Like Gertler and Karadi (2015), our VAR consists of four

monthly frequency US variables: industrial production, the consumer price index, the 1-year

zero-coupon government bond yield, and the excess bond premium (Gilchrist and Zakrajsek,

2012). We estimate the model with 12 lags of monthly variables, using monthly data from 1979

to 2018. We construct quarterly monetary policy shocks from the monthly VAR, by cumulat-

ing monthly shocks within the quarter. To identify a monetary policy shock, we use high-

frequency monetary policy surprise measures from G ¨urkaynak et al. (2005)—changes in mon-

etary policy expectations in a short time window (30 minutes) around Federal Open Market

Committee (FOMC) announcements—as instruments for the reduced-form monetary policy

innovation. The key identifying assumption is that no other potentially confounding events,

which could simultaneously drive private sector behavior and the monetary policy decision,

can occur within the short time window around the FOMC announcements. Despite the sam-

ple extension, our instrument—changes in the three-month-ahead federal funds futures rate

in 30-minute windows around FOMC announcements—continues to pass tests for instrument

validity, with a ﬁrst-stage F -statistic in excess of 10.

3 Monetary Policy Spillovers

To contextualise our estimates of prudential policy interactions, we ﬁrst estimate the spillover

effects of US monetary policy to EMs using (1). Figure 1 presents estimates of the spillover

13Additional detail on our monetary policy shocks is provided in Appendix A.3.

ECB Working Paper Series No 2339 / December 201913coefﬁcient for (log) total credit and (log) bank credit in EMs. For comparison, we also plot

the impulse response of the two variables to the US monetary policy shock in a group of 34

advanced economies, which exclude the US. The coefﬁcient estimates can be interpreted as

average impulse responses to a +1pp US monetary policy tightening shock in the two groups.

Two observations are noteworthy. First, in line with a large body of literature, a US mone-

tary policy tightening is associated with a ﬁnancial tightening abroad. Total credit falls signif-

icantly in EMs, and bank credit falls signiﬁcantly in both advanced and emerging economies,

within two years of a US monetary policy shock. Second, EMs are disproportionately neg-

atively affected by a US monetary policy tightening, in comparison to advanced economies.

Total credit in EMs falls by signiﬁcantly more than in advanced economies. As well as being

statistically signiﬁcant, the difference is economically signiﬁcant: the average fall in EM bank

credit after a +1pp US monetary policy tightening shock is substantially larger than the corre-

sponding average impact for advanced economies.

Figure 2 plots spillover coefﬁcient estimates for (log) house prices. The point estimate for

spillovers to EMs is negative at all horizons. A US monetary policy tightening is associated, on

average, with a reduction in house prices, albeit smaller than the responses of total credit and

bank credit. Although the point estimates are statistically insigniﬁcant, the wide conﬁdence

bands, in part, reﬂect heterogeneity across emerging markets. The heterogeneity in spillovers

Figure 1: US monetary policy spillovers to total credit and bank credit in emerging markets
and advanced economies

mp}8

Notes: {βh
h=0 estimates with (log) total credit (left-hand side) and (log) bank credit (right-hand side) for 29
emerging markets (‘EM’; black) and 34 advanced economies (‘AE’; excluding US, red) as dependent variable
(regression (1)). The classiﬁcation of advanced and emerging economies is from the IMF’s World Economic
Outlook. The gray shaded area and the red-dashed lines denotes 90% conﬁdence intervals around point estimates
for emerging and advanced economies, respectively, constructed from Driscoll and Kraay (1998) standard errors.

-15.00-10.00-5.000.005.00Percent02468QuarterEMAE-10.00-5.000.005.00Percent02468QuarterEMAEECB Working Paper Series No 2339 / December 201914to emerging markets is well known. Iacoviello and Navarro (2018), for example, assess how

the effects of higher US interest rates on EMs differ depending on an economy’s exchange rate,

trade openness and vulnerability. In this paper, we assess a policy-relevant dimension of US

monetary policy spillover variation in EMs: prudential policy.

Figure 2: US monetary policy spillovers to house prices in emerging markets and advanced
economies

mp}8

Notes: {βh
h=0 estimates with (log) house prices for 29 emerging markets (‘EM’; black) and 34 advanced
economies (‘AE’; excluding US, red) as dependent variable (regression (1)). The classiﬁcation of advanced and
emerging economies is from the IMF’s World Economic Outlook. The gray shaded area and the red-dashed lines
denotes 90% conﬁdence intervals around point estimates for emerging and advanced economies, respectively, con-
structed from Driscoll and Kraay (1998) standard errors.

4 Prudential Policy Interactions

To assess how the prudential policies in EMs interact with the spillovers from US monetary

policy, we estimate (2) using different prudential policy measures P rui,t−1.

4.1 Aggregate Prudential Policy

We ﬁrst study the interaction of US monetary policy with our measure aggregate EM prudential

policy, including all prudential policy indicators in the Cerutti et al. (2017b) dataset with the

exception of general capital requirements.

Figure 3 presents estimates of the sequence of interaction coefﬁcients {ˆδh}8

h=0 from (2) with
EM (log) total credit and (log) bank credit as dependent variables.14 The plots show that the

14For house prices, the interaction coefﬁcient with respect to the aggregate prudential policy proxy is insigniﬁ-

-4.00-2.000.002.004.00Percent02468QuarterEMAEECB Working Paper Series No 2339 / December 201915Figure 3: Interaction of US monetary policy spillovers with aggregate prudential policy mea-
sures in recipient emerging markets for total credit and bank credit

Notes: {δh}8
h=0 estimates with (log) total credit (left-hand side) and (log) bank credit (right-hand side) for 29
emerging markets as dependent variable (regression (2)). The light blue shaded area denotes the 90% conﬁdence
interval around point estimates, constructed from Driscoll and Kraay (1998) standard errors. The aggregate
prudential policy measure is deﬁned as the two-year cumulated sum of all prudential policy actions, excluding
aggregate capital requirements, in the Cerutti et al. (2017b) dataset.

interaction coefﬁcient is signiﬁcantly positive around the 12 to 21-month (h = 4 to 7) horizon

when total credit is the dependent variable. The point estimates of the coefﬁcient for bank credit

are positive at all horizons too, signiﬁcantly so at h = 4. The coefﬁcients have the following

interpretation: controlling for time ﬁxed effects, an additional prudential policy tightening in

an EM in advance of a +1pp US monetary policy tightening can, on average, reduce the total

credit hit from the US monetary policy tightening by around 1.4pp over a 15-month horizon

(h = 5).

Using our proxies for aggregate prudential policy, we do not uncover signiﬁcantly positive

interaction coefﬁcients for house prices, CPI and real GDP in EMs. However, as we discuss in

the next sub-section, we do ﬁnd that the interaction effects of speciﬁc prudential policy instru-

ments can signiﬁcantly feed through to macro-ﬁnancial outcomes—house prices in particular.

Nevertheless, our ﬁndings for aggregate prudential policy alone have important policy im-

plications for EMs. In the face of spillovers from US monetary policy and the associated global

ﬁnancial cycle, our results indicate that EMs can rely on prudential policies to signiﬁcantly re-

duce the extent to which US monetary policy drives cyclical ﬂuctuations in credit conditions.

Following an unexpected US monetary policy tightening, EMs with tighter prudential policy

face smaller falls in total lending, consistent with their ﬁnancial sector being better placed to

cantly different from zero at all horizons.

-1.000.001.002.003.00pp02468Quarter-0.500.000.501.001.502.00pp02468QuarterECB Working Paper Series No 2339 / December 201916absorb the adverse spillovers tighter US monetary policy due to domestic prudential policies.

Competing Hypotheses To assess the robustness of our headline ﬁndings, we extend (2)

to account for other factors Γi,t−1 that could possibly interact with the US monetary policy

spillovers to EMs that are distinct from prudential policy. Table 2 documents the results of ro-

bustness analyses, presenting estimates of the interaction coefﬁcient δh for spillovers to total
credit when additional interactions M P $
t × Γi,t−1 (and Γi,t−1 alone) are included in the set of
controls Xi,t−1. For reference, column (1) reports the ˆδh estimates from the baseline speciﬁca-

tion, plotted in ﬁgure 3 (left-hand side).

In columns (2) and (3), we extend the regression speciﬁcation to include interactions be-

tween the US monetary policy shock and countries’ lagged capital ﬂow restrictiveness—i.e.

M P $

t ×KCi,t−1 and KCi,t−1 are included in the set of time and country-varying controls Xi,t−1,

where KCi,t−1 is a measure of capital controls in country i at time t − 1. This is an impor-

tant robustness test because, like prudential policies, capital controls may help to mitigate the

spillovers from foreign shocks by limiting cross-border ﬂows. By accounting for this interaction

independently, we assuage worries that our results could simply reﬂect the effects of, poten-

tially correlated, capital ﬂow restrictions. To measure capital ﬂows in our set of EMs, we use

the index of Fern´andez, Klein, Rebucci, Schindler, and Uribe (2016).15 In column (2), we use a

measure of overall capital controls, spanning restrictions on inﬂows and outﬂows of a range of

asset categories. In column (3), our measure of capital controls is focused on inﬂow restrictions,

again spanning a range of asset categories.

Importantly, although the inclusion of an additional capital control interaction reduces the
absolute size of our ˆδh estimates at all horizons relative to the baseline in column (1), the in-

teraction coefﬁcient estimates remain statistically signiﬁcant at (at least) the 5-quarter horizon.

The coefﬁcient estimates in columns (2) and (3) indicate that, once controlling for capital ﬂow

restrictiveness, an additional tightening of prudential policy in an EM in advance of a +1pp

US monetary policy tightening can, on average, reduce the hit to total credit from the shock by

around 1pp after one year.16

In columns (4)-(6), we add alternative interaction time and country-varying variables. In

column (4), we additionally allow US monetary policy shocks to interact with the level of

15This index is measured at an annual frequency. We translate to quarterly frequency by assuming the index

value for the calendar year is maintained in each quarter.

16We present the capital ﬂow interaction coefﬁcients—i.e. the coefﬁcients on M P $

t × KCi,t−1—in Appendix B.2.

ECB Working Paper Series No 2339 / December 201917Table 2: Interaction coefﬁcient estimates ˆδh from regression (2) for total credit using aggregate
prudential policy measures, which exclude aggregate capital requirements, in recipient emerg-
ing markets

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Additional Interaction Variables

Baseline

Capital
Control

Capital
Inﬂow
Control

Credit-
to-GDP
Growth

FX
Regime

M P $

t × P rui,t−1

h = 0

h = 1

h = 2

h = 3

h = 4

h = 5

h = 6

h = 7

h = 8

0.16
(0.16)
0.24
(0.28)
0.34
(0.40)
0.65
(0.43)
0.99*
(0.53)
1.35**
(0.57)
1.14*
(0.60)
1.20*
(0.68)
0.86
(0.78)

0.09
(0.17)
0.07
(0.27)
0.10
(0.37)
0.42
(0.39)
0.74
(0.51)
1.02**
(0.49)
0.86*
(0.51)
0.90
(0.57)
0.57
(0.73)

0.10
(0.16)
0.11
(0.27)
0.15
(0.37)
0.45
(0.39)
0.76
(0.50)
1.06**
(0.52)
0.90
(0.55)
0.95
(0.62)
0.59
(0.75)

0.11
(0.16)
0.12
(0.29)
0.18
(0.40)
0.45
(0.38)
0.79*
(0.47)
1.12**
(0.54)
0.90
(0.58)
0.92
(0.65)
0.56
(0.73)

0.10
(0.14)
0.25
(0.25)
0.38
(0.35)
0.79**
(0.39)
1.09**
(0.50)
1.52***
(0.52)
1.27**
(0.54)
1.37**
(0.63)
0.91
(0.73)

Home
Own.
Share

0.17
(0.15)
0.26
(0.28)
0.37
(0.39)
0.67
(0.41)
0.98*
(0.52)
1.36**
(0.56)
1.14*
(0.59)
1.22*
(0.67)
0.84
(0.77)

Country
FE

0.12
(0.10)
0.31
(0.23)
0.40
(0.31)
0.60
(0.36)
0.81*
(0.44)
1.47***
(0.52)
1.32**
(0.61)
1.68**
(0.73)
0.89
(0.85)

YES
YES
NO

YES
Country FE
YES
Time FE
Country FE ×M P $
NO
t
Notes: ˆδh, for h = 1, ..., 8, coefﬁcient estimates from various speciﬁcations of (2) designed to account for other
potential interactors with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically signiﬁcant coefﬁcient
estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay (1998) standard errors
(reported in parentheses).

YES
YES
NO

YES
YES
NO

YES
YES
NO

YES
YES
YES

YES
YES
NO

lagged credit-to-GDP growth in country i. Column (5) includes an interaction between US

monetary policy and the de facto exchange rate regime of the emerging market (Ilzetzki, Rein-

hart, and Rogoff, 2019). By accounting for this, the peak prudential policy interaction coefﬁcient

increases in size and signiﬁcance, indicating that the exchange rate regime, indicating that the

exchange rate regime is an important dimension of heterogeneity in monetary policy spillovers.

Column (6) accounts for the role of home ownership in the interaction, deﬁned as an indicator

variable equal to 1 if a country’s home ownership share exceeds the cross-country median of

70%, and 0 otherwise.17 In all cases, the alternative interaction term reﬂects a dimension of

17Home ownership share data is from HOFINET and measures average home ownership rates in each country

ECB Working Paper Series No 2339 / December 201918a country’s vulnerability to foreign shocks, distinct from prudential policy and the continued
positive and statistically signiﬁcant coefﬁcient, on M P $

t × P rui,t−1, at the four and ﬁve-quarter

horizons at least, validates our main result.

In column (7), we further extend the regression speciﬁcation to include interactions be-

tween the US monetary policy shock and the country ﬁxed effects—i.e. M P $

t × f h
i

is included

in the set of time and country-varying controls Xi,t−1. This extension accounts for the possibil-

ity that country-speciﬁc, non-time-varying, factors could also interact with spillovers from US

monetary policy aside from prudential policies. These factors are likely to capture persistent

country-speciﬁc vulnerabilities, including structural imbalances, debt levels and institutional

features. The fact the interaction coefﬁcient estimate remains positive and statistically signiﬁ-

cant across these various robustness interactions, supports our main conclusion.

Economic Signiﬁcance Direct comparison of coefﬁcient estimates from (1) and (2) is not pos-

sible as the latter includes time ﬁxed effects f h

t , while the former does not. To illustrate the

economic signiﬁcance of our headline results, we estimate a hybrid version of (2) that includes
the monetary policy variable M P $

t as an additional explanatory variable and, in order to do so,

omits time ﬁxed effects f h

t . To replace the ﬁxed effects, we add observed time-varying global

control variables Gt−1 to the regression speciﬁcation. This enables concurrent estimation of the

direct average spillover effect of US monetary policy to EMs βh

mp and the interaction coefﬁcient
with domestic prudential policy δh. Because we omit time ﬁxed effects, we do not rely on this

hybrid speciﬁcation for inference, instead we use it to compare the two coefﬁcients to gauge

the economic signiﬁcance of our ﬁndings.

Figure 4 illustrates how the estimated spillover from a +1pp US monetary policy tightening

varies depending on the lagged aggregate prudential policy actions carried out in an EM. Here

the prudential policy measure sums all two-year cumulated actions in the Cerutti et al. (2017b)

dataset. The blue line plots the estimated spillover to an EM with zero net prudential policy

actions, P rui,t−1 = 0, indicating that a +1pp exogenous tightening of US monetary policy

leads to around a 7% fall in total credit and 5% fall in bank credit in such EMs after around

12 to 18 months. An EM with an additional prudential policy tightening action, P rui,t−1 = 1,

is estimated to face a substantially smaller spillover. The peak spillover of a US monetary

policy tightening shock in this EM to total credit is around 5.6% and to bank credit around

over the 2005-2014 period.

ECB Working Paper Series No 2339 / December 201919Figure 4: US monetary policy spillovers to total credit and bank credit for different levels of
aggregate prudential policy in recipient emerging markets

mp + δh}8

Notes: {βh
h=0 estimates with (log) total credit (left-hand side) and (log) bank credit (right-hand side) for
29 emerging markets as dependent variable in hybrid version of regression (2) that excludes time ﬁxed effects
f h
t , but includes US monetary policy measure M P $
t and lagged global controls Gt−1. The aggregate prudential
policy measure is deﬁned as the two-year cumulated sum of all prudential policy actions, excluding aggregate
capital requirements, in the Cerutti et al. (2017b) dataset. The blue line denotes estimated spillover from a 1pp
US monetary policy tightening shock to an EM with a 0 value for prudential policy. The green line denotes the
comparable spillover estimate for an EM with a +1 prudential policy action—i.e. on net, one additional policy
tightening. The red line denotes the opposite spillover, for an EM with a −1 prudential policy action.

4%, indicating that an additional prudential policy tightening can offset the monetary policy

spillover by around 20%.

In the context of a the summary statistics presented in table 1, this ﬁgure can be scaled by

the standard deviation of the aggregate prudential policy proxy (2.5). In this case, the peak

spillover of a US monetary policy tightening shock to an illustrative EM with a prudential

policy setting one standard deviation above the mean would be around 3.5% and 2.5% to total

and bank credit, respectively. A one standard deviation tightening is then associated with a

spillover reduction of around 50%.

4.2 Speciﬁc Prudential Policy Instruments

In this sub-section, we explore the interaction of US monetary policy with speciﬁc prudential

policies in EMs. Within the Cerutti et al. (2017b) dataset, we are able to investigate interac-

tions with ﬁve categories of prudential policies: (i) LTV ratio limits, (ii) reserve requirements,

(iii) (sectoral) capital buffers, (iv) interbank exposure limits, and (v) concentration ratio lim-

its. This classiﬁcation is particularly interesting in light of the distinction between prudential

policy instruments that ‘dampen the cycle’—(i) and (ii), in particular—and those that ‘increase

-8-6-4-20Percent02468QuarterNo Pru+1 Pru-1 PruTotal Credit: 2-year Cumulative Macroprudential Measures-6-4-20Percent02468QuarterNo Pru+1 Pru-1 PruBank Credit: 2-year Cumulative Macroprudential MeasuresECB Working Paper Series No 2339 / December 201920resilience’—(iii)-(v)—laid out in Borio (2010) and Claessens et al. (2013).

Within our framework we ﬁnd that, consistent with the view that US monetary policy is

a driver of global cyclical ﬂuctuations, LTV ratio limits and reserve requirements signiﬁcantly

interact with US monetary policy spillovers, while we do not ﬁnd evidence of a signiﬁcant

interaction for sectoral capital requirements, interbank exposure limits and concentration ratio

caps—the latter two of which were used to a limited extent by EMs in our sample. Our results

provide a novel test of this classiﬁcation, externally validating the separation of instruments

that predominantly dampen the cycle, versus those that increase resilience.

4.2.1 Loan-to-Value Ratio Limits

LTV ratio limits restrict the maximum amount an individual or ﬁrm can borrow against their

collateral. These restrictions are most commonly applied to real estate transactions and the

Cerutti et al. (2017b) dataset focuses on this aspect. As a consequence, we assess the interaction

of LTV ratio limits with US monetary policy spillovers with the hypothesis that the policy

should signiﬁcantly reduce cyclical ﬂuctuations in house prices.

Using the two-year cumulated LTV ratio limit indices as the prudential policy measure

in regression 2, ﬁgure 5 presents the interaction coefﬁcients for house prices and total credit.18

The results for house prices, in the left-hand side of the ﬁgure, accord with our hypothesis. LTV

ratio limits are associated with smaller cyclical ﬂuctuations in house prices in EMs following

innovations to US monetary policy.

However, the interaction effect for total credit (right-hand side), and also bank credit, is

insigniﬁcantly different from zero at all horizons. This result is perhaps not surprising: in a

market, like the housing market, with an inelastic short-run supply curve, the majority of ad-

justment in response to cyclical ﬂuctuations must occur in prices and not quantities. Studying

the direct effects of LTV ratio limits, Akinci and Olmstead-Rumsey (2015) and Richter, Schu-

larick, and Shim (2018) ﬁnd that changes in LTV limits do not have signiﬁcant effects on total

credit quantities.

18See Appendix B.5 for additional robustness analyses.

ECB Working Paper Series No 2339 / December 201921Figure 5: Interaction of US monetary policy spillovers with loan-to-value ratio limits in recipi-
ent emerging markets for house prices and total credit

mp}8

Notes: {δh
h=0 estimates with (log) house prices (left-hand side) and (log) total credit (right-hand side) for 29
emerging markets as dependent variable (regression (2)). The light blue shaded area denotes the 90% conﬁdence
interval around point estimates, constructed from Driscoll and Kraay (1998) standard errors. The prudential policy
measure is deﬁned as the two-year cumulated sum of loan-to-value ratio limits in the Cerutti et al. (2017b) dataset.

4.2.2 Reserve Requirements

Within the Cerutti et al. (2017b) dataset, reserve requirements encompass all changes imposed

on deposit accounts denominated in both domestic and foreign currency with prudential policy

objectives. Given their broad application, we estimate (2) with the two-year cumulated reserve

requirement indices, hypothesising that these policies should signiﬁcantly interact with US

monetary policy spillovers for both total credit and bank credit.

Figure 6 presents the interaction coefﬁcient estimates at different horizons for total credit

and bank credit.19 In line with our hypothesis, the estimates are signiﬁcantly positive at a range

of horizons, indicating that reserve requirements can help to offset the spillover effects of US

monetary policy shocks in EMs.20

Taken together, the results in this section suggest that two subsets of prudential policies

instruments—speciﬁcally LTV ratio limits and reserve requirements—are particularly effective

at offsetting the spillover effects of US monetary policies and dampening a country’s exposure

to the associated global credit cycle. This is an important mechanism through which Borio

(2010) and Claessens et al. (2013) identify these prudential policies to be effectively used by

19For house prices, the interaction coefﬁcient with respect to the reserve requirement proxy is insigniﬁcantly

different from zero at all horizons. See Appendix B.6 for additional robustness analyses.

20Within our dataset, we do not ﬁnd signiﬁcant differences between domestic and foreign currency denominated

reserve requirements, although more granular analyses of currency denomination may prove useful here.

-2.00-1.000.001.002.00pp02468QuarterHouse Prices: 2-year Cumulative LTV Limits-6.00-4.00-2.000.002.004.00pp02468QuarterTotal Credit: 2-year Cumulative LTV LimitsECB Working Paper Series No 2339 / December 201922Figure 6: Interaction of US monetary policy spillovers with reserve requirements in recipient
emerging markets for total credit and bank credit

mp}8

Notes: {δh
h=0 estimates with (log) total credit (left-hand side) and (log) bank credit (right-hand side) for 29
emerging markets as dependent variable (regression (2)). The light blue shaded area denotes the 90% conﬁdence
interval around point estimates, constructed from Driscoll and Kraay (1998) standard errors. The prudential policy
measure is deﬁned as the two-year cumulated sum of all reserve requirements, levied on domestic and foreign
currency-denominated deposits, in the Cerutti et al. (2017b) dataset.

national authorities to counter-cyclically dampen an expected credit boom or credit crunch.

5 Cross-Country Heterogeneity in Policy Interactions

In this section, we ask what conditions make this interaction effect stronger? To do so, we

estimate (3) using different country characteristics Zi,t−1 as additional interactors.

5.1 Geography

We ﬁrst investigate geographical differences in the strength of the policy interaction. To do this,

we estimate separate interaction coefﬁcients for the 7 Latin American economies, 8 European

economies and 11 Asian economies that comprise our set of EMs.21 There are reasons to expect

differences across the groups. First, the geographical proximity of Latin American economies

to the US is likely to strengthen economic ties between the two regions, both through trade and

ﬁnancial links. Second, data from Gopinath (2015) shows that the degree of dollar currency

invoicing of international trade differs signiﬁcantly across the three regions. Based on a smaller

subset of EMs than in our macro-ﬁnancial panel, the data from Gopinath (2015) illustrates that

the average degree of dollarisation in Latin American EMs is around 97%, while in Asia and

21We omit the remaining 3 African economies from this comparison.

-1.000.001.002.003.00pp02468QuarterTotal Credit: 2-year Cumulative Reserve Requirements-1.000.001.002.00pp02468QuarterBank Credit: 2-year Cumulative Reserve RequirementsECB Working Paper Series No 2339 / December 201923Figure 7: Interaction of US monetary policy spillovers with aggregate prudential policies, ex-
cluding aggregate capital requirements, in recipient emerging markets in Latin America, Asia
and Europe

mp}8

Notes: {δh
h=0 estimates with (log) total credit (left-hand side) and (log) bank credit (right-hand side) for 29
emerging markets as dependent variable (regression (3)), grouped by region. The light yellow shaded area denotes
the 90% conﬁdence interval around point estimates for countries in Latin America, constructed from Driscoll and
Kraay (1998) standard errors. The aggregate prudential policy measure is deﬁned as the two-year cumulated sum
of all prudential measures, excluding aggregate capital requirements, in the Cerutti et al. (2017b) dataset.

Europe the shares are around 80% and 41%, respectively.

We thus assess regional differences in the interaction with the hypothesis that geographi-

cally close and more dollarised EMs are likely to gain the most from offsetting prudential policy

actions in the face of US monetary policy spillovers. Our results appear to conﬁrm this, quan-

titatively at least. Estimates of the interaction coefﬁcient are most strongly positive for Latin

America (Figure 7), although differences are not statistically signiﬁcant.

5.2 Home Ownership

The degree of home ownership within an economy is another important determinant of ﬁnan-

cial risks. Higher home ownership rates tend to be associated greater housing market risks, as

more households borrow and lever-up to buy housing.

Our results indicate that prudential polices are indeed more effective at dampening cycli-

cal ﬂuctuations in EMs with higher home ownership shares (Figure 8). The left-hand plot

shows that, in countries with above median home ownership shares, the interaction between

our baseline measure of aggregate prudential policy and US monetary policy for spillovers to

bank credit is signiﬁcantly positive.22 In contrast, the interaction is insigniﬁcantly different for

22Home ownership share data is described with reference to Table 2.

0,002,004,006,008,00pp02468QuarterLatin AmericaAsiaEuropeTotal Credit: 2-year Cumulative Macroprudential Measures-1,000,001,002,003,00pp02468QuarterLatin AmericaAsiaEuropeBank Credit: 2-year Cumulative Macroprudential MeasuresECB Working Paper Series No 2339 / December 201924Figure 8: Interaction of US monetary policy spillovers with aggregate prudential policies, ex-
cluding general capital requirements, in recipient emerging markets with home ownership
shares above and below the median

mp}8

Notes: {δh
h=0 estimates with (log) bank credit (left-hand side) and (log) house prices (right-hand side) for 29
emerging markets as dependent variable (regression (3)), grouped by share of home ownership. The light blue
shaded area and green-dashed lines denote the 90% conﬁdence interval around point estimates for countries
with below and above median home ownership shares, respectively, constructed from Driscoll and Kraay (1998)
standard errors. The aggregate prudential policy measure is deﬁned as the two-year cumulated sum of all
prudential measures, excluding general capital requirements, in the Cerutti et al. (2017b) dataset (left-hand side);
the right-hand side uses two-year cumulated LTV ratio limits only.

countries with home ownership shares that lie below the cross-country median.

The right-hand panel hones in speciﬁcally on the role of LTV ratio limits, which we found

to be signiﬁcantly important for spillovers to house prices in the previous section. Consistent

with the view that housing sectors with high home ownership shares pose greater risks to a

country’s ﬁnancial stability, our results indicate that LTV ratio limits are indeed more effec-

tive at dampening housing market ﬂuctuations in these economies. We ﬁnd a signiﬁcantly

positive interaction coefﬁcient for above median home ownership countries when considering

spillovers to house prices and the role of LTV ratio limits. In contrast, the interaction for below

median home ownership countries is broadly insigniﬁcant. The difference between the two is

signiﬁcantly positive at the h = 1 horizon.

5.3 Exchange Rate Regimes

Exchange rate regimes are at the centre of the traditional international macroeconomic pol-

icy trilemma. A country with free capital mobility can only pursue an independent monetary

policy if their exchange rate is allowed to ﬂoat. Ever since the work of Mundell (1963) and

Fleming (1962), it has been understood that the spillover effects of foreign shocks will depend

-2.00-1.000.001.002.00pp02468QuarterHome Ownership < MedianHome Ownership > MedianBank Credit: 2-year Cumulative Macroprudential Measures-6.00-4.00-2.000.002.00pp02468QuarterHome Ownership < MedianHome Ownership > MedianHouse Prices: 2-year Cumulative LTV MeasuresECB Working Paper Series No 2339 / December 201925Figure 9: Interaction of US monetary policy spillovers with aggregate prudential policy in re-
cipient emerging markets under ﬁxed and ﬂoating exchange rates for total credit and bank
credit

mp}8

Notes: {δh
h=0 estimates with (log) total credit (left-hand side) and (log) bank credit (right-hand side) for 29
emerging markets as dependent variable (regression (2)). The light blue shaded area and green-dashed lines denote
the 90% conﬁdence interval around point estimates for countries with ﬂoating and ﬁxed exchange rate regimes,
respectively, constructed from Driscoll and Kraay (1998) standard errors. Aggregate prudential policy measure is
deﬁned as the two-year cumulated sum of all prudential measures, excluding general capital requirements, in the
Cerutti et al. (2017b) dataset. Exchange rate regimes are classiﬁed using the de facto measure of Ilzetzki et al. (2019).

on the prevailing exchange rate regime. Within the Mundell-Fleming paradigm, monetary pol-

icy spillovers are likely to be larger than for countries with ﬂoating exchange rates, as relative

prices are unable to adjust to insulate against the effects of foreign shocks. But, in the context

of our EM-focused study, a competing channel is at play: foreign currency-denominated debt.

In a country with a high share of foreign currency-denominated debt, a ﬁxed exchange rate is

likely to insulate against foreign shocks to some extent, by preventing valuation effects.

To assess how the interaction changes with respect to exchange rate regimes, we estimate (3)

using the de facto exchange rate regime classiﬁcation of Ilzetzki et al. (2019) as our indicator of

country characteristics Zi,t−1, differentiating between countries anchored to the US dollar and

those not. We favour a de facto classiﬁcation precisely because we wish to account for the role

of exchange rate adjustment in determining the interaction, rather than possible institutional

characteristics associated with de jure measures.

Our results indicate that prudential policies appear to equally dampen cyclical ﬂuctuations

from US monetary policy shocks under ﬂoating and ﬁxed exchange rate regimes (Figure 9).

Point estimates for the interaction effect are approximately the same for ﬁxed and ﬂoating

regimes, suggesting that the competing effects of exchange rate regimes for spillovers tend

to roughly balance in the context of cross-border prudential policy interactions.

-2.000.002.004.006.00pp02468QuarterFloatingFixedTotal Credit: 2-year Cumulative Macroprudential Measures-2.000.002.004.006.00pp02468QuarterFloatingFixedBank Credit: 2-year Cumulative Macroprudential MeasuresECB Working Paper Series No 2339 / December 2019266 Conclusion

This paper has presented novel evidence into the role of prudential policy in reducing the

macro-ﬁnancial spillover effects of US monetary policy shocks to EMs. We ﬁnd that prudential

policies can partially offset the negative spillover effects of US monetary policy, and dampen a

country’s exposure to the associated global credit cycle, in a statistically and economically sig-

niﬁcant manner. Importantly, our ﬁndings are robust to accounting for other factors—such as

capital controls—that could also reduce spillovers to EMs. In particular, we identify LTV ratio

limits and reserve requirements to be effective tools for achieving this. Reserve requirements

signiﬁcantly reduce the spillover effects of US monetary policy to credit supply, through bank

credit especially, while LTV ratio limits signiﬁcantly mitigate spillovers to house prices.

While our empirical speciﬁcation allows us to estimate cross-border monetary and pruden-

tial policy interactions, some limitations in our analysis remain—common to the majority of

extant literature on prudential policy. First, our aggregate measure of prudential policy in-

cludes a relatively restricted number of prudential instruments, limiting the range of measures

that we consider. Second, our prudential policy dataset measures policy actions and captures

the intensity of policy changes only to a limited extent. Third, our empirical framework cannot

be easily reversed to study how the level of interest rates inﬂuences the spillovers of pruden-

tial policy ‘shocks’, due to challenges identifying exogenous innovations to prudential policies.

Future research will, no doubt, beneﬁt from improvements in prudential policy data coverage

and granularity.

Nevertheless, our ﬁndings have important implications, suggesting that prudential policies

can be effective at reducing the spillover effects of US monetary policy, helping policymakers

to maintain monetary policy autonomy in the face of spillovers and the global ﬁnancial cycle.

ECB Working Paper Series No 2339 / December 201927Appendix

A Data Sources

A.1 Macro-Financial Panel Dataset

Our macro-ﬁnancial dataset spans 29 EMs (and 35 advanced economies). We use data for the
following macro-ﬁnancial variables from a range of sources, listed below:

• Total credit and Bank credit (Sources: Bank for International Settlements and Interna-

tional Financial Statistics)

• House prices (Sources: Bank for International Settlements and Oxford Economics)

• Real GDP (Sources: International Monetary Fund, OECD and National Statistics Insti-

tutes)

• Consumer Price Index (CPI) (Source: International Monetary Fund)

A.2 Prudential Policy Data

The Cerutti et al. (2017b) dataset includes information on the following types of prudential
policies:

1. Capital requirements

(a) Aggregate capital requirements, reﬂecting the implementation of Basel capital agree-

ments

(b) Sector-speciﬁc capital buffers, levied on

i. Real estate credit
ii. Consumer credit
iii. Credit to other sectors

2. Concentration ratio limits

3. Interbank exposure limits

4. Loan-to-value ratio limits

5. Reserve requirements

(a) Reserve requirements on local currency-denominated accounts

(b) Reserve requirements on foreign currency-denominated accounts

ECB Working Paper Series No 2339 / December 201928A.2.1 Exploratory Prudential Policy Data

Figure 10 illustrates the the cross-sectional variation in the prudential policy dataset, plotting

a global heat map of cumulated aggregate prudential policy actions from 2011:Q1 to 2012:Q4

for the 29 EMs in our dataset. The plot shows a wide degree of cross-country variation in the

activeness with which prudential policy is used across countries, in particular in EMs. For this

two-year period, the tightest aggregate prudential policies occurred in Peru and Nigeria, with

the loosest in India.

Figures 11-14 illustrate the time series variation in our baseline measure of aggregate pru-

dential policy for Emerging Asian, Emerging European, Latin American and African economies,

respectively, constructed by cumulating all actions over a two-year period and summing all

prudential policies, except general capital requirements, in the Cerutti et al. (2017b) prudential

policy dataset.

A.2.2 Summary Statistics

Table 3 presents summary statistics for our aggregate prudential policy proxy cumulated over

2 years by country. Table 4 presents summary statistics for the same variable by year. Column

(1) illustrates that, on average, most EMs tightened their prudential policies in aggregate (ex-

cluding general capital requirements) before the 2007-2008 ﬁnancial crisis, loosening them im-

mediately after. Columns (2)-(6) present summary statistics for 2-year cumulated measures of

speciﬁc prudential policy instruments. This aggregate loosening post-crisis was concentrated

in capital requirements, LTV ratio limits and reserve requirements.

A.3 Monetary Policy Shocks

A.3.1 Methodology

To describe the econometric framework for identifying US monetary policy shocks, we draw

heavily on Gertler and Karadi (2015) (Section II). Let Yt be a K × 1 vector of economic and

ﬁnancial variables and εt be a vector of structural white noise shocks. A general structural

form VAR is given by

AYt =

p
(cid:88)

j=1

CjYt−j + εt

(4)

where A and Cj ∀j ≥ 1 are conformable coefﬁcient matrices.

ECB Working Paper Series No 2339 / December 201929Figure 10: Cumulated aggregate prudential policy actions by EM country, 2011:Q1-2012:Q4

Notes: Sum of the all prudential policy actions, excluding general capital requirements, in the Cerutti et al. (2017b)
prudential policy actions dataset between 2011:Q1 and 2012:Q4 in each of the 29 EMs in our dataset.

Figure 11: Time series variation in two-year cumulated aggregate prudential policy actions in
Emerging Asian economies, 2001:Q4-2017:Q4

Notes: Time series of two-year cumulated aggregate prudential policy actions, constructed using all prudential
policies, except general capital requirements, in the Cerutti et al. (2017b) prudential policy actions dataset between
2001:Q4 and 2017:Q4 in Emerging Asian economies.

[-4,-2](-2,0](0,1](1,2](2,3](3,4](4,6](6,11]No dataPrudential Policy Actions over Two-Year Period, 2011:Q1-2012:Q4-50510Actions2002q12004q12006q12008q12010q12012q12014q12016q12018q1DateChinaIndiaIndonesiaKuwaitMalaysiaMongoliaPhilippinesSaudi ArabiaThailandTurkeyVietnamAggregate Prudential Policy ActionsECB Working Paper Series No 2339 / December 201930Figure 12: Time series variation in two-year cumulated aggregate prudential policy actions in
Emerging European economies, 2001:Q4-2017:Q4

Notes: Time series of two-year cumulated aggregate prudential policy actions, constructed using all prudential
policies, except general capital requirements, in the Cerutti et al. (2017b) prudential policy actions dataset between
2001:Q4 and 2017:Q4 in Emerging European economies.

Figure 13: Time series variation in two-year cumulated aggregate prudential policy actions in
Latin American economies, 2001:Q4-2017:Q4

Notes: Time series of two-year cumulated aggregate prudential policy actions, constructed using all prudential
policies, except general capital requirements, in the Cerutti et al. (2017b) prudential policy actions dataset between
2001:Q4 and 2017:Q4 in Latin American economies.

-10-50510Actions2002q12004q12006q12008q12010q12012q12014q12016q12018q1DateBulgariaCroatiaHungaryPolandRomaniaRussiaSerbiaUkraineAggregate Prudential Policy Actions-10-50510Actions2002q12004q12006q12008q12010q12012q12014q12016q12018q1DateArgentinaBrazilChileColombiaMexicoPeruUruguayAggregate Prudential Policy ActionsECB Working Paper Series No 2339 / December 201931Table 3: Summary statistics for prudential policy proxies constructed by cumulating actions
over a two-year period by country

Prudential Policy Measure
Argentina
Brazil
Bulgaria
Chile
China
Colombia
Croatia
Hungary
India
Indonesia
Kuwait
Lebanon
Malaysia
Mexico
Mongolia
Nigeria
Peru
Philippines
Poland
Romania
Russia
Saudi Arabia
Serbia
South Africa
Thailand
Turkey
Ukraine
Uruguay
Vietnam

# Obs.
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65
65

P rui,t
-0.046
1.000
0.585
-0.123
1.631
0.000
-0.185
-1.046
0.569
0.354
0.123
0.092
0.492
0.246
0.385
1.723
1.415
0.538
-0.292
-1.923
0.585
-0.015
-1.215
0.000
0.385
1.908
-0.538
0.800
-0.062

σ (P rui,t) min (P rui,t) max (P rui,t)

2.308
2.817
2.297
0.484
4.629
0.810
2.221
0.975
3.211
1.643
0.781
0.897
2.463
0.662
1.128
3.560
4.687
1.347
1.826
2.740
3.832
0.800
3.059
0.000
1.026
3.121
2.586
1.897
2.098

−7
−4
−5
−2
−7
−2
−5
−3
−5
−2
−1
−2
−3
0
−1
−3
−9
−3
−4
−7
−7
−2
−8
0
−2
−5
−7
−3
−5

5
8
7
1
11
2
4
2
8
3
2
2
7
2
4
11
11
3
3
2
7
3
6
0
3
9
5
4
4

Notes: Summary statistics are constructed for each country by pooling observations over the full sample
period.

ECB Working Paper Series No 2339 / December 201932Figure 14: Time series variation in two-year cumulated aggregate prudential policy actions in
African economies, 2001:Q4-2017:Q4

Notes: Time series of two-year cumulated aggregate prudential policy actions, constructed using all prudential
policies, except general capital requirements, in the Cerutti et al. (2017b) prudential policy actions dataset between
2001:Q4 and 2017:Q4 in African economies.

The reduced-form representation is attained by pre-multiplying each side of (4) by A−1:

Yt =

p
(cid:88)

j=1

BjYt−j + ut

(5)

where ut is a vector of reduced-form shocks, with the following relationship to structural dis-

turbances

ut = Sεt

(6)

with Bj = A−1Cj and S = A−1. Σ = E [utu(cid:48)

t] = E [SS(cid:48)] is the variance-covariance matrix of

the reduced-form errors.

Deﬁne Y p

t ∈ Yt as the policy indicator, with corresponding exogenous structural shock εp

t ∈

εt. Like Gertler and Karadi (2015), let the policy indicator in the VAR differ from the policy

instrument to permit interest rate variation due to forward guidance. The policy indicator

is deﬁned as a government bond interest rate of somewhat longer maturity than the policy

instrument—e.g. federal funds rate in US. The government bond rate captures innovations to

both the current policy rate and expectations about the path of future policy rates.

-50510Actions2002q12004q12006q12008q12010q12012q12014q12016q12018q1DateLebanonNigeriaSouth AfricaAggregate Prudential Policy ActionsECB Working Paper Series No 2339 / December 201933Table 4: Summary statistics for prudential policy proxies constructed by cumulating actions
over a two-year period by year

Prudential Policy Measure
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017

# Obs.
116
116
116
116
116
116
116
116
116
116
116
116
116
116
116
116

P rui,t
-0.336
-0.405
-0.034
0.586
0.966
1.043
1.138
-0.759
-1.164
1.638
2.078
0.871
0.310
-0.345
-0.707
-0.638

σ (P rui,t) min (P rui,t) max (P rui,t)

1.704
2.261
1.724
1.818
2.030
2.019
2.509
1.998
2.442
3.425
3.005
2.983
2.576
2.506
2.068
1.944

−5
−7
−7
−5
−6
−2
−6
−7
−8
−7
−3
−4
−6
−9
−7
−6

7
8
3
5
7
10
11
4
7
10
11
11
9
6
5
6

Notes: Summary statistics are constructed for each year by pooling observations over quarters within the
year and across the 29 EMs.

To identify the monetary policy shock εP

t , let Zt be a vector of instrumental variables and

εq
t a vector of structural shocks excluding the policy shock. The identiﬁcation assumptions are:

E

(cid:104)
Ztεp
t

(cid:48)(cid:105)

E

(cid:104)
Ztεq
t

(cid:48)(cid:105)

= φ

= 0

(7)

(8)

To be valid, the instruments must be correlated with the policy shock, but orthogonal to all

other structural shocks.

Let s represent the column in matrix S corresponding to the impact of the structural mon-

etary policy shock εp

t on each element of the reduced-form residuals ut (see (6)). Estimates of

the elements in the vector s can be obtained in the following two steps:

1. Estimate the reduced-form VAR (5) by OLS to obtain a vector of reduced-form residuals

ˆut. Deﬁne ˆup
let ˆuq

t as the reduced-form residual from the equation for the policy indicator, and

t be the vector of reduced-form residuals from the other variable equations, for q (cid:54)= p.

2. Deﬁne sq ∈ s as the response of uq

t to a unit increase in the structural policy shock εp
t .
t on ˆup
Obtain an estimate of the ratio sq/sp from a two-stage least squares regression of ˆuq
t ,
using the instrument set Zt. Estimates of sp and sq can be derived up to a sign convention

ECB Working Paper Series No 2339 / December 201934using the estimates reduced-form variance-covariance matrix of residuals ˆΣ.23

A.3.2 Data and Results

We estimate the structural shocks using a common methodology and 1979:07-2018:10 sample

for the US. Because the regression framework includes 12 lags, monthly frequency estimates of

policy shocks begin in 1980:07.

The VARs include four variables that match the baseline speciﬁcation in Gertler and Karadi

(2015): the 1-year government bond interest rate, industrial production, the consumer price

index (CPI), and a measure of corporate credit spreads.24 Industrial production and CPI are

included in log levels, while interest rates and credit spreads are included in levels. The VARs

for all regions include 12 lags of monthly variables—i.e. p = 12. The instrument for monetary

policy Zt is selected based on its relevance, measured by ﬁrst-stage F -statistics, which is 22.9

for this sample.

B Additional Empirical Results

In this appendix, we touch upon additional coefﬁcient estimates from regressions unreported

in the main body of the paper.

B.1 Additional Aggregate Interactions

Figure 15 reports interaction coefﬁcient estimates for EM real GDP and CPI using the aggregate

measure of prudential policy, excluding general capital requirements. The point estimates are

insigniﬁcant at all horizons, further motivating our focus on the interactions for total and bank

credit.

B.2 Accounting for Capital Controls

In table 2, we report to regression speciﬁcations which include additional interactions between
the US monetary policy shock and countries’ lagged capital ﬂow restrictiveness—i.e. M P $

t ×

23See Gertler and Karadi (2015, pp. 51-52).
24US industrial production and CPI data are from FRED. The 1-year government bond interest rate is from
G ¨urkaynak et al. (2007). Credit spreads are measured using the excess bond premium from Gilchrist and Zakrajsek
(2012). Monetary policy surprises are from G ¨urkaynak et al. (2005), constructed using intraday variation in the three
month-ahead federal funds futures rate in 30-minute windows around FOMC announcements.

ECB Working Paper Series No 2339 / December 201935Figure 15: Interaction of US monetary policy spillovers with aggregate prudential policy in
recipient emerging markets for real GDP and the consumer price index

mp}8

Notes: {δh
h=0 estimates with (log) real GDP (left-hand side) and the CPI (right-hand side) for 29 emerging
markets as dependent variable (regression (2)). The light blue shaded area denotes the 90% conﬁdence interval
around point estimates, constructed from Driscoll and Kraay (1998) standard errors. Aggregate prudential policy
is deﬁned as the two-year cumulated sum of all prudential policies, excluding general capital requirements, in the
Cerutti et al. (2017b) dataset.

KCi,t−1 and KCi,t−1 are included in the set of controls Xi,t−1, where KCi,t−1 is a measure

of capital controls in country i at time t − 1. Consistent with the focus of our paper, we only
report the δh coefﬁcient estimates in the main body of the paper—i.e. the coefﬁcients on M P $

t ×

P rui,t−1.

Although not the main focus of our paper, the coefﬁcient estimates on the M P $

t × KCi,t−1

provide some indication about the extent to which capital controls can help to insulate against

the spillover effects of US monetary policy. A positive interaction coefﬁcient for capital controls

can be interpreted in a similar vein to the coefﬁcient estimates on the prudential policy inter-

action in the main body of the paper. Table 5 reports the capital control interaction coefﬁcients

for total credit (columns (1) and (2)) and bank credit (columns (3) and (4)). Odd-numbered

columns use the overall capital ﬂow restrictiveness measure of Fern´andez et al. (2016), even-

number columns use the inﬂow-focused measures. In both cases, a higher number indicates

greater capital ﬂow restrictions. A signiﬁcantly positive interaction coefﬁcient suggests that

capital controls can help to offset some of the spillovers of US monetary policy for EMs.

The point estimates in table 5 are positive at all horizons, and signiﬁcantly different from

zero at some of them. These coefﬁcients indicate that—like prudential policy—tighter capital

controls in advance of a US monetary policy tightening shock can help to mitigate the negative

spillovers from US monetary policy. Importantly for our study, as columns (2) and (3) of table

-0.200.000.200.400.60pp02468Quarter-0.200.000.200.400.60pp02468QuarterECB Working Paper Series No 2339 / December 201936Table 5: Interaction coefﬁcient estimates for capital controls from regression (2) for total and
bank credit, when assessing the interaction of emerging market aggregate prudential policy
with US monetary policy

Total Credit

(1)
Capital
Controls

(2)
Capital Inﬂow
Controls

(3)
Capital
Controls

Bank Credit
(4)
Capital Inﬂow
Controls

M P $

t × KCi,t−1

h = 0

h = 1

h = 2

h = 3

h = 4

h = 5

h = 6

h = 7

h = 8

0.74
(1.21)
3.60*
(1.96)
5.19*
(3.02)
5.65
(3.77)
5.96
(4.75)
9.89
(6.97)
8.13
(6.88)
10.17
(8.60)
9.27
(8.47)

0.65
(1.14)
3.03
(1.87)
4.37
(2.82)
5.79
(3.69)
6.87
(4.54)
10.07
(6.51)
8.74
(6.67)
10.69
(8.38)
11.42
(8.50)

1.02
(1.21)
3.06
(2.01)
4.27
(3.17)
3.41
(3.94)
2.95
(4.79)
2.43
(5.77)
1.03
(6.31)
0.85
(7.26)
0.82
(7.62)

1.48
(1.15)
3.36*
(1.98)
4.43
(2.98)
4.17
(3.78)
4.42
(4.49)
3.28
(5.27)
2.50
(5.86)
2.59
(6.80)
3.87
(7.17)

YES
YES

Country FE
Time FE
Notes: M P $
t × KCi,t−1 coefﬁcient estimates from an extended variant of regression (2), which includes
M P $
t × KCi,t−1 and KCi,t−1 in the set of control variables Xi,t−1 to account for other potential interactors,
in addition to prudential policy, with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically signiﬁcant
coefﬁcient estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay (1998)
standard errors (reported in parentheses).

YES
YES

YES
YES

YES
YES

2 show, prudential policies continue to signiﬁcantly offset the spillovers of US monetary policy

to EM total credit, even when accounting for the potentially competing role of capital ﬂow

restrictions.

B.3

Including General Capital Requirements in P rui,t−1

In the main body of the paper, our measure of aggregate prudential policy excludes general

capital requirements, which predominantly reﬂect Basel III requirements—as explained in sec-

tion 2.1. Column (1) of table 6 documents point estimates for the interaction coefﬁcient for

total, at different horizons, when general capital requirements are included in the aggregate

ECB Working Paper Series No 2339 / December 201937Figure 16: US monetary policy spillovers to total credit and bank credit for different levels of
aggregate prudential policy in recipient emerging markets

mp + δh}8

Notes: {βh
h=0 estimates with (log) total credit (left-hand side) and (log) bank credit (right-hand side) for
29 emerging markets as dependent variable in hybrid version of regression (2) that excludes time ﬁxed effects
f h
t , but includes US monetary policy measure M P $
t and lagged global controls Gt−1. The aggregate prudential
policy measure is deﬁned as the two-year cumulated sum of all prudential policy actions, including general
capital requirements, in the Cerutti et al. (2017b) dataset. The blue line denotes estimated spillover from a 1pp
US monetary policy tightening shock to an EM with a 0 value for prudential policy. The green line denotes the
comparable spillover estimate for an EM with a +1 prudential policy action—i.e. on net, one additional policy
tightening. The red line denotes the opposite spillover, for an EM with a −1 prudential policy action.

prudential policy measure. As in table 2, the point estimates are positive at all horizons. In this

case, the signiﬁcantly positive 5-quarter-ahead coefﬁcient indicates that an additional tighten-

ing of prudential policy in an EM in advance of a +1pp US monetary policy tightening can, on

average, reduce the hit to total credit hit from the shock by around 1.2 pp.

Columns (2)-(8) repeat the robustness analyses of table 2 for the extended measure of ag-

gregate prudential policy. As in the main body of the paper, signiﬁcantly positive interaction

coefﬁcients remain at the 5-quarter-ahead horizon, at least, when potentially competing inter-
action hypotheses are included in the regression, in addition to the M P $

t × P rui,t−1 term.

Economic Signiﬁcance Our headline economic signiﬁcance is robust to the inclusion of gen-

eral capital controls in the aggregate prudential policy measure, as ﬁgure 16 shows. The quan-

titative effect of domestic prudential policy is similar, with an additional prudential policy

tightening action associated with a reduction in the peak total credit spillover from around 7%

to 6%, and a from a little under 5.5% to 4.5% for bank credit.

-8-6-4-20Percent02468QuarterNo Pru+1 Pru-1 PruTotal Credit: 2-year Cumulative Prudential Measures-6-4-20Percent02468QuarterNo Pru+1 Pru-1 PruBank Credit: 2-year Cumulative Prudential MeasuresECB Working Paper Series No 2339 / December 201938Table 6: Interaction coefﬁcient estimates ˆδh from regression (2) for total credit using aggregate
prudential policy measures, which include general capital requirements, in recipient emerging
markets

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Additional Interaction Variables

Baseline

Capital
Control

Capital
Inﬂow
Control

Credit-
to-GDP
Growth

FX
Regime

M P $

t × P rui,t−1

h = 0

h = 1

h = 2

h = 3

h = 4

h = 5

h = 6

h = 7

h = 8

0.15
(0.17)
0.18
(0.29)
0.26
(0.39)
0.57
(0.43)
0.87
(0.55)
1.22**
(0.59)
0.96
(0.61)
1.05
(0.70)
0.69
(0.79)

0.08
(0.18)
0.01
(0.28)
0.01
(0.37)
0.34
(0.41)
0.63
(0.53)
0.92*
(0.53)
0.72
(0.54)
0.80
(0.61)
0.46
(0.75)

0.10
(0.18)
0.05
(0.28)
0.07
(0.38)
0.38
(0.41)
0.66
(0.53)
0.96*
(0.56)
0.77
(0.59)
0.86
(0.67)
0.49
(0.80)

0.09
(0.18)
0.05
(0.30)
0.08
(0.39)
0.36
(0.37)
0.66
(0.47)
0.97*
(0.55)
0.70
(0.57)
0.73
(0.65)
0.35
(0.72)

0.09
(0.15)
0.18
(0.25)
0.28
(0.34)
0.68*
(0.39)
0.94*
(0.50)
1.35**
(0.52)
1.05*
(0.54)
1.17*
(0.65)
0.70
(0.76)

Home
Own.
Share

0.15
(0.16)
0.20
(0.28)
0.29
(0.38)
0.59
(0.42)
0.86
(0.53)
1.23**
(0.57)
0.96
(0.60)
1.06
(0.69)
0.66
(0.79)

Country
FE

0.12
(0.11)
0.26
(0.24)
0.35
(0.33)
0.55
(0.39)
0.72
(0.47)
1.41**
(0.56)
1.22*
(0.66)
1.68**
(0.80)
0.82
(0.98)

YES
YES
NO

YES
Country FE
YES
Time FE
Country FE ×M P $
NO
t
Notes: ˆδh, for h = 1, ..., 8, coefﬁcient estimates from various speciﬁcations of regression (2) designed to
account for other potential interactors with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically
signiﬁcant coefﬁcient estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay
(1998) standard errors (reported in parentheses).

YES
YES
YES

YES
YES
NO

YES
YES
NO

YES
YES
NO

YES
YES
NO

ECB Working Paper Series No 2339 / December 201939B.4 Robustness: Cumulating Prudential Policy over 1 Year

In the main body of the paper, we construct a measure of prudential policy that cumulates ac-

tions in the two years prior to a monetary policy shock. As we explain in section 2.1, this choice

represents the balancing of a trade-off. On the one hand, the cumulation period needs to be suf-

ﬁciently long to capture policy transmission and implementation lags and proxy persistence in

the level of prudential policy. But, on the other, we want to avoid a cumulation period that is

too long such that reversals in prudential policy suppress variation in our proxy. To assess the

robustness of our results to the choice of a two-year cumulation period, we repeat our headline

interaction analysis for a one-year cumulated measure of aggregate prudential policy, which

again excludes general capital requirements.

The results of this, for total credit and bank credit, are documented in tables 7 and 8, re-

spectively. Column (1) of both tables broadly corroborate with the results in the main body of

the paper, in terms of sign, signiﬁcance and magnitude. Column (1) of table 7 indicates that,

using our one-year cumulated aggregate prudential policy measure, an additional EM pruden-

tial policy tightening in advance of a +1pp US monetary policy shock can, on average, reduce

the hit to total credit hit from the shock by around 1.5pp. The corresponding ﬁgure for the

two-year cumulated headline result is 1.4pp.

ECB Working Paper Series No 2339 / December 201940Table 7: Interaction coefﬁcient estimates ˆδh from regression (2) for total credit using aggregate
prudential policy measures, which exclude general capital requirements and are cumulated
over 1 year, in recipient emerging markets

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Additional Interaction Variables

Baseline

Capital
Control

Capital
Inﬂow
Control

Credit-
to-GDP
Growth

FX
Regime

M P $

t × P rui,t−1

h = 0

h = 1

h = 2

h = 3

h = 4

h = 5

h = 6

h = 7

h = 8

0.61**
(0.25)
0.49
(0.44)
0.66
(0.59)
0.90
(0.61)
1.51**
(0.70)
1.49**
(0.70)
1.14
(0.85)
1.16
(0.98)
0.73
(1.05)

0.64**
(0.26)
0.40
(0.40)
0.60
(0.53)
0.87
(0.54)
1.44**
(0.64)
1.20*
(0.66)
0.94
(0.81)
0.99
(0.92)
0.65
(1.06)

0.64**
(0.28)
0.42
(0.44)
0.62
(0.56)
0.84
(0.55)
1.40**
(0.62)
1.15*
(0.65)
0.87
(0.80)
0.90
(0.92)
0.51
(1.03)

0.57**
(0.24)
0.34
(0.43)
0.45
(0.58)
0.73
(0.61)
1.37**
(0.68)
1.28*
(0.72)
0.90
(0.89)
0.87
(1.02)
0.42
(1.09)

0.51**
(0.20)
0.42
(0.37)
0.56
(0.48)
0.80
(0.49)
1.32**
(0.59)
1.37**
(0.57)
0.99
(0.73)
1.09
(0.86)
0.54
(0.95)

Home
Own.
Share

0.61**
(0.23)
0.51
(0.41)
0.68
(0.55)
0.86
(0.56)
1.41**
(0.66)
1.45**
(0.65)
1.07
(0.82)
1.12
(0.95)
0.62
(1.04)

Country
FE

0.54**
(0.20)
0.46
(0.33)
0.48
(0.44)
0.50
(0.48)
0.99*
(0.51)
1.06
(0.66)
0.68
(0.87)
0.87
(1.04)
0.10
(1.12)

YES
YES
NO

YES
Country FE
YES
Time FE
Country FE ×M P $
NO
t
Notes: ˆδh, for h = 1, ..., 8, coefﬁcient estimates from various speciﬁcations of regression (2) designed to
account for other potential interactors with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically
signiﬁcant coefﬁcient estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay
(1998) standard errors (reported in parentheses).

YES
YES
YES

YES
YES
NO

YES
YES
NO

YES
YES
NO

YES
YES
NO

ECB Working Paper Series No 2339 / December 201941Table 8: Interaction coefﬁcient estimates ˆδh from regression (2) for bank credit using aggregate
prudential policy measures, which exclude general capital requirements and are cumulated
over 1 year, in recipient emerging markets

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Baseline

Capital
Control

Additional Interaction Variables

Capital
Inﬂow
Control

Credit-
to-GDP
Growth

FX
Regime

Home
Own.
Share

Country
FE

M P $

t × P rui,t−1

h = 0

h = 1

h = 2

h = 3

h = 4

h = 5

h = 6

h = 7

h = 8

0.22*
(0.13)
0.01
(0.28)
0.19
(0.36)
0.43
(0.40)
0.96*
(0.52)
0.74
(0.60)
0.50
(0.73)
0.49
(0.79)
0.31
(0.85)

0.19
(0.15)
-0.08
(0.29)
0.16
(0.35)
0.43
(0.37)
0.94*
(0.48)
0.68
(0.62)
0.49
(0.79)
0.53
(0.87)
0.40
(0.95)

0.18
(0.15)
-0.08
(0.29)
0.15
(0.35)
0.41
(0.34)
0.89**
(0.42)
0.62
(0.55)
0.41
(0.71)
0.42
(0.78)
0.26
(0.85)

0.18
(0.14)
-0.09
(0.28)
0.06
(0.36)
0.36
(0.40)
0.92*
(0.53)
0.74
(0.61)
0.49
(0.72)
0.53
(0.79)
0.36
(0.85)

0.14
(0.12)
-0.04
(0.23)
0.09
(0.28)
0.39
(0.32)
0.83*
(0.42)
0.66
(0.50)
0.41
(0.64)
0.46
(0.66)
0.18
(0.70)

0.21*
(0.12)
0.02
(0.24)
0.18
(0.32)
0.41
(0.37)
0.87*
(0.50)
0.68
(0.58)
0.42
(0.71)
0.44
(0.76)
0.21
(0.82)

0.13
(0.11)
-0.07
(0.21)
-0.02
(0.29)
0.08
(0.31)
0.47
(0.35)
0.22
(0.50)
-0.06
(0.66)
-0.11
(0.72)
-0.47
(0.79)

YES
YES
NO

YES
Country FE
YES
Time FE
Country FE ×M P $
NO
t
Notes: ˆδh, for h = 1, ..., 8, coefﬁcient estimates from various speciﬁcations of regression (2) designed to
account for other potential interactors with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically
signiﬁcant coefﬁcient estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay
(1998) standard errors (reported in parentheses).

YES
YES
YES

YES
YES
NO

YES
YES
NO

YES
YES
NO

YES
YES
NO

ECB Working Paper Series No 2339 / December 201942B.5 Robustness: Loan-to-Value Ratio Limits and House Prices

Table 9 complements the analysis in section 4.2.1 in the main body of the paper, focusing specif-
ically on the effects of loan-to-value ratio limits. Column (1) documents the ˆδh estimates pre-

sented in left-hand side of ﬁgure 5 for house prices. Columns (2)-(7) present the robustness of

these estimates to the inclusion of additional interaction terms, designed to capture potentially

competing hypotheses.

Table 9: Interaction coefﬁcient estimates ˆδh from regression (2) for house prices using loan-to-
value ratio limit prudential policy measures in recipient emerging markets

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Additional Interaction Variables

Baseline

Capital
Control

Capital
Inﬂow
Control

Credit-
to-GDP
Growth

FX
Regime

M P $

t × P rui,t−1

h = 0

h = 1

h = 2

h = 3

0.24
(0.28)
0.66**
(0.31)
0.63
(0.51)
0.48
(0.82)

0.41
(0.35)
1.00**
(0.44)
0.82
(0.81)
0.69
(1.29)

0.35
(0.32)
0.84**
(0.36)
0.74
(0.68)
0.55
(1.09)

0.16
(0.35)
0.45
(0.38)
0.39
(0.61)
0.21
(0.90)

0.24
(0.28)
0.62**
(0.31)
0.60
(0.49)
0.44
(0.75)

Home
Own.
Share

0.23
(0.25)
0.62**
(0.28)
0.47
(0.45)
0.30
(0.75)

Country
FE

0.46
(0.34)
0.75
(0.54)
0.29
(0.70)
-0.22
(0.96)

YES
YES
NO

YES
Country FE
YES
Time FE
Country FE ×M P $
NO
t
Notes: ˆδh, for h = 1, ..., 8, coefﬁcient estimates from various speciﬁcations of regression (2) designed to
account for other potential interactors with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically
signiﬁcant coefﬁcient estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay
(1998) standard errors (reported in parentheses).

YES
YES
NO

YES
YES
NO

YES
YES
NO

YES
YES
YES

YES
YES
NO

B.6 Robustness: Reserve Requirements and Credit

Tables 10 and 11 complement the analysis in section 4.2.2 in the main body of the paper, fo-

cusing speciﬁcally on the effects of reserve requirements. Column (1) of each table documents
the ˆδh estimates presented in ﬁgure 6 for total credit and bank credit, respectively. Columns

(2)-(7) present the robustness of these estimates to the inclusion of additional interaction terms,

designed to capture potentially competing hypotheses.

ECB Working Paper Series No 2339 / December 201943Table 10: Interaction coefﬁcient estimates ˆδh from regression (2) for total credit using reserve
requirement prudential policy measures in recipient emerging markets

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Additional Interaction Variables

Baseline

Capital
Control

Capital
Inﬂow
Control

Credit-
to-GDP
Growth

FX
Regime

M P $

t × P rui,t−1

h = 0

h = 1

h = 2

h = 3

h = 4

h = 5

h = 6

h = 7

h = 8

0.21
(0.17)
0.32
(0.32)
0.54
(0.43)
0.89*
(0.49)
1.17*
(0.62)
1.58**
(0.70)
1.19*
(0.68)
1.22
(0.78)
0.66
(0.83)

0.18
(0.18)
0.23
(0.32)
0.43
(0.42)
0.85*
(0.48)
1.14*
(0.62)
1.54**
(0.60)
1.22**
(0.57)
1.27**
(0.63)
0.73
(0.78)

0.19
(0.17)
0.27
(0.31)
0.48
(0.41)
0.88*
(0.47)
1.15*
(0.60)
1.58**
(0.62)
1.27**
(0.61)
1.32*
(0.68)
0.76
(0.80)

0.16
(0.17)
0.21
(0.31)
0.38
(0.41)
0.70*
(0.41)
0.97*
(0.53)
1.35**
(0.66)
0.96
(0.64)
0.96
(0.75)
0.40
(0.76)

0.15
(0.15)
0.33
(0.28)
0.58
(0.38)
1.04**
(0.45)
1.27**
(0.57)
1.75***
(0.65)
1.31**
(0.60)
1.37*
(0.73)
0.69
(0.78)

Home
Own.
Share

0.21
(0.16)
0.34
(0.31)
0.57
(0.42)
0.91*
(0.47)
1.16*
(0.61)
1.59**
(0.69)
1.18*
(0.66)
1.22
(0.77)
0.63
(0.82)

Country
FE

0.20
(0.13)
0.37
(0.26)
0.61*
(0.36)
0.89**
(0.44)
1.04*
(0.53)
1.85***
(0.62)
1.58**
(0.71)
1.99**
(0.87)
0.96
(1.02)

YES
YES
NO

YES
Country FE
YES
Time FE
Country FE ×M P $
NO
t
Notes: ˆδh, for h = 1, ..., 8, coefﬁcient estimates from various speciﬁcations of regression (2) designed to
account for other potential interactors with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically
signiﬁcant coefﬁcient estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay
(1998) standard errors (reported in parentheses).

YES
YES
NO

YES
YES
NO

YES
YES
NO

YES
YES
YES

YES
YES
NO

ECB Working Paper Series No 2339 / December 201944Table 11: Interaction coefﬁcient estimates ˆδh from regression (2) for bank credit using reserve
requirement prudential policy measures in recipient emerging markets

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Additional Interaction Variables

Baseline

Capital
Control

Capital
Inﬂow
Control

Credit-
to-GDP
Growth

FX
Regime

M P $

t × P rui,t−1

h = 0

h = 1

h = 2

h = 3

h = 4

h = 5

h = 6

h = 7

h = 8

0.17
(0.14)
0.26
(0.26)
0.55
(0.37)
0.91*
(0.46)
1.04*
(0.57)
0.82
(0.61)
0.55
(0.65)
0.51
(0.69)
0.39
(0.73)

0.15
(0.15)
0.22
(0.27)
0.51
(0.39)
0.97**
(0.48)
1.11*
(0.60)
0.92
(0.64)
0.68
(0.69)
0.66
(0.74)
0.55
(0.77)

0.15
(0.14)
0.23
(0.25)
0.53
(0.37)
0.97**
(0.46)
1.09*
(0.57)
0.93
(0.63)
0.69
(0.68)
0.67
(0.71)
0.54
(0.74)

0.14
(0.13)
0.19
(0.24)
0.44
(0.34)
0.80*
(0.41)
0.93*
(0.53)
0.72
(0.57)
0.48
(0.61)
0.47
(0.66)
0.36
(0.70)

0.11
(0.12)
0.28
(0.23)
0.58*
(0.33)
1.04**
(0.41)
1.08**
(0.50)
0.90*
(0.53)
0.61
(0.57)
0.60
(0.61)
0.40
(0.65)

Home
Own.
Share

0.17
(0.13)
0.28
(0.25)
0.56
(0.36)
0.92**
(0.44)
1.01*
(0.56)
0.80
(0.60)
0.52
(0.64)
0.49
(0.68)
0.34
(0.73)

Country
FE

0.12
(0.11)
0.26
(0.23)
0.56
(0.34)
0.86**
(0.39)
0.84*
(0.48)
0.71
(0.58)
0.44
(0.71)
0.43
(0.83)
0.18
(0.97)

YES
YES
NO

YES
Country FE
YES
Time FE
Country FE ×M P $
NO
t
Notes: ˆδh, for h = 1, ..., 8, coefﬁcient estimates from various speciﬁcations of regression (2) designed to
account for other potential interactors with monetary policy spillovers. ∗, ∗∗ and ∗∗∗ denote statistically
signiﬁcant coefﬁcient estimates at 10%, 5% and 1% signiﬁcance levels, respectively, using Driscoll and Kraay
(1998) standard errors (reported in parentheses).

YES
YES
NO

YES
YES
NO

YES
YES
NO

YES
YES
YES

YES
YES
NO

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ECB Working Paper Series No 2339 / December 201950Acknowledgements 

We thank Ambrogio Cesa-Bianchi, Martin Bijsterbosch, Menzie Chinn (discussant), Giancarlo Corsetti, Alistair Dieppe, Matteo Iacoviello, 
Emile Marin, Gian Maria Milesi-Ferretti, Jouchi Nakajima (discussant), Dennis Reinhardt, Hélène Rey, Ilhyock Shim, Rhiannon 
Sowerbutts, and an anonymous referee, participants of International Banking Research Network (IBRN) meetings, and presentation 
attendees at the Bank of England, BIS-BoE-ECBIMF Spillovers Conference 2019, European Central Bank, Money, Macro and Finance 
Annual Conference 2019, and the World Bank for useful comments. The views expressed here are those of the authors, and not 
necessarily those of the Bank of England or the European Central Bank. 

Andra Coman 
European Central Bank, Frankfurt am Main, Germany; email: andra.coman@ecb.europa.eu 

Simon P. Lloyd 
Bank of England, London, United Kingdom; email: simon.lloyd@bankofengland.co.uk 

© European Central Bank, 2019 

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