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Cross-border banking  ows, Monetary policy, and the Business cycle

Cross-border banking ows, Monetary policy, and the Business cycle

Cross-border banking  ows, Monetary policy, and the Business cycle

Uploaded on: 2020-08-26


In this chapter, we investigate the role of global banks headquartered in eight
Asian countries in monetary policy transmission using bank-level panel data.
We nd that the Asian global banks were less responsive to domestic monetary
policy (hampering e ects), considering the bank-lending channel perspective.
We investigate the role of internal transactions between headoces and their
subsidiaries in this procedure as Cetorelli and Goldberg have argued: global
banks, when facing nancial stress, reallocate their internal funds from their
foreign aliates in support of the parent banks. This action might help the
parent banks to better protect their bank lending (Cetorelli and Goldberg
2012a, Jeon and Wu 2014). To examine if the channel they suggest operates,
we perform three additional identi cations: (1) Examine the relationship between
the size of foreign subsidiaries that Asian global banks have and the
magnitude in the hampering e ect, (2) loan supply of Asian global banks
during 2008
After the recent nancial crisis in 2008-2009, more attention from academics
and policymakers has been paid to globalized banking and its role in the
internal capital market, especially between the head oces of global banks and
their foreign subsidiaries. Recent studies appear to have reached a conclusion
that global banks could raise funds from their foreign subsidiaries to relieve
their liquidity constraints in the face of nancial stress in the home market,
and this helped the global banks cope better with the liquidity shock during
the recent nancial crisis.1
The existing studies on the issue can be divided broadly into two strands:
(i) studying the role of United States (US) headquartered global banks and its
consequences on shock propagation and monetary policy e ectiveness from the
US perspective (Cetorelli and Goldberg 2012a, 2012c; Correa et al. 2014) and
(ii) examining the behaviour of foreign banks (foreign subsidiaries of global
banks) operating in non-US host countries (Wu et al. 2011; Jeon et al. 2013,
2014). This might be due to the fact that the recent nancial crisis originated
in the US, while non-US countries, especially emerging market economies,
have long held concerns about the destabilizing e ects of sudden out
of foreign capital, when international nancial markets are unstable.2 For
example, Jeon and Wu (2014) found that foreign-owned banks operating in
seven Asian countries reduced their bank lending signi cantly during the 2008-
2009 crisis period, despite an eased monetary policy in those Asian countries.
One explanation for this is that the foreign-owned banks were being forced to
direct nances from Asia to the US or Europe, where the headquarters of the
bank group are located.
In this chapter, we present the rst study of the lending behaviour of Asian
global banks. More speci cally, we review banks that are headquartered in the
selected eight Asian countries (Parents), but that have operations (Juniors)
in other countries. We aim to examine whether these Asian global banks
1See Section 1.2.2 for recent empirical evidences.
2There have been long-held concerns about the destabilizing e ects that foreign banks
(or foreign nance - nancial openness, in a broader context) might bring about in the
emerging (or recipient) countries (Kose et al. 2009). On the one hand, it has been argued
that foreign nance enhances the eciency, competitiveness, and stability of the banking
systems in the host economies (Summers 2000, McCauley et al. 2010). On the other hand,
foreign banks have also been observed to act as a destabilizing force, as short-term pro t
seeking speculators, or as a source of contagion, by transmitting adverse shocks from the
home country to the host countries, especially when the banks in home countries face serious
nancial stress (Bhagwati 1998, Rodrik 1998, Stiglitz 2002).
1.1 Introduction
are less a ected by domestic monetary policy shocks, and thus lowering the
e ectiveness of the policy, and the internal capital markets does play a role in
this mechanism. This necessitates the classi cation of banks based on their
ownership types.
We classi y commecial banks as follows. Among the domestically-owned
banks, we de ne a global banks as a bank that has at least one foreign subsidiary
abroad for which the parent bank has a 50% or higher controlling
equity; otherwise, it is classi ed as a domestic bank. A bank is de ned as a
foreign bank if the capital owned by the overseas banks or rms is higher than
50% of the capital; these are the banks previously studied by Jeon and Wu
(2014). Hereafter, we term global banks headquartered in the eight selected
Asian countries as Parents3; their foreign subsidiaries are termed Juniors, to
help distinguish them from Foreign banks, which are the subsidiaries of foreign
global banks operating in the Asian region (foreign banks have been the focus
of previous research). We call domestically-owned banks that do not have a
subsidiary abroad and are not owned by a foreign rm Domestic banks. Based
on this classi cation, it is interesting to study Parents, not Foreign bank,
considering the fact that Foreign bank is only a small percentage of Asian
bank assets, while Parents constitute the majority of total bank assets.
We construct bank-level panel data for 271 commercial banks in eight Asian
countries: Hong Kong, Indonesia, Korea, Malaysia, the Philippines, Singapore,
Taiwan and Thailand. Data were obtained from Bureau van Dijk0s BankScope.
Except for indicators that distinguish banks based on ownership type, the
model we use is standard in estimating the bank-lending channel in that we
control the possible e ect of other bank-level characteristics (e.g. size, liquidity
and capital) as well as overall economic activities (e.g. real GDP growth
rate, unemployment rate) a ecting loan demand pressure. We estimate our
speci cation models using an Ordinary Least Squares estimator with clustered
standard errors for robustness and bank-level xed e ects. To relieve a possible
endogeneity problem, we adopt one-year lag bank characteristic variables,
as is commonly used in the relevant literature.
To answer our questions, we measure the marginal responsiveness of Parents0
bank lending to monetary policy in comparison with other types of banks. We
analyze observations from 2000 to 2007 at rst (Section 1.5.1 and Section
3Since Asian global banks (Parents) have relatively few and very small foreign sub-
sidiaries (Juniors), where many are located in the same Asian region, it might not appro-
priate to call them global banks. However, we follow this terminology, as it is in line with
the previous literature. For more detail, refer to Section 1.3.
1.1 Introduction, and then move to include all observations from 2000 to 2014 with
a focus on the banking behaviours in the 2008-2009 crisis periods (Section We nd that Parents are less a ected by monetary policy than different
types of banks5, with other bank characteristics and loan-demand pressure
controlled. This nding suggests that Parents lower the e ectiveness of
monetary policy that a ect real economic activity via the bank lending channel.
The theory of the bank lending channel predicts that banks have to (at
least to some extent) shrink their loan supply growth to keep total assets in
line with the reduced volume of liabilities in the face of monetary tightening.
The literature has aimed to nd relevant bank characteristics that a ect the
extent to which banks adjust their loan supplies in reaction to monetary policy
changes.6 Based on our ndings, we conjecture that having a global network
could serve as an additional feature that help banks better shield their loan
growth in the bank lending channel perspective.
One concern we have on our strategy is selection bias. It might be the
case that the largest and most successful banks tend to become global banks
(Parents) by launching new subsidiaries abroad and, at the same time, being
better able to deal with nancial stress by easily accessing external funds, as
compared to smaller and less successful banks. If this is the case, the hampering
e ect the Parents have against monetary policy tightening might just
ect their size or successfulness and not be an intrinsic feature of the globalized
banking network and internal capital market. To relieve this concern,
we control the e ects of other banking characteristics, including measurement
4In contrast to the existing literature, which mainly focuses on the nancial crisis period,
we aim to determine the behaviour of global banks during the normal period, which we believe
is more appropriate to measure the impacts of the globalized network on monetary policy
transmission, excluding anomalies and distortions. In this sense, we examine bank lending
from 2000 to 2007, with both the pre-2000 and post-2008 periods being excluded. During
the 1997-1998 Asian nancial crisis, Asian countries experienced a signi cant restructuring
of their banking industry. After 2008, Asian countries were signi cantly a ected by the
international nancial turmoil that initially was generated by the US subprime mortgage
lending crisis and the sovereign debt crisis in Europe. Then, we extends the observations to
include the nancial crisis and recent periods from 2008-2014 in Section
5In baseline regressions, in which we have Parents dummy, the reference groups are
both Domestic banks and Foreign banks. In robustness test, we also introduce a dummy
indicative of Foreign banks, where Domestic banks is the only constitute of the reference
6Researchers have tried to determine what bank characteristics help banks protect their
loan growth in the face of a negative monetary policy shock for many years. The magnitude
of this adjustment might depend on the banks characteristics (e.g. size of liquid assets, total
assets and equity) (Kashyap and Stein 1995, 2000; Kishan and Opiela 2000; Ashcraft 2006
among others). The previous literature have found supporting evidence that these bank
characteristics actually a ect the extent to which banks adjust loan growth. For more detail
on the bank lending channel and the relevant evidence, see Section 1.2.1.
1.1 Introduction
of size, liquid assets and capital, and the interacting e ects of these characteristics
with monetary policy changes on bank lending.
The remaining identi cations are devoted to determining whether the hampering
e ect that Parents have actually comes from (at least partially) its
operational internal capital market. We provide two sets of indirect evidence
supporting the role of the internal capital market. Firstly, we distinguish
Parents into two subgroups, based on the total size of all the Juniors they
have: those with relatively larger Juniors and those with relatively smaller
Juniors (Section We then measure the di erence of the hampering
e ects between the two Parents subgroups. The rationale is that if Parents
have relatively larger Juniors, then it is more likely that they depend more
on the internal capital market to raise funds and depend less on other external
sources in the face of nancial stress. If the Juniors they have are small, in
contrast, the extent to which Juniors can support their Parents would be
limited. We nd that the size of the Juniors does matter for the magnitude
of the Parents hampering e ect; this supports the role of the internal capital
market for the hampering e ects that Parents have.
Secondly, we move our attention from the normal periods of time to the
2008-2009 nancial crisis, and examine the response of bank lending during
2008 and 2009 with extended sample periods from 2000 to 2014 (Section We nd Parents that have relatively larger foreign subsidiaries
(Juniors) could better shield its bank lending during the nancial turmoil.
This ndings complements Jeon and Wu (2014)'s nding; they found that
Foreign banks operating in the Asia region had reduced its loan supply during
the crisis period to a larger extent, and they postulated that this might
be due to the liquidity reallocations from foreign subsidiaries to headquarters
of global banks. We nd that these opposite responses of Parents and
Foreign banks (many of the Foreign bank are the Juniors of the Parents)
invigorates the proposition that global banks could better protect their loan
supply by raising funds from their subsidiaries, thus causing a decline in bank
lending in the subsidiaries. Based on our ndings, we conclude that there
is evidence supporting the existence of the operational internal capital market
between the Asian global banks (Parents) and their foreign subsidiaries
(Juniors). This might play a role (at least partially) in the hampering e ect
on the monetary policy transmission. These results have important policy
implications that we discuss in the conclusions section.
1.1 Introduction
Then, we investigate the more direct relationship between the bank lending
of Juniors and their Parents0 nancial conditions, following the speci cation
strategies adopted in Jeon et al. (2013) in Section The primary idea
is that the signi cant connectedness between the loan growth of the Juniors
and the nancial conditions of their Parents can be regarded as supporting
evidence of the operational internal capital market between the Parents and
the Juniors. Based on this strategy, we nd that when Parents are in better
nancial conditions, their Juniors become less dependent on their own internal
funds in expanding its loan growth, and vice versa, though the evidence
is weak. We believe that this evidence supports our argument that the internal
capital market plays a role in helping its Parents better protect its bank
lending in the face of monetary policy tightening.
Our approach contributes to the relevant research in several ways. Firstly,
opposite to the conventional approach, this paper focuses on Asian-headquartered
global banks (Parents), not Foreign banks operating in the Asia region. This
work provides the evidence for Asia that parallels the Cetorelli and Goldberg
(2012a) work for the US. Asian global banks deserve more attention in that
they constitute a nancially signi cant and important institution in the region,
and thus, play a larger role in monetary policy transmission, as compared to
domestic banks or foreign banks.8 In addition, this approach enlightens us in
that the internal capital market could work between Asian global banks and
their subsidiaries, not merely between foreign banks operating in Asia and
their parent banks. To our best knowledge, no previous study on this issue
in Asia (and other emerging markets) has examined whether this mechanism
works for Asian global banks. Therefore, we believe that our ndings would
ll the gap in the literature and help us to have a better understanding on
how the globalized banking network in the region a ects shock propagation
and monetary policy e ectiveness.
7The dataset used for this is di erent from that used in other sections. For this, we use
26 Parents (global banks) headquartered in the eight selected Asia countries and 82 Juniors
aliated with them operating in the Asia region or somewhere else around the world. In
other parts of this paper, basically we analyze 271 commercial banks operating in the 8 Asia
countries by distinguishing them one of three types: 26 Parents, 157 Domestic banks and
113 Foreign banks. See Figure 1.2 in Section 1.3.2 for further explanation.
8In 2010, the 26 Asian global banks took 41.2% of the total banks assets in the eight
selected countries. In contrast, 81 foreign banks operating in the region at this time had
asset share of 31.9%. With Hong Kong excluded, 57 foreign banks only had 10.1% of assets
share in the region. Hong Kong is an exception in that foreign banks occupy the majority
market share of the banking industry, possibly due to its historical backgrounds. Detailed
information on the structure of the banking industry is discussed in Section 1.3.
1.2 Relevant studies
Secondly, this paper covers both the non-crisis periods and crisis periods.
The existent literature primarily focuses only on the crisis periods, not both.
In contrast, we explore the role of global banks in comparison with other types
of banks with the bank lending channel perspective for the normal period, and
expand our analysis for the recent nancial crisis period. We conjecture that
the monetary policy contractions are the main sources of liquidity shock during
normal periods, whereas international nancial turmoil dominates eased
monetary policy to relieve the nancial stress that banks faced during the
2008-2009 crisis period. For this reason, we distinguish the two periods, but
our ndings from these di erent periods provides consistent evidence in a
complementary way. During normal periods, our nding supports that Asian
global banks have hampering e ects against monetary policy, while they shield
their loan supply better and keep their loan price lower than other types of
banks in face of nancial turmoil in 2008 and 2009.
The remainder of this paper proceeds as follows. In Section 1.2, we introduce
the previous studies on the bank lending channels and recent research
that focuses on the global banking network. Section 1.3 investigates the structure
of the banking industry in the selected Asian countries and the features
of the Asian global banks. In Section 1.4, we explain the construction of the
dataset used. We suggest our identi cation strategies and estimation results
in Section 1.5. Section 1.6 concludes.
1.2 Relevant studies
In Section 1.2.1, we introduce the theory of the bank-lending channel and the
relevant literature as one of the monetary policy transmission mechanisms.
In Section 1.2.2, we review the recent literature that examines the role of
the globalized bank network, which we hypothesize may be regarded as new
bank characteristics that possibly a ect the e ectiveness of the bank lending
channel, as well as the cross-border shock propagation.
1.2.1 Bank lending channel: Theory and relevant evidence
Bank lending channel is one of monetary policy transmission mechanisms in
which monetary policy is transmitted through the banking system.9
9The other two channels through which monetary policy is transmitted via banking
system are traditional interest rate channel and broad credit channel (Bernanke and Gertler
1995, Mishkin 1996, Peek and Rosengren 2013).
1.2 Relevant studies
This channel activates as follows (Bernanke and Gertler 1995, Kashyap
and Stein 2000, Peek and Rosengren 2013). When available bank reserves
reduce due to monetary policy tightening since central banks reduce liquidity
supply to the markets during a policy contraction either directly or indirectly
(in the case of an interest rate setting policy), banks can respond to this by
either replacing the reservable customer deposits with nonreservable debts, or
shrinking their assets such as loans and securities in order to keep total assets
in line with the reduced volume of funding liabilities, or more realistically
combining the two to some extent (Figure 1.1). If banks are unable or unwilling
to fully insulate their loan portfolio, this reduction in the availability of
bank loan stemming from loan-supply side decreases aggregate demand in the
economy, and overall economic activity slows down.
Figure 1.1: Procedure of bank lending channel in a simpli ed balance sheet
Cash / Reserves
Reservable deposits
External funds / Nonreservable debts
Net worth (equity capital)
In face of a contractionary monetary policy shock, banks lose their reservable de-
posits. To keep their balance sheet insulated from the policy tightening, the banks should
raise other components in liability sides such as nonreservable liabilities (e.g. time deposits)
or external nance. In the case where banks cannot substitute all of their losses in reservable
deposits towards other forms of nance, then the asset sides of their balance sheets must
shrink. Furthermore, if they cannot fully absorb the shock by reducing their liquid assets
such as securities, cash and reserves in asset sides, then they have to reduce their loan
supply, thus shrinking bank lending (Bernanke and Gertler 1995, Kashyap and Stein 2000,
Peek and Rosengren 2013).
The dependency of banks on the adjustment of loan might di er across
The traditional interest rate channel works in two stages: at rst, a contractionary mon-
etary policy lead to an increase in short-term policy rates, and this in turn cause a rise in
long-term interest rates based on term structure of yield curve. An increase in the long-term
interest rates raises the cost of capital, thereby causing a decline in investment spending,
and thus aggregate demand declines.
The broad credit channel (balance sheet e ect or nancial accelerator) focus on credit
market imperfections associated with asymmetric information and moral hazard problems,
and the magni cation, or propagation, of monetary policy shocks to frictions in the credit
markets (Bernanke, Gertler, and Gilchrist 1999). An increase in interest rates associated
with a tightening of monetary policy causes a deterioration in rm health, in terms of both
net income and net worth. The reduction in the collateral value of the rm's assets, in
turn, cause an increase in the external nance premium that must be paid by the rm for
all sources of external nance. This additional increase in the cost of borrowing causes a
reduction in aggregate demand.
1.2 Relevant studies
banks based on their intrinsic features. Empirical studies on bank lending
channel thus have investigated bank characteristics that might play a role
as a reasonable proxy for the degree of access to nonreservable liabilities or
external nance that possibly alleviates the impact of monetary policy tightening
on the reduction on bank lending. This is because the extent to which
banks adjust their loans instead of other components in their balance sheet
depends heavily on how well they can access external funds. Kashyap and
Stein (1995) hypothesized that smaller banks have more limited access to external
nance and thus their loan portfolio are more impacted by a tightening
of monetary policy. They found empirical evidence supporting the proposition
that small banks are more responsive (shrink their loan portfolios by
more) than large banks to a monetary policy tightening. They also found
that banks with large securities holdings have an option of adjusting to the
shrinkage of reservable deposits by selling some of their securities, and thus
less liquid banks are more responsive to monetary policy tightening (Kashyap
and Stein 2000). Kishan and Opiela (2000) used the capital-to-asset ratio
as the proxy for a bank's ability to raise external nance, nding that the
loan portfolios of well capitalized banks are less sensitive to monetary policy
shocks. Campello (2002) distinguished smaller banks based on whether the
bank is aliated with a large multibank holding company, and found that
the lending of small banks aliated with large multibank holding companies
react less to a tightening of monetary policy than does the lending of similar
sized standalone banks. In a similar context, Ashcraft (2006) found that
aliation with a multi-bank holding company gives a subsidiary bank better
access to external funds than otherwise similar stand-alone banks. Holod and
Peek (2007) utilized the distinction between publicly traded and non-publicly
traded banks to classify banks by the ease with which they can access external
funds. Considering recent developments in nancial markets, Loutskina
and Strahan (2009) found that growth in loan securitization, in particular the
expansion of the secondary mortgage market, has weakened the transmission
of monetary policy through the lending channel by increasing bank balance
sheet liquidity. Bluedorn, Bowdler and Koch (forthcoming) stressed that the
realized interest rate that is commonly used in the literature is endogenous
to expected future macroeconomic conditions, which are likely to exert separate
e ects on both loan demand and loan supply. They argued this might
cause a bias in the estimated impact conditional upon bank characteristics,
and provided a comparison of the heterogeneity in bank lending responses to
1.2 Relevant studies
an explicitly identi ed monetary policy measure (Romer and Romer 2004) and
the realized interest rate.
1.2.2 Global banks and monetary policy transmission
After the recent nancial crisis, many studies have been conducted on the
globalized banking system and its internal capital market between the parent
banks and their foreign subsidiaries.10
US evidence The empirical research aiming to nd supporting evidence for
the operational internal capital market between head oces and their foreign
subsidiaries has been led by studies of US-headquartered global banks, using
the regulatory banking database.11 Using US bank-level regulatory data from
1980 to 2005, Cetorelli and Goldberg (2012a) found that having global operations
insulates US global banks from changes in monetary policy, while banks
without global operations are a ected by US monetary policy to a larger extent
than previously found. They also found evidence that the internal capital
market between the head oces and their foreign subsidiaries plays a role,
by examining the internal transactions between them.12 In a separate study,
Cetorelli and Goldberg (2012c) illustrated that parent banks, when hit by
a funding shock, reallocate liquidity in the organization according to a locational
pecking order. Aliate locations that are important for the parent
bank revenue streams are relatively protected from the liquidity reallocations
in the organization, while traditional funding locations are more extensively
10One earlier study before the recent nancial crisis is Peek and Rosengren (1997, 2000).
They examined how the nancial crisis in Japan in the early 1990s a ected the lending of
the Japanese banks in the US.
11US global banks has been most actively studied. The reason for this might be that the
recent nancial crisis originated in the US, thus, the role of banks in the US during the crisis
and the recovery period attracted a lot of attention. In addition, there is an e ective number
of banks that could be categorized as global banks in the US. As of 2005 Q4, there exist
107 global banks in the US according to Cetorelli and Goldberg (2012a). In other advanced
countries, however, there is a small number of global banks, making it dicult to analyse
their behaviour in comparison to other types of banks. More speci cally, the number of
global banks, as of 2013, according to BankScope is: Canada (5), France (10), Germany (5),
Italy (6), Japan (7), and the United Kingdom (12).
12They de ned a bank as global in each period if it had foreign assets greater than zero,
thus identifying banks that have actual oces in foreign countries. They categorized banks
into four types: large domestic banks, large global banks, small banks aliated with a do-
mestic bank holding company and small banks aliated with a global bank holding company.
They measured the di erences in the responsiveness to the monetary policy indicators of the
di erent bank groups in separate regressions. They also found evidence supporting the ex-
istence of an active internal capital market by examining the aggregate value of the internal
transactions between the head oces and their foreign aliates (Net Due item in the US
regulatory databases so called call report).
1.2 Relevant studies
used to bu er shocks to the parent bank balance sheets. With a host country
perspective, Cetorelli and Goldberg (2012b) analysed the activity of foreign
banks in the US through the operations of their local branches. They found
that internal capital markets are at play in response to parent-bank funding
shocks, and this causes a sizeable impact on the branch lending supply. Correa
et al. (2014) found that the intra- rm borrowing in US global banks resulting
from liquidity management strategies across the global organizations serves
as a shock absorber and a ects the lending patterns to domestic and foreign
Non-US evidence Consistent empirical results are found in non-US region
or emerging countries. The literature on non-US countries examines the role
of the internal capital market and its consequences on domestic monetary
policy e ectiveness from the foreign banks perspective, not the domesticallyowned
global banks, as in the US-cases. Cetorelli and Goldberg (2011) argued
that the loan supply in the emerging markets could be signi cantly a ected
through a contraction in the local lending by domestic banks owned by foreign
banks. They analysed 17 country bank lending databases from 2007 to 2009,
and found that the loan supply in the emerging markets could be signi cantly
a ected through three separate channels: a contraction in the direct, crossborder
lending by foreign banks; a contraction in the local lending by the
foreign banks aliates in emerging markets; and a contraction in the loan
supply by domestic banks resulting from the funding shock to their balance
sheet induced by the decline in interbank, cross-border lending.
From data on the cross-border banking
ows of 25 advanced and emerging
market economies, Reinhardt and Riddiough (2014) found that parent banks
in an advanced-economy bene t from in
ow during episodes of heightened
global risk. It has been also suggested that the internal capital markets are
at play in foreign banks working in France (Bussire et al. 2014) and Poland
(Pawlowska et al. 2014). Case-studies for Canada, Germany, Ireland, Italy
and the UK are currently in progress as a joint research project.13 From the
emerging economies data of Asia, Latin America, and Central and Eastern
13Researchers from eleven central banks and the Federal Reserve Bank of New York
formed the IBRN (International Banking Research Network) to jointly analyse issues per-
taining to global banks. As a research agenda in 2013, they are currently studying how
funding shocks a ecting parent banks are transmitted into foreign countries through their
cross-border banking activities. The primary advantage of this cooperation is that each
researcher from each central bank utilizes their own regulatory database of the banks oper-
ations in its territory; they share standardized methodologies.
1.2 Relevant studies
Europe during the period from 1996 to 2003, Wu et al. (2011) found that
the bank lending channel in the emerging economies is declining in strength,
as foreign banks adjust their outstanding loan portfolios and interest rates to
a lesser extent than that of domestic banks in the face of a monetary policy
Jeon and Wu (2014) studied bank lending for seven Asian countries, focussing
on foreign-owned banks during the recent nancial crisis period. They
incorporate two dummy variables, one to distinguish foreign banks from domestic
banks, and the other to segregate the 2008-2009 nancial crisis periods
from the normal period, into an otherwise standard bank lending channel
model. They focus on the marginal responsiveness of foreign banks during the
crisis period and nd that foreign banks operating in seven Asian countries reduced
their bank lending signi cantly during the nancial crisis period. From
this nding, they postulate that this is due to the reallocation of funds from
foreign banks (subsidiaries) to their head oces through the internal capital
In their earlier study, Jeon et al. (2013) attempted to nd more direct
evidence supporting the existence of internal capital markets, using bank-level
data for 368 foreign subsidiaries of 68 multinational banks in 47 emerging
economies during the period of 1994-2008. They measured how parent banks0
nancial conditions a ect their subsidiaries0 dependence on their own funds in
expanding real loan growth. They nd that when parent banks (head oces)
are in a good nancial condition, the real loan growth of the subsidiaries
become less dependent on their own funds.
Our speci cations in this chapter broadly follow those of Jeon et al. (2013,
2014). The primary di erence to Jeon and Wu (2014) is that we distinguish
domestically-owned global banks (Parents), and measure asymmetric
response of Parents to monetary policy shocks both in normal periods from
2000 to 2007, and crisis periods with an extended periods from 2000 to 2014.
We also measure how a Parents0 nancial conditions a ect their subsidiaries0
(Juniors) lending, using the speci cation strategy adopted by Jeon et al.
(2013). The salient di erence to this reference is the scope of the analysis,
in that they analysed 368 foreign subsidiaries of 68 multinational banks in
47 emerging economies, while we only examine 26 Parents headquartered in
the eight Asian countries and their 82 foreign subsidiaries (Juniors). We will
discuss these in detail in Section 1.5.
1.3 Structure of the banking industry
1.3 Structure of the banking industry
We examine the primary features of the Parents (global banks headquartered
in the eight Asian countries) and investigate the relative size of their Juniors
(foreign subsidiaries aliated with Parents). The gures in this section were
constructed based on the annual bank-level data of the 271 commercial banks
of the eight selected Asian countries from BankScope, which we will discuss
in more detail in Section 1.4.
1.3.1 Banking industry structure and global banks
The most salient feature in the banking structure of the eight Asian countries
is that the banking industry is highly concentrated. It is dominated by a few
of the largest banks, in relation to asset size. Table 1.1 illustrates that the
largest 10% of banks in number occupy approximately one third to two thirds
of the total assets in the country. The upper half banks account for almost
90% of the total assets.
Table 1.1: Concentration of the banking industry
HHI1) Share of Asset2)
10th 25th 50th
Hong Kong 0.258 77.3 88.8 98.1
Indonesia 0.085 63.0 81.2 93.5
Korea 0.125 35.6 63.4 90.0
Malaysia 0.102 47.5 68.2 91.0
Philippines 0.104 46.8 69.2 91.8
Singapore 0.315 40.8 94.8 97.9
Taiwan 0.111 33.7 68.1 90.4
Thailand 0.053 33.4 63.7 86.2
Notes: 1) Her ndahl-Hirschman Index: HHI =
i=1 s2
i where,
si is market share of bank i operating in the country (si =
i=1 asseti), 2) Asset share of the largest 10th, 25th, 50th
percentile banks in size in each domestic banking sector, 3) As of
To investigate the banking structure in detail, we classify 271 commercial
banks operating in the eight Asian countries based on their ownership type as
below. Domestically-owned banks are classi ed either global banks or domestic
banks, based on whether they have at least one foreign subsidiary abroad.
Foreign banks are foreign-owned banks operating in the region. For the banks
operating in the eight Asian countries, we term global banks as Parents, the
1.3 Structure of the banking industry
foreign banks as Foreign banks, and the domestic banks as Domestic banks.
 Global bank: A domestically-owned bank that has at least one foreign
subsidiary abroad for which the head oce has 50% or higher
controlling equity
 Foreign bank: A bank owned by foreign banks or rms; the capital owned
by overseas banks or rms is higher than 50% of the capital
 Domestic bank: A domestically-owned bank that does not have a foreign
subsidiary abroad
According to this classi cation, Table 1.2 shows the composition of the
banking sector in numbers, total assets, and average asset per bank in years
2000 and 2010. The high degree of concentration in the banking sector, as
illustrated in Table 1.1, is driven by the Parents, as the Parents constitute
the largest banks in each country. More speci cally, Parents are a few in
number, but they occupy the majority of the banking sector in size. In 2010,
the Parents in the eight countries consisted of 26 out of the total 212 banks
(12.3%), but they occupied 41.2% of the total assets. The average assets of the
Parents is 83.4 billion US dollars, far exceeding those of the Domestic banks
(13.5 billion US$) and the Foreign banks (20.8 billion US$).
This contrast becomes more enlarged when we exclude Hong Kong, where
Foreign banks, not Parents, constitute the largest bank group, possibly due
to its historical background.14 With Hong Kong excluded, 24 Parents (13.3%)
occupy 53.7% of the total assets, while 57 Foreign banks (31.5%) take up only
10.1% of the total assets in seven out of the eight Asian countries investigated
in 2010. It is also clear that from 2000 to 2010, the di erence in asset shares
between Parents and Foreign banks enlarged. These results illustrate that
Foreign banks are still relatively small in the Asian region compared to the
regional domestic banks.
Consequently, we deduce that the Parents (Asian headquartered global
banks) in our sample constitute the largest banks in each country, with a significant
market share.15 This necessitates research in this area. If Foreign bank
14Hong Kong has a high penetration of foreign banks, both in number of banks and in
assets; only one domestic bank was ranked as one of the largest 10 banks. In Hong Kong,
the largest banks are either UK or China originated foreign banks; this might be due to its
close economic relationship with both countries, based on historic backgrounds.
15With the exceptions of Hong Kong, Taiwan and the Philippines, the biggest bank in
each country is a global bank (Parents). As explained previously, foreign banks constitute
the largest banking group in Hong Kong. The largest banks operating in Taiwan and the
1.3 Structure of the banking industry
Table 1.2: Structure of the banking industry
All countries1) Excluding HK2)
2000 2010 2000 2010
Number of banks
Parents 18 26 16 24
Domestic bank 133 105 125 100
Foreign bank 85 81 52 57
All banks 236 212 193 181
Share of assets (%)
Parents 25.0 41.2 32.2 53.7
Domestic bank 46.9 26.8 62.7 36.1
Foreign bank 28.0 31.9 5.2 10.1
All banks 100.0 100.0 100.0 100.0
Asset per bank3)
Parents 27.8 83.4 29.5 86.8
Domestic bank 7.1 13.5 7.3 14.0
Foreign bank 6.6 20.8 1.5 6.9
All banks 8.5 24.8 7.6 21.4
Notes: 1) Include all the eight selected Asian countries,
2) Seven countries with Hong Kong excluded, 3) billion US$,
4) See APPENDIX A.2 for detailed information on banking
industry structure by country and by year.
(foreign-owned banks) lending behaviour di ers from that of other banks, it
might not cause signi cant di erences, in that they control only a small portion
of the total assets. However, if the Parents have a di erent lending
practice, this could cause an important implication on the e ectiveness of the
monetary policy transmission and shock propagation via the banking sector.
1.3.2 Global banks and their foreign subsidiaries
In this section, we examine the composition of banks in the selected countries,
and information on the Juniors (foreign subsidiaries aliated with
Parents) more in detail. As explained previously, our primary regressions examine
banks operating in eight selected Asian countries, classi ed as Parents,
Foreign bank and Domestic bank. Though Juniors are not included in our
main speci cation model, it is necessary to pay careful attention to properly
identify the Juniors for several reasons.16 Firstly, our distinction of Parents
Philippines are domestic banks that do not have foreign aliates; however, global banks still
constitute the largest banks. In Taiwan, all four global banks rank in the top eight banks.
In the Philippines, all three global banks rank within top ve banks in size.
16In this sense, in our basic speci cation, only these three types matter; Junior does
not matter. In Section, however, we only utilize the observations of the 26 Parents
headquartered in the eight countries and their 82 Juniors operating in 19 countries; hence,
1.3 Structure of the banking industry
from other types of banks is based on the existence of the Juniors aliated
with them. Secondly, one of our strategies is based on the size of the Juniors,
since we conjecture that the extent that the Parents utilizes their internal capital
market with their Juniors can be proxied by the Juniors0 size. Thirdly,
we make use of pairs of 26 Parents and their corresponding 82 Juniors to
examine how nancial conditions of one side a ect bank lending of the other
side in Section 1.5.3.
Figure 1.2 illustrates the composition of the banking sector by type of
ownership. In the territories of the eight countries, there exists 26 Parents,
157 Domestic banks and 113 Foreign banks. These banks constitute the rst
dataset for examining the asymmetric responses in the lending behaviours of
these three di erent types of banks (Section 1.5.1 and Section 1.5.2).
If we move our focus to the Parents-Juniors context, 26 Parents have 82
Juniors, in total, operating in other economies. Each Parent has 3.2 Juniors,
on average, ranging from 1 to 7; these Juniors are located in 19 countries.17
This is our second dataset. We utilize the pairs of the Parents and their
Juniors to examine the more direct relationship between the nancial conditions,
on the one hand, and bank lending, on the other hand, in Section 1.5.3.
It is noteworthy to mention that among these 82 Juniors, 30 are located in
one of the other countries within the eight countries considered. These 30
banks are the Juniors in the parent subsidiary context in the second dataset;
at the same time, they are classi ed as Foreign banks in the host country
perspective in the rst dataset.
this is not necessarily the Asian region in terms of when we examine the relationship between
the bank lending of Juniors and their Parents nancial conditions. It is noteworthy to
state that 30 out of 113 Foreign banks are aliates of their Parents. Thus, they could be
classi ed as Juniors at the same time.
17By region, the majority of subsidiaries (63 out of 82) are located in other Asian countries,
while 8 and 1 are located in the European Union (EU) and the US, respectively. Among the
63 Asian subsidiaries, 30 are located in the 8 selected countries; thus, they are classi ed as
Foreign banks in the host economies. 33 Juniors are primarily located in China and the
southern Asian countries of Vietnam and Cambodia.
1.3 Structure of the banking industry
Figure 1.2: Composition of the banks based on their type of ownership
271 banks locating in the 8 countries 82 subsidiaries of the 26 Parents
Global banks (Parents) (26)
Juniors (82)
- operating in the 8 countries (30)
- operating outside the 8 countries (52)
Domestic banks (157)
Foreign banks (113)
- aliated with Parents in Asia (30)
- aliated with parent banks headquartered
in other region (83)
This diagram illustrates the scope of banks used in our analysis based on their type
of ownerships. One dataset examines 271 banks operating in the 8 selected Asia countries,
while the second dataset investigates 26 Parents banks and their 82 Juniors. Figures in
parenthesis is the number of banks.
 Banks in the 8 countries: 271 commercial banks in the eight Asian countries
(shaded area) constitute the rst dataset used throughout Section 1.5 (except for Section
1.5.3). We categorize these 271 banks based on the type of ownership into Parents (global
banks), Domestic (domestically-owned banks that do not have a foreign subsidiary) and
Foreign bank (foreign-owned banks). One caveat is that sum of the number of banks in
each of the sub-categories exceeds total number of banks (271), re
ecting that some of the
banks experienced a change in type during 2000-2014.
 26 Parents and their 82 Juniors: In Section 1.5.3, we build upon the second dataset.
This dataset consists of the 26 Parents and their 82 Juniors (foreign subsidiaries aliated
with the 26 Parents) operating in 19 countries. One caveat is that 30 out of these 82
Juniors are located in one of the 8 Asian countries (but not the country where its Parents
are located). Thus, they are included in both datasets (classi ed as Foreign bank in the
rst dataset).
Relative size of the subsidiaries As stressed previously, the most interesting
feature of the Juniors is their size. We think the most relevant characteristics
of foreign subsidiaries in regard with the magnitude of hampering
e ects that their parent banks have is their relative size. This is because the
larger the Juniors are, the more likely that Parents can utilize its internal
capital market to relieve the nancial stress they face. In this regard, we
examine the relative size of 82 Juniors compared to their Parents.
Figure 1.3 delivers information on the relative size of Juniors, as compared
to their Parents. At rst, Panel A provides the distribution of the relative
size of the Juniors based on 739 observations from 2000 to 2014. The x-axis
represents the relative size of a single Junior as a ratio to its Parents size
(total assets) in each year (= assetJ
j =assetP
i  100). The y-axis represents its
1.4 Data description
frequency. The distribution of the Juniors size is very left-skewed, with its
median 1.31% and mean 7.38% (Std Dev. 13.88, Min 0.00%, Max 114.57%).
This illustrates that an individual subsidiary is quite small, when compared
to its parent banks.
Secondly, we sum the size of the individual subsidiaries in Panel A across
its parent banks, to calculate the total size of all subsidiaries that each global
parents banks have in a speci c year (=
j=1 assetJ
j =assetP
i 100). Panel B
shows the distribution of this aggregated size, based on 326 bank-year observations
from 2000 to 2014.18 The X-axis represents the ratio and the Y-axis
represents the frequency. This distribution is left-skewed as well, with its median
of 1.99% and mean of 16.00% (Std Dev. 24.70, Min 0.00%, Max 114.57%).
This measure might be more relevant in that from the Parents0 perspective,
it could better represent the potential capacity of their Juniors to provide
enough sucient liquidity to help its Parents shield its loan in the face of
nancial stress.
Figure 1.3: Division of Parents, based on the size of their Juniors
Size of Juniors (%)
0 20 40 60 80 100 120
Panel (A)
Size of all Juniors (%)
0 20 40 60 80 100 120
Panel (B)
Notes: 1) In Panel (A) presents distribution of Juniors in its size. The x-axis rep-
resents the ratio of a single Juniors assets to its Parents assets for a speci c year
(= assetJ
j =assetP
i  100). The y-axis represents its frequency based on 739 bank-year
observations from 2000 to 2014 (Median 1.31%, Mean 7.38%, Std Dev. 13.88, Min 0.00%,
Max 114.57%).
2) Panel (B) presents Distribution of the Parents by the total assets of all the Juniors
that they have. The x-axis denotes the ratio of total assets of all Juniors aliated to each
Parents (=
j=1 assetJ
j =assetP
i  100) based on 326 bank-year observations from 2000 to
2014 (Median 1.99%, Mean 16.00%, Std Dev. 24.70, Min 0.00%, Max 114.57%).
1.4 Data description
We brie
y introduce the standard model typically adopted to investigate the
bank lending channel and what kind of indicators are included for the estima-
18In Section 1.5.2, we divide Parents into two sub-groups based on these statistics; (i)
Parents(1) that have relatively larger Juniors, and (ii) Parents(2) that have relatively
smaller Juniors.
1.4 Data description
tion of such a basic model. Then we explain how to construct the bank-level
and country-level dataset that will be used in our empirical analysis. Our
speci cation models, thought of as an extension of this basic model, will be
explained in more detail in Section 1.5.
1.4.1 Bank-level data
We used Bureau van Dijk0s BankScope database to construct the annual unbalanced
panel data for the bank lending and bank characteristics.19 BankScope
provides information from individual bank balance sheets and nancial statements
in a standard format.20 We only include commercial banks to exclude
the possible bias due to the di erent nature and business scope of the di erent
types of banks.21
The de nitions of the bank-level variables (two dependent variables and
ve bank characteristics) used are as follows (See APPENDIX A.1 for more
detail). We consider two dependent variables, real loan growth rate and real
loan interest rate, representing a change in the loan quantity and a change in
loan price in real terms, respectively. For the bank characteristic variables, we
include ve indicators that measure the size of the liquid asset, equity or total
asset, and the degree of riskiness, as well as the pro tability of the loans.
Dependent variables (bank lending):
Notes: See APPENDIX A.1 for detailed de nitions of each of these variables.
19Most of the literature on this topic has used the regulatory database or BIS cross-border
banking statistics. We do not utilize these sets of data, in that the former are not open to
the public and the latter provides limited information on country-level bank lending to the
public. The regulatory database has detailed information on individual bank characteristics
and management status, including internal transactions between the head oce and its
subsidiaries, allowing us to directly test the existence of internal capital markets and its
impacts on monetary policy transmissions (Cetorelli and Goldberg 2012a among others).
The BIS cross-border banking database provides the cross-border banking transactions in
aggregated level of its member countries. It possibly allows us to test the international
movement of liquidity in response to monetary policy at the country-level (Cetorelli and
Goldberg 2011, Reinhardt and Riddiough 2014).
20The overall quality of the BankScope database for its coverage for banks has been
assessed as good. Cunningham (2001) observes that in 15 of 19 emerging market economies,
banks listed in BankScope cover more than 90% of the total banking sector assets, with
1999 as a reference year. The countries examined in the paper embrace 6 countries out of 8
countries investigated in our study, with Hong Kong and Singapore excluded.
21BankScope classi es banks into 6 categories, based on specialization (except central
banks): commercial banks, saving banks, cooperative banks, real estate and mortgage banks,
investment banks and Islamic banks. As of 2014, commercial banks constituted 58.7% in
number (283 out of 482 banks), and 88.1% in total assets (7.6 out of 8.6 trillion US$).
1.4 Data description
 Real loan growth rate (%)
: A change in the loan quantity in real terms,
generated by de
ating nominal loan in the local
currency with the domestic price level (CPI).
 Real loan interest rate (%p)
(Loan rate)
: A measure of a change in the loan price, which
is the di erence in the ratio of the interest income
to the total earning assets.
Bank characteristics variables:
 Liquidity (%) : A ratio of liquid assets to total assets.
 Capitalization (%) : A ratio of equity to total assets.
 Size (log) : A log of the total assets measured in US dollars.
 Riskiness (%) : A ratio of the loan loss provision to total loans.
 Pro tability (%) : A ratio of the net income to total assets (ROAA).
To eliminate the impacts of abnormal observations, we excluded the highest
and lowest 1 percentile of the growth rates of the assets and loans in real terms.
We also excluded the highest and lowest 1 percentile of the bank characteristics,
such as the liquidity, capitalization and riskiness.22 After this procedure
was conducted, we had an unbalanced dataset, consisting of 2,613 bank-year
observations for 271 banks with 314 bank-year observations removed.
Table 1.3 illustrates the descriptive statistics of the bank lending variables
and the bank characteristics by bank type group. Compared to the other types
of banks, Parents have less liquid assets and equity, measured as a ratio to
total assets. In size, measured in terms of assets, Parents are the largest,
followed by the Domestic banks and Foreign banks. Parents have the most
loan loss provisions, measured as a ratio to total assets. These characteristics
of Parents, as compared to Domestic banks and Foreign banks, necessitate
controlling for these factors to partial out the e ects of these bank characteristics,
and thus, isolating the e ect of bank ownership on bank lending.
In addition, we examine the correlation structure among the bank-level
22We also exclude observations when the equity is negative, in that it does not represent
normal business conditions. We do not remove the 1st and 99th percentile of the ROAA
(pro tability), considering that, unlike other characteristics, it has quite a concentrated
distribution. In the case of size, we drop the outliers of the growth rate of real total as-
sets (de
ated by CPI). This is to exclude bank-year observations with rapid expansions or
shrinkages, possibly due to the M&A (mergers and acquisitions), or other unusual managerial
reasons, though we include the size of the bank (in level, not in growth rate) in the bank-
characteristic variable. After this procedure, we lose 203 bank-year observations (for each
variable to which the top 1% and bottom 1% removal rule is applied, about 32 observations
are removed; 8 observations are removed where capital is negative. Observations that do not
have full bank-level variable sets are also excluded.). We examine the alternative methods
of dealing with the outliers in Section 1.5.4 Robustness tests. The results, when none of the
outliers are excluded, will be presented, as well.
1.4 Data description
Table 1.3: Descriptive statistics
Type of Banks
All Parents Domestic Foreign
banks banks banks banks
Observations 2613 319 1392 902
Groups2) 271 26 157 113
lnAsset3) (%) 6.99 7.10 7.11 6.78
lnLoan (%) 8.22 8.56 8.05 8.35
Loan rate (% point) -0.31 -0.28 -0.27 -0.39
Liquidity (%) 22.01 17.89 19.32 27.61
Capitalization (%) 11.24 8.47 10.00 14.12
Size3) (log) 8.50 10.67 8.40 7.90
Riskiness (%) 1.05 1.09 1.13 0.91
ROAA (%) 0.98 0.94 0.74 1.36
Notes: 1) Figures are mean value of total 2,613 bank-year observations.
2) Number of sub-groups exceeds total number re
ecting that some banks
experience a change of type of bank ownership during 2000 to 2014. 3) As
size is dummy variable, there cannot be any size outliers. Instead we exclude
outliers relevant to size based on growth rates of real assets (lnAsset). This
is because we do not regard the largest and the smallest banks as outliers,
rather we regard a signi cant change in asset size as abnormal observations
possibly stemming from managerial changes such as M&A (mergers and ac-
quisitions). 4) See APPENDIX A.3 for further information on descriptive
statistics and pair-wise correlation coecients.
variables (the pairwise correlation matrix in APPENDIX A.3). If some of the
bank characteristics are highly correlated and included in the estimation model
simultaneously, this might generate a problem of multicollinearity. We con rm
that some of the bank characteristics are statistically signi cantly correlated;
however, they do not have substantially high correlation coecients.
1.4.2 Country-level data
The country-speci c data consists of indicators representing monetary policy,
overall economic activity and banking sector structure. For an indicator of
monetary policy, we use annual di erence of the overnight interbank loan
rate, in that it re
ects the primary price of the short term funds for banks and
the overall changes in the monetary policy stance, regardless of the monetary
policy operational regimes that each country adopts.23 Among the total 2,613
bank-year observations from 2000 to 2014, 1,135 observations were related
23Among the eight countries, 6 countries (except for Hong Kong and Singapore) adopt
ation targeting as its monetary policy framework. Hong Kong and Singapore have ex-
change rate targeting as their monetary policy scheme; hence, they adjust their money base
and their domestic interest rate, to maintain the exchange rate as relatively constant within
a predetermined band.
1.5 Identi cation strategies and estimation results
to policy tightening (MP > 0), while 1,413 corresponded to policy easing
(MP < 0). For 65 observations, the monetary policy is unchanged (MP = 0).
As a measure of the overall economic activity in the country, the growth
rate of real GDP and the unemployment rate are adopted. For indicators of
the banking sector conditions, the nancial depth and HHI are included.24
Financial depth, a measure of nancial resources provided to the private sector,
are measured as a ratio of the bank credit to the private sector to GDP.
The degree of concentration, and thus, competitiveness in the banking sector
is measured by the HHI (Her ndal-Hirschman index), calculated as the
sum of the squares of each bank0s share of assets in the banking sector of the
countries considered. We calculate this index using the bank-level data from
BankScope. The macroeconomic conditions and banking sector indicators
for the eight countries were collected from the International Financial Statistics
(IFS) of the International Monetary Fund (IMF), except for the nancial
depth, which was provided by the World Bank database.
1.5 Identi cation strategies and estimation results
This section consists of three sub-sections. In Section 1.5.1, we compare the
responsiveness of the bank lending of di erent types of banks to monetary
policy changes. We nd that Parents (global banks headquartered in the 8
Asian countries) have a hampering e ect on the domestic monetary policy, as
they were less a ected by the monetary policy during the normal periods from
2000 to 2007.
The remaining sub-sections aim to determine indirect evidence supporting
the relevance of the operational internal capital markets on the hampering
e ects. In Section, we compare Parents that have relatively larger
Juniors with those that have smaller Juniors. In Section, we include
all observations available from 2000 to 2014 and examine the bahaviour of
bank lending with a focus of 2008-2009 crisis periods. Lastly, we examine
whether the nancial conditions of the Parents a ect the loan growth of their
Juniors in Section 1.5.3. Section 1.5.4 provides results of robustness tests.
24Though these are not indicators that are commonly included in

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1 Global banks and monetary policy transmission 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Relevant studies . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.1 Bank lending channel: Theory and relevant evidence . . 7
1.2.2 Global banks and monetary policy transmission . . . . . 10
1.3 Structure of the banking industry . . . . . . . . . . . . . . . . . 13
1.3.1 Banking industry structure and global banks . . . . . . 13
1.3.2 Global banks and their foreign subsidiaries . . . . . . . 15
1.4 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.4.1 Bank-level data . . . . . . . . . . . . . . . . . . . . . . . 19
1.4.2 Country-level data . . . . . . . . . . . . . . . . . . . . . 21
1.5 Identi cation strategies and estimation results . . . . . . . . . . 22
1.5.1 Hampering e ect of global banks on monetary policy . . 23
1.5.2 Division of Parents into two subgroups . . . . . . . . . . 30 Normal periods, Year 2000-2007 . . . . . . . . 30 Financial crisis and recent years . . . . . . . . 34
1.5.3 Interdependence on nancial conditions . . . . . . . . . 40
1.5.4 Robustness tests . . . . . . . . . . . . . . . . . . . . . . 47
1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2 US monetary policy and global banking
ows 56
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.2 Relevant studies . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.3 Identi cation of monetary policy shocks . . . . . . . . . . . . . 63
2.3.1 Intended change in Federal Funds rate . . . . . . . . . . 63
2.3.2 Removal of endogeneity in the Federal Funds rate . . . 63
2.3.3 Frequency transformation . . . . . . . . . . . . . . . . . 65
2.4 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . 68
2.5 Evidence from VARs . . . . . . . . . . . . . . . . . . . . . . . . 70
2.5.1 Structural VAR: Replication of Bruno and Shin . . . . . 71
2.5.2 VAR with exogenously identi ed shock . . . . . . . . . . 73
2.5.3 Banking out
ows across countries . . . . . . . . . . . . . 77 Banking Flows: AEs and EMEs . . . . . . . . 78 Country-speci c bilateral VARs . . . . . . . . 79
2.6 Robustness tests . . . . . . . . . . . . . . . . . . . . . . . . . . 84
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
3 Exchange rate regimes and sudden banking out
ows 90
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.2 Empirical evidence . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.2.1 Identi cation of US the monetary policy shock . . . . . 94
3.2.2 Responses of local variables to US monetary policy shock 95
3.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.3.1 Households . . . . . . . . . . . . . . . . . . . . . . . . . 98
3.3.2 Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.3.3 Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.3.4 Features of a small open economy . . . . . . . . . . . . 104
3.3.5 Monetary policy regime and equilibrium . . . . . . . . . 105
3.3.6 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 106
3.4 Exchange rate regimes and banking out
ows . . . . . . . . . . . 108
3.4.1 Baseline simulation . . . . . . . . . . . . . . . . . . . . . 108
3.4.2 Counterfactual: Alternative exchange rate policy . . . . 110
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
A APPENDIX: Chapter 1 117
A.1 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . 118
A.2 Structure of the banking industry . . . . . . . . . . . . . . . . 119
A.3 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . 120
A.4 List of Parents and the relevant information . . . . . . . . . . . 121
B APPENDIX: Chapter 2 123
B.1 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . 124
B.2 Leverage of US global banks . . . . . . . . . . . . . . . . . . . . 125
C APPENDIX: Chapter 3 126
C.1 Banking
ows and interest rates . . . . . . . . . . . . . . . . . 126
C.2 Monetary policy shock identi cation . . . . . . . . . . . . . . . 127
C.3 Banking
ows by country . . . . . . . . . . . . . . . . . . . . . 128
C.4 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . 128
C.5 Estimation methodology . . . . . . . . . . . . . . . . . . . . . . 129


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