Managing currency risk with foreign operations: …...Managing currency risk with foreign...
Transcript of Managing currency risk with foreign operations: …...Managing currency risk with foreign...
Managing currency risk with foreign operations:
Evidence from international banks
Karen Y. Jang
Florida International University ([email protected])
Minho Wang
Florida International University ([email protected])
This draft: September 2019
Abstract: We provide novel evidence that multinational firms can reap the benefits of corporate
international diversification by managing currency risk through foreign operations. Using the data of
international banks and employing a newer technique to capture non-diversifiable currency risk, we find
that while changes in exchange rates affect the performance of all international banks, those with foreign
operations in the U.S. have better stock performance (i.e., a higher stock return and the Sharpe ratio but a
lower stock return volatility) when highly sensitive to downside currency risk. The strategies employed at
foreign banking branches offer an explanation: international banks, when highly sensitive to extreme,
negative currency movement, increase asset exposure in the foreign market by accelerating their asset and
business loan growth and transfer the interest rate risk associated with business loans to borrowers. Their
asset expansion is mainly funded by increased borrowings in U.S. market, which is especially valuable
when the negative currency risk corresponds to the extreme depreciation in local currencies.
JEL Classification: F31, G21, G28
Key Words: Foreign exchange, Currency risk management, Foreign operations, International banks
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1. Introduction
Theories on corporate international diversification say that in the presence of barriers to portfolio
capital flows, multinational corporations (MNCs) have advantages relative to single-country firms (Agmon
and Lessard 1997). As MNCs invest in establishing foreign subsidiaries and control the operations, they
can exert flexibility in shifting revenue-producing resources among its global operating units and enjoy the
advantage of less risk in profits than does a similar sized single-nation firm (Rugman 1976). But a line of
literature shows that multinational firms’ ability to lower the risk through diversification effects can be
empirically challenged. For example, Reeb, Kwok and Baek (1998) find increased risks at MNCs and argue
that a higher cash flow volatility resulting from internationalization might dominate theoretically-motivated
diversification benefits. The sources of cash flow volatility seem to mostly generate from exchange rate
uncertainty: Eun and Resnick (1988) build a model where exchange rate uncertainty is a largely
nondiversifiable factor, which adversely affects the performance of international portfolios and Bartov,
Bodnar and Kaul (1996) find that a significant increase in volatility of stock returns is associated with
exchange rate variability.
In this research, we offer new evidence that multinational corporations can reap the benefits of
corporate international diversification by leveraging risk-return opportunities through foreign operations.
We focus on the currency risk because the major source of the observed higher volatility at internationally
diversified corporations is foreign exchange risk. We also focus on one specific industry - international
banks and its one specific foreign operations - the U.S. credit market. There are distinct advantages to using
this industry and its operational presence in the U.S. as a laboratory for studying whether and how corporate
international diversification through foreign operations is associated with the way currency risk
management affects firm value. First, we can distinguish international firms with foreign operations in the
U.S. from those without one in our data, which allows us to construct a sample of treatment and control
groups. Second, all non-U.S. international banks with U.S. operations, regardless of the origin of the
country, face the same restrictions on permissible lines of business and face the same prohibitions on the
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use of financing. These close similarities reduce firm heterogeneity to a degree unlikely to be found in
other single-industry studies, and certainly not to be found in a multi-industry study. Third, these non-U.S.
banks have a significant presence in the U.S., with the aggregate assets they hold surpassing $ 2.5 trillion
in 2017:Q4. This indicates that while we focus only on one specific foreign business location, it is one of
the most important overseas markets for international banks. Fourth, branch banking is the most popular
way for international banks to expand operations in the U.S and they are required to file FFIEC 002, which
is called “Report of Assets and Liabilities of U.S. Branches and Agencies of Foreign Banks”. While this
report does not contain the financial information on the branch-level profitability, it shows their asset- and
liability-side activities at the U.S. branches, which clearly helps us observe corporate behavior at foreign
operations in the context of foreign exchange risk. Fifth, unlike nonfinancial industries, banks’ behavior is
closely tied to financial stability, business cycle fluctuations and economic growth. Especially, how
international banks behave with respect to currency risk matters to policymakers considering that non-U.S.
banks provide important economic benefits to the U.S. economy. So studying with a tighter focus on this
industry goes beyond the limitations of a single-industry analysis.
We first show that international banks regardless of the physical presence in the U.S. have a
significant amount of foreign exchange exposure and not surprisingly, international banks with U.S.
operations have a relatively higher responsiveness to foreign exchange movements. The overall economic
exposure to currency risk, measured by the sensitivity of stock returns to changes in exchange rates between
the USD and a local currency, is 2.86 times higher for international banks with U.S. operations than for
those without one. The difference in the estimated sensitivity of stock market returns to the dollar factor
(i.e., the average change in the exchange rates between the USD and all other currencies) between two
groups is a lot smaller – only 1.65 times larger at international banks with U.S. operations, which confirms
that exchange rates can affect the performance of nearly all firms in the economy (Bartov, Bodnar, and
Kaul 1996). Next, we examine how these measures of sensitivity to currency risk relate to stock
performance, finding that sensitivities to currency risk are in general positively related to the volatility of
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stock return but negatively related to the stock return and the risk-adjusted return. But we find suggestive
evidence that the foreign operations of international banks are fairly effective in mitigating the negative
impact of foreign currency exposure, which leads us to more rigorously test the role of foreign operations
in exploiting currency risk-return opportunities.
To deepen our line of inquiry, we focus on a largely nondiversifiable factor of currency risk.
Previous studies find that movements in foreign exchange rates show extreme discontinuous changes (i.e.,
jumps) and these jumps are nondiversifiable risks priced in currency markets. So we follow a statistical
approach of Lee and Wang (2018) and decompose the currency price changes into continuous and
discontinuous price movements through the technical process of “jump detection”. And with respect to
discontinuous price changes, we also consider the direction – negative or positive ones. Then, we estimate
the sensitivities of an individual currency to continuous, discontinuous positive, and discontinuous negative
price movements. So the decomposition of movements in foreign exchange rates generates “continuous”,
“positive jump” and “negative jump” betas. Our main focus is on negative jump betas because a higher
sensitivity to negative, discontinuous movements (i.e., the extreme depreciation in local currencies)
represents higher currency risks. We think this decomposition is essential for revealing corporate behaviors
with respect to foreign exchange risk.
With a sample of 452 global banks in 48 countries from 1997 through 2017, we formally test
whether and how corporate foreign operations capitalize on currency risk-return opportunities. Consistent
with the previous studies showing that a higher sensitivity of an individual currency to extreme
discontinuous changes is priced, we find the positive risk premium on negative jump betas. More
importantly, we provide evidence that the partial effect of currency risk depends on the existence of foreign
operations: the operations of U.S. branches further increase the stock return, when international banks are
exposed to a high sensitivity to negative country-specific currency risks, without raising additional volatility
of stock return, which produces sizable improvements in the Sharpe ratio, our measure of a risk-adjusted
return.
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Because of the endogenous nature of corporate decision on the establishment of foreign operations,
we do not rule out the possibility that other factors could also affect the stock performance. An ideal
empirical strategy would randomly assign a bank’s foreign operations but we do not have a randomized
experiment. Instead, unlike previous studies, we can observe what actions international banks take at
foreign operations in response to currency risks, which helps us explain why we find additional risk-
adjusted returns at international banks with foreign operations in the U.S. So we utilize the U.S. branch
activities data compiled by the regulators and look into what actions those international banks take. In
particular, we investigate what strategies they employ for credit extension and capital funding in relation to
currency risk-return trade-offs. International banks, when highly responsive to downside currency risk,
accelerate their asset growth at U.S. branches, and this faster asset expansion comes mainly with the growth
in profitable business loans. We further document that when they increase their commercial & industrial
loan growth in response to extreme, downside currency risk, international banks try to transfer the interest
rate risk associated with business loans to borrowers by increasing only a supply of floating-rate business
loans. We do not find any effect of negative jump beta risks on real estate loans, whose rates are generally
fixed, nor on fixed-rate business loans. This conservative management of interest rate risk seems to be
sensible when international banks ride out the negative, downside currency risk, which helps coordinate
various risks they are exposed to.
Next, we see how international banks pursue their funding strategies at foreign operations in
response to the currency risk and find a higher currency risk accelerates international banks’ liability
growth. Along with asset growth and commercial & industrial loan growth at international banks in
response to the extreme, negative currency risk, a flexible liability expansion at foreign markets seems to
help them take advantage of the downside currency risk. Obviously, the marginal value of the borrowings
those foreign operations can tap in the U.S. market should be greater because the negative currency risk
corresponds to the extreme depreciation in individual currencies.
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Our study closely relates to two strands of the previous literature: (1) the studies on the relation
between corporate international diversification and firm value enhancement and (2) those on the economic
implications of foreign exchange risk. First, it is rooted in the literature about the corporate international
diversification and its implication on firm value. Building on international portfolio theory, Hughes, Logue
and Sweeney (1975), Agmon and Lessard (1977), Amihud and Lev (1981), and Michel and Shaked (1986)
document risk reduction through geographical diversification of cash flows at multinational firms. But this
line of literature has been somewhat challenged by the new evidence of a higher risk at MNCs. Reeb, Kwok
and Baek (1998) find a positive relation between the systematic risk and corporate internationalization and
Berger, El Ghoul, Guedhami, and Roman (2017) show that internationalization increases banks’ risk due
to market-specific factors in foreign markets. Meanwhile, a line of literature that studies why corporate
international diversification observes a higher risk finds that the foreign exchange rate is a source of the
increased risk. Eun and Resnick (1988) argue that as exchange rate uncertainty is a largely nondiversifiable
factor adversely affecting the performance of international portfolios, it is essential to effectively control
exchange rate volatility. Bartov, Bodnar and Kaul (1996) also find a positive relation between exchange
rate variability and stock return volatility at U.S. multinational firms using the breakdown of the Bretton
Woods system as an empirical setting.
Our study is also related to currency risk and its implications. There is a long line of literature
studying how currency risk is priced and whether it should be a large component of the cost of equity.
While Jorion (1991) argues that currency risk is not significantly priced, Francis, Hasan, and Hunter (2008)
show that all industries in the U.S. have a significant currency premium, which accounts for about 11.7
percent of total risk premium in absolute value. More recent studies (Chernov, Gravelin and Zviadadze
2018; Farhi and Gabaix 2016; Jurek 2014; Lee and Wang 2018) focus on the sources of currency risk, and
argue that extreme, discontinuous changes in foreign exchange rates are undiversifiable and hence priced
in currency markets.
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Building on these studies, we make a novel contribution. Surprisingly few studies thus far have
examined how multinational corporations in fact utilize their foreign operations to achieve the benefits of
corporate international diversification in the face of foreign market-specific risks such as foreign exchange
risk or political risk. The void is glaring considering that multinational corporations are said to leverage
their global operations to boost their profit margins and create shareholder value. In this study, we examine,
with detailed foreign branch-level data, what strategies international banks employ at their foreign
operations to create firm value when they manage their major risk - the currency risk.
The rest of the paper is organized as follows. Section 2 describes the data sources and Section 3
shows foreign currency exposure at international banks and explores its relation to stock performance.
Section 4 explains the statistical technique we use to capture nondiversifiable currency risk – the process
of jump detection and beta estimation. Section 5 presents our findings on the role of foreign operations in
the relation between currency risk and stock performance. Section 6 examines how international banks
utilize their foreign operations in the U.S. for currency risk management by investigating their asset-side
and liability-side activities. Section 7 concludes our study with a discussion of policy implications.
2. Data sources
Our main data source is Thomson Reuters Datastream. This database contains daily stock prices,
trading volumes, return indices, and daily exchange rates in nearly 200 countries around the world. We
find 452 publicly-traded, non-U.S. international banks in 48 countries after filtering out banks in the
countries whose exchange rates are strictly pegged to the USD. In order to see which international banks
have foreign operations in the U.S., we resort to file FFIEC 002, “Report of Assets and Liabilities of U.S.
Branches and Agencies of Foreign Banks”. This report is merged with “Structure and Share Data for U.S.
Banking Offices of Foreign Entities” to identify the home country of the international banks.1 This merged
1 https://www.federalreserve.gov/releases/iba/
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dataset provides a comprehensive list of foreign-owned banking offices in the U.S. Then, with a matching
process by bank name, we distinguish non-U.S., international banks with U.S foreign operations from all
other banks in our Datastream sample. We have 106 international banks with U.S branch operations and
346 banks without one in this data and with this sample of treatment and control groups, we study the
relation between currency risk and firm value. The summary statistics of all international banks are reported
in Table 1. When we look into the international banks’ investment and funding behavior at foreign
operations, we include both publicly-traded and privately held international banks and study at foreign
branch level. We have 491 foreign banking branches. Lastly, we note that as “Structure and Share Data
for U.S. Banking Offices of Foreign Entities” has been publicly available from 1997 Q1, our sample period
spans from 1997:Q1 to 2017:Q4.
3. Foreign currency exposure of international banks
In this section, we first explore the economic exposure of international banks to foreign exchange
rates. Changes in exchange rates can affect not only firms that are engaged in foreign operations but also
purely domestic firms because (1) those purely domestic firms can have contractual exposure (e.g., they
make dollar-denominated loans in their domestic market and raise dollar funding (Ivashina, Scharstein and
Stein 2015)) and (2) their competitive position can be affected in their domestic marketplace where they
face international competition. To measure the overall economic exposure to currency risk, we estimate
the sensitivity of each firm’s stock returns to changes in exchanges rates between the USD and a local
currency. Specifically, we estimate the following regression with a sample of 452 banks for each quarter:
𝑟𝑒𝑡𝑖,𝑡 = 𝛼𝑖 + 𝛽𝑖 ∗ ∆𝑆𝑖,𝑡 + 휀𝑖,𝑡 (1)
where 𝑟𝑒𝑡𝑖,𝑡 is the stock return of bank i at day t and ∆𝑆𝑖,𝑡 is the change in exchange rates between the USD
and the local currency for bank i at day t. Foreign exchange rates are expressed in the USD per local
currency. The coefficient 𝛽𝑖 measures the overall economic exposure of bank i for each quarter. We take
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the average of the economic exposure across international banks for each quarter and then report the time-
series average. Table 2 Panel A shows that international banks regardless of the existence of U.S operations
have significant economic exposure to exchange rate movements and not surprisingly, the exposure is 2.86
times higher for international banks with U.S. operations than those without one.
As currency rate movements can come either from value changes in the USD or from value changes
in the local currency, we further break the exchange rate movements into two components – the dollar factor
and the local factor and measure the sensitivities of stock market returns to each factor. Specifically, we
estimate the following equation:
𝑟𝑒𝑡𝑖,𝑡 = 𝛼𝑖𝐷𝑜𝑙𝑙𝑎𝑟 + 𝛽𝑖
𝐷𝑜𝑙𝑙𝑎𝑟𝐷𝑜𝑙𝑙𝑎𝑟𝑡 + 휀𝑖,𝑡𝐷𝑜𝑙𝑙𝑎𝑟 (2)
where 𝐷𝑜𝑙𝑙𝑎𝑟𝑡 is the average change in the exchange rates between the USD and all other currencies at day
t. We call the coefficient 𝛽𝑖𝐷𝑜𝑙𝑙𝑎𝑟 “dollar factor exposure”. The dollar factor is well-rooted in the literature
on currency risks (e.g., Lustig, Roussanov and Verdelhan 2011 and Menkhoff, Sarno, Schmeling and
Schrimpf 2012). Also, to measure the sensitivity to the local factor, we use the residuals of the regression,
∆𝑆𝑖,𝑡 = 𝑎𝑖 + 𝑏𝑖 ∗ 𝐷𝑜𝑙𝑙𝑎𝑟𝑡 + 𝑒𝑖,𝑡 and estimate the following equation:
𝑟𝑒𝑡𝑖,𝑡 = 𝛼𝑖𝐿𝑜𝑐𝑎𝑙 + 𝛽𝑖
𝐿𝑜𝑐𝑎𝑙𝑒𝑖,𝑡 + 휀𝑖,𝑡𝐿𝑜𝑐𝑎𝑙 (3).
Then, we call the coefficient 𝛽𝑖𝐿𝑜𝑐𝑎𝑙 “local factor exposure”. Panel B in Table 2 shows that both groups of
international banks are greatly affected by the dollar factor, but the estimated sensitivity of stock market
returns to the dollar factor at international banks with the U.S. operations is on average only 1.65 times
larger. On the other hand, the sensitivity to the local factor between these two groups displays a large
difference as shown in Panel C.
Next, we examine how these measures of sensitivity to currency risk relate to stock market
performance and whether the foreign operations in the U.S. play any role in the impact of foreign currency
exposure on stock return and volatility. So we estimate the following equation:
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𝑌𝑖,𝑐,𝑞 = 𝜑0 + 𝜑1𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦𝑖,𝑐,𝑞 + 𝜑2𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦𝑖,𝑐,𝑞 ∗ 𝐼(𝐹𝑂)𝑖,𝑐,𝑞 + 𝜑3′ 𝑋𝑖,𝑐,𝑞 + 𝛿𝑞 + 휁𝑐 + 𝜖𝑖,𝑐,𝑞 (4)
where 𝑌𝑖,𝑐,𝑞 is the stock return, volatility of stock return or Sharpe ratio for bank i in country c at quarter q
and 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦𝑖,𝑐,𝑞 is either the economic exposure measured with equation (1) or the dollar factor
exposure estimated with equation (2) for bank i in country c at quarter q. 𝐼(𝐹𝑂)𝑖,𝑐,𝑞 is an indicator variable
that equals one if a bank i in country c has foreign operations in the U.S. and equals zero otherwise and
𝑋𝑖,𝑐,𝑞 is a set of control variables that include market value and total revenue for bank i in country c at
quarter q. We saturate our model with country and time fixed effects.
Table 3 Panel A first shows the relation between economic exposure and stock performance. A
higher economic exposure to exchange rate variability is in general negatively related to stock returns and
risk-adjusted returns and positively related to stock return volatility. This confirms the findings of previous
studies that exchange rate exposure can negatively affect firm performance. But we find some suggestive
evidence on the role the foreign operations play in mitigating the negative effect of currency exposure: the
coefficients of the interaction term Economic expo.*I(FO) flip the signs of coefficients on the variable
Economic expo. across the board. Panel B where we replace the variable Economic expo. with the variable
Dollar expo. finds similar results. Also, it informs us that the explanatory power of our model increases
with a focus on the dollar factor, i.e., the market, systematic component. We think these results are
interesting but could be considered merely suggestive because (1) the test variable Economic Expo. we use
in this regression framework might contain the non-market, diversifiable component and the market, non-
diversifiable component and (2) both variables Economic expo. and Dollar expo. do not detect the
discontinuous, jump component, which truly represents the currency risk. So in the next section, we explain
how we capture a largely nondiversifiable factor of currency risk through the process of jump detection and
beta estimation.
4. Capturing currency risk: the process of jump detection and beta estimation
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As explained earlier, foreign exchange rate dynamics have many components and previous studies
suggest the dollar risk factor explains approximately 70 percent of the variance and corresponds to the
market (USD), undiversifiable component. In addition, focusing on the dollar risk factor is in line with the
aim of our research that studies how the foreign operations in the U.S. help international banks manage
currency risk.
The technical process can be summarized in a following way: we first measure the overall
sensitivity of an individual currency to the market – a standard beta. Then, to capture continuous vs.
discontinuous price movements, we decompose the standard beta through the jump detection process into
continuous and discontinuous betas. Further, with respect to discontinuous price changes, we consider the
direction, estimating positive jump and negative jump betas. So this statistical process decomposes the
standard betas into continuous, positive jump and negative jump betas. Again, the breakdown of the
standard beta is rooted in the prior studies that show individual exchange rates respond to the separated
market components with different magnitudes and the extreme, discontinuous movements in foreign
exchange rates are undiversifiable and priced in currency markets (Chernov, Gravelin, and Zviadadze 2018;
Farhi and Gabaix 2016; Jurek 2014; Lee and Wang 2019). Therefore, we think a decomposition process of
the standard beta would be essential for revealing corporate behavior with respect to foreign exchange risk.
More technically, following Lee and Wang (2018), we estimate, with the daily exchange rate data,
quarterly decomposed betas using the equation (5):
�̂�𝑖,𝑝(𝑞)
=𝛴𝑙∈𝑃𝑝
𝑟𝑖,𝑡(𝑙)𝑟0,𝑡(𝑙) ⋅ 𝐼(𝑞)𝑖,𝑡(𝑙)𝐼(𝑞)0,𝑡(𝑙)
𝛴𝑙∈𝑃𝑝𝑟0,𝑡(𝑙)
2 ⋅ 𝐼(𝑞)0,𝑡(𝑙) (5)
where �̂�𝑖,𝑝(𝑞)
is the sensitivity of exchange rate 𝑖 to decomposed dollar factor q over the p-th quarter. 𝑞 =
{𝑐, 𝑗+, 𝑗−} is a notation to distinguish three different decomposed betas (i.e., “c,” “j+,” and “j-”) indicate
“continuous,” “positive jump,” and “negative jump,” respectively. 𝑃𝑝 = {𝑙|𝑡(𝑙) belongs to the p-th quarter}
is the set of indexes for the p-th quarter. 𝑡(𝑙) is the l-th discrete observation if we define the whole-time
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horizon 0 = 𝑡(0) < 𝑡(1) < ⋯ < 𝑡(𝑛) = 𝑇, where N is the number of time-partitions such that 𝑃1 ∪ ⋯ ∪
𝑃𝑁 = {0, 1, ⋯ , 𝑛}. 𝑟𝑖,𝑡(𝑙) = ln 𝑆𝑖,𝑡(𝑙) − ln 𝑆𝑖,𝑡(𝑙−1) is a change in log spot rate from time t(l-1) to t(l), and
𝑟0,𝑡(𝑙) is the dollar factor, which is the average changes in log spot rate across exchange rates at time t(l).
𝐼(𝑞)𝑖,𝑡(𝑙) and 𝐼(𝑞)0,𝑡(𝑙)are indicators for jump arrivals. 𝐼(𝑐)0,𝑡(𝑙) (𝐼(𝑐)𝑖,𝑡(𝑙)) takes the value of unity if a
market jump (an individual jump for exchange rate i) does not occur from time t(l-1) to t(l) and zero
otherwise. 𝐼(𝐽 +)0,𝑡(𝑙) (𝐼(𝐽 −)0,𝑡(𝑙)) takes the value of unity if a positive (negative) market jump does
occur and zero otherwise. 𝐼(𝐽 +)𝑖,𝑡(𝑙) and 𝐼(𝐽 −)𝑖,𝑡(𝑙) take the value of unity for all l. These three betas are
decomposed of the standard beta, which is the sensitivity of individual currency to the dollar factor
(regardless of whether changes in exchange rates are continuous or discontinuous). So by setting all
indicators in equation (1) equal to unity, we can estimate the standard beta. As we have quarterly
observations of international banks’ financial data, we estimate the betas on a quarterly basis accordingly.
Meanwhile, in order to detect jumps, we employ the approach proposed by Lee and Mykland (2008)
and follow the application of Lee and Wang (2019). We identify approximately 10 percent of currency
returns as jumps at the 5 percent significance level. This jump percentage is relatively high because we use
the critical value based on the standard normal distribution to capture small-sized jumps. This modification
allows us to detect a greater number of jumps than the original approach in Lee and Mykland (2008), which
uses a critical value based on the Gumbel distribution. Such a large number of detected jumps are preferred
for consistent beta estimation, and Lee and Wang (2019) adopt the same technique. In addition, the number
of negative jumps tends to be greater than that of positive jumps because of South American currencies.
We present our jump detection results in Table 4 for all currencies in our sample. The beta estimation
results are reported in Table 5, where time-series averages of standard, continuous, positive jump and
negative jump betas for all local currencies are shown. The range of jump betas is greater than that of
standard and continuous betas, and this finding has two implications: (1) individual currencies are more
sensitive to unusual price changes with respect to value changes in U.S. currency (USD), and (2) the
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responses of individual currencies to such extreme value changes with respect to USD substantially differ
across currencies.
5. Role of foreign operations in the relation between currency risk and stock performance
In order to test whether and how corporate foreign operations help capitalize on the currency risk-
return opportunities, we estimate the following regression:
𝑌𝑖,𝑐,𝑞 = 𝜆0 + 𝜆1𝛽𝑐,𝑞−1𝐽−
+ 𝜆2𝛽𝑐,𝑞−1𝐽+
+ 𝜆3𝛽𝑐,𝑞−1𝐽−
∗ 𝐼(𝐹𝑂)𝑖,𝑐 + 𝜆4𝛽𝑐,𝑞−1𝐽+
∗ 𝐼(𝐹𝑂)𝑖,𝑐 + 𝜆5𝛽𝑐,𝑞−1𝐶 + 𝜆6
′ 𝑋𝑖,𝑐,𝑞
+ 𝛾𝑖 + 𝛿𝑞 + 휁𝑐 + 𝑢𝑖,𝑐,𝑞 (6)
where 𝛽𝑐,𝑞−1𝐽+
, 𝛽𝑐,𝑞−1𝐽−
, and 𝛽𝑐,𝑞−1𝐶 are estimated positive jump, negative jump and continuous betas,
respectively for an individual currency for country c in quarter q-1. We include bank, country and time
fixed effects. Our main interest is in the coefficient 𝜆3, which shows the effect of foreign operations on the
relation between nondiversifiable currency risk and stock performance.
Table 6 Panel A presents the results of the regression with the dependent variable, Stock return.
Consistent with the previous studies showing that a greater sensitivity of an individual currency to extreme
discontinuous changes in exchange rates is priced, we find the positive risk premium on negative jump
betas. Furthermore, the interaction term JNeg beta*I(FO) returns statistically significant and positive
coefficients as shown in columns [3] and [4]. Taking the coefficients at face value, we find the foreign
banking operations leverage the currency risk-return tradeoffs by 196 percent. Panel B shows the results
of the estimation of the equation (6) with dependent variable Stock return volatility. A higher sensitivity
to nondiversifiable currency risk increases stock return volatility as well because we find the positive
coefficients of the variable JNeg beta across the board. However, the interaction term JNeg beta*I(FO)
returns a negative coefficient in column [3] when we do not use bank fixed effects and it is statistically
insignificant when we do use bank fixed effects. It means the foreign operations, when highly sensitive to
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downside currency risk, do not additionally raise stock return volatility. The findings shown in Panels A
and B naturally lead us to look into the risk-adjusted return, so we estimate the equation (6) with the
dependent variable Sharpe ratio and report the results in Panel C. The coefficients of the interaction term
JNeg beta*I(FO) are statistically significant and positive in both columns [3] and [4] and column [4] tells
us that foreign operations can leverage the risk-adjusted return by additional 53 percent. We think the
economic magnitude we find in this test is non-trivial.
6. Foreign operations in currency risk management
6.1. Foreign banking operations in the U.S.
Thus far, our analysis implies that international banks with foreign operations in the U.S. show
superior stock performance over domestically-oriented international banks without U.S. branches.
However, this analysis tells us little about exactly what actions these banks take at foreign operations to
enhance the risk-return tradeoff. To this end, we have an attractive empirical setting: unlike previous
studies, we can observe, from the U.S. foreign branch balance sheet data compiled by the regulators, the
behavior of international banks in response to currency risk. Branches and agencies are the most common
structure of foreign banking organizations.2 They are not permitted to offer FDIC-insured retail deposits
and only accept certain types of deposits (e.g., deposits of any size from foreign individuals or entities
and/or wholesale deposits from U.S. citizens and residents). These entities are a legal extension of their
parent company and so considered a unit of their parent banks by regulators. Thus, they are not separately
capitalized and instead they report to the U.S. bank regulatory authorities the amount of capital supplied by
their parent banks and related offices, which is essentially equal to the amount of equity capital for foreign
branches in the U.S. They are not required to report their earnings on a stand-alone basis in the U.S., either.
2 Agencies differ from branches in terms of the range of activities each is permitted to conduct, but the differences are
minor. The functional similarity of agencies and branches is underscored by the fact that both types of entities file the
same form of quarterly CALL report.
14
Overall, branches and agencies are a relatively low-cost method of entry into the U.S. credit market for
international banks.
We emphasize that while we look at one specific location of foreign operations, international banks
have a significant presence in the U.S. financial system, providing many important benefits to individuals,
businesses, and the general economy. Figure 2 shows the trends of aggregate assets, loans, commercial &
industrial loans and deposits held by U.S. branches of international banks over the period of 1997-2017 in
millions of dollar (Figure 1a) and as a percent market share (Figure 1b). In 2007, the aggregate amount of
assets U.S branches of international banks held surpassed $ 2 trillion for the first time, and they hold $ 2.5
trillion of assets in total as of 2017:Q4. Importantly, the predominant type of assets held at the foreign
branches is commercial and industrial loans and international banks, through foreign branches, made nearly
25 percent of all commercial & industrial loans to U.S. businesses in 2008. While the market share has
been decreased since then, they still supply approximately 20 percent of all U.S. commercial & industrial
loans. Further, the recent trends suggest that foreign branches of international banks remain active in
business lending in the wake of the latest 2007-08 financial crisis. So in order to protect U.S. economic
agents and the overall stability of our financial system, states and federal banking agencies regulate and
supervise foreign banking operations in the U.S and these regulatory reports, FFIEC 002 “Report of Assets
and Liabilities of U.S Branches and Agencies of Foreign Banks”, which are publicly available, show their
balance sheet activities.
6.2. Regression framework
We study the effect of nondiversifiable currency risk on the asset-side and liability-side activities
at foreign banking operations by estimating the following specification with a panel data of U.S. foreign
branches over the period of 1997-2017:
𝑍𝑏,c,𝑞 = 𝜋0 + 𝜋1𝛽𝑐,𝑞−1𝐽− + 𝜋2𝛽𝑐,𝑞−1
𝐽+ + 𝜋3𝛽𝑐,𝑞−1𝐶 + 𝜋4
′ 𝑋𝑏,𝑐,𝑞−1 + 𝛾𝑏 + 𝛿𝑞 + 휁𝑐 + 휂𝑠 + 𝜇𝑖,c,𝑞 (7),
15
where b indicates foreign branches, q indexes time, c denotes home countries, and s indexes states where
foreign branches are located in the U.S. 𝑍𝑏,𝑐,𝑞 is one of the observed measures of foreign bank activities:
asset growth, liquid asset growth, business loan growth, floating-rate business loan growth, fixed-rate
business loan growth, real estate loan growth, liability growth, or equity growth (or equity ratio). Asset
Growth shows a quarterly growth rate of total assets; Liquid Asset Growth measures a quarterly growth rate
of cash and U.S. government securities; RE Loan Growth indicates a quarterly growth rate of real estate
loans; Business Loan Growth measures a quarterly growth rate of commercial & industrial loans; Flt-rate
Loan Growth shows a quarterly growth rate of floating-rate commercial & industrial loans; Fix-rate Loan
Growth is a quarterly growth rate of fixed-rate commercial & industrial loans; Liability Growth shows a
quarterly growth rate of liabilities; and Equity Growth measures a quarterly growth rate of capital from
foreign parents and related offices.3
All measures of decomposed currency betas are lagged in our specification. We think the estimated
betas for each currency serve as exogenous test variables in our empirical setting because the size of assets
and banking activities of any international bank entity are not substantial enough to determine the value of
our estimated betas. As a parsimonious set of control variables, we adopt ln TA, natural logarithm of total
branch assets; Equity, capital from parent banks and related offices scaled by total branch assets; FX
derivative dummy, a dummy variable that equals one for a bank branch that uses foreign currency
derivatives and equals zero otherwise; and IR derivative dummy, a dummy variable that equals one for a
bank branch that uses interest rate derivatives and equals zero otherwise. All control variables are lagged
as well.
We saturate our regression model with four fixed effects. The fixed effects, 𝛾𝑖, ensure that all
foreign branch-specific characteristics are accounted for, as long as they are invariant over our sample
period. Time fixed effects, 𝛿𝑞, are included to reflect time-varying factors common to all foreign branches
3 In CALL reports, the item is “Net due to related depository institutions” (RCFD2927).
16
in our sample. We also include country fixed effects, 휁𝑐 to take into account time-invariant characteristics
of foreign banking operations’ home countries (e.g., whether an international bank’s home country has
adopted an appropriate system of financial regulation or not). Foreign branches are required to choose a
“home state” similar to a U.S. bank’s home state, and branching has been generally restricted outside of the
home state. The presence of state fixed effects, 휂𝑠, guarantees that different trends of states where foreign
banking operations are primarily located are controlled for. The summary statistics and the definitions for
the variables we use in this study are presented in Tables 7-8.
6.3 Asset-side activities
Table 9 presents how foreign banking operations expand their asset-side activities in response to
the currency risk. We put all decomposed betas and focus on the variable JNeg beta. Columns [1] and [2]
show that the coefficients of the variable JNeg beta get statistically significant, implying a higher sensitivity
to nondiversifiable currency risk is associated with a faster asset expansion at foreign banking operations.
Economically, a one-standard deviation increase in the variable JNeg beta is associated with a 45 percent
increase in quarterly asset growth.
A higher negative jump beta represents country-specific currency depreciation risk, so
understanding whether a faster asset growth comes from more liquid assets or from riskier and illiquid
assets is integral to understanding foreign banks’ risk-taking behaviors with respect to the movements in
exchange rates. Thus, we next test how our currency risk measures relate to growth rates of liquid assets,
which are the balances of cash and U.S. government securities, and we expect that if foreign bank branches
behave in a purely risk-averse manner, they hold more liquid assets. The coefficients of the variable JNeg
beta in columns [3] and [4] do not get statistically significant. Instead, we find statistically significant and
positive coefficients on the variable JNeg beta in columns [5] and [6], where we report the results of
regressing the variable Business Loan Growth on our currency risk measures. Economically, a one-standard
deviation increase in the variable JNeg beta is associated with a more than 100 percent increase in quarterly
business loan growth. This strong economic magnitude might suggest that international banks strategically
17
try to capitalize on a high sensitivity to negative jump risk through foreign operations. As mentioned above,
a significant portion of foreign operations’ assets is composed of business loans and these results indicate
that international banks extend more business loans through foreign branches in the U.S. when they are
highly sensitive to discontinuous, negative exchange rate changes. The previous literature shows that a
negative jump beta is positively associated with the riskiness of a country and the corresponding currency,
so it represents a downside risk, all else equal. Further, Bakshi, Carr, and Wu (2008) find that investors
increase their risk premium when country-specific currency receives a negative shock, which in turn
potentially increases the cost of capital at parent banks. A higher cost of capital with volatile cash flows
from a loan project in a home country will transform NPV>0 loan applications into NPV<0 loan
applications, other things being constant. In reponse, international banks with foreign operations in the
U.S. might strategically want to leverage their physical presence in the U.S., supplying more commercial
& industrial loans in the foreign market. The positive coefficients of the variable JNeg beta are also in
good comparison with insignificant coefficients on the variable JPos beta.4
Importantly, we also test how foreign banks supply variable-rate and fixed-rate loans, respectively,
when they provide more business loans. When making variable-rate loans, financial institutions effectively
transfer the interest rate risk to their borrowers, and whether and how foreign operations shift one of the
major risks when raising business loan growth in the face of a higher negative currency risk is an important
question. Columns [1] and [2] in Table 10 first present the results of an empirical analysis for floating-rate
business loan growth: only the coefficients of the variable JNeg beta become statistically significant, which
suggests that international banks who are exposed to extreme, negative currency risk try to increase their
business loan supply by transferring interest rate risks to their borrowers at foreign operations. We do not
find evidence that international banks increase the supply of fixed-rate business loans as shown in columns
[3] and [4] because there are no statistically significant coefficients on the variable JNeg beta. We also
4 Previous literature finds that only downside jumps in currency markets are priced as a potential source of risk and
that is because financial markets react more strongly react to negative economic news than to positive news.
18
report the results of running the same test for real estate loan growth in columns [5] and [6]. Loans backed
by real estate are generally fixed and our nondiversifiable currency risk measures do not show an association
with a growth in real estate loans in a significant way.
In sum, we observe that during the volatile negative currency jump periods, highly sensitive
international banks on average increase a supply of business loans. When we separately estimate a rate of
growth for fixed-rate loans and floating-rate loans, respectively, we find only the supply of floating-rate
business loans is substantially shifted upward. This conservative management of interest rate risk seems to
be sensible when international banks ride out the negative, downside risk, which helps coordinate various
risks they are exposed to.
6.4 Liability-side activities
The liability side of foreign operations’ balance sheet shows how credit extension and other asset-
side activities are funded. Understanding how foreign operations of international banks strategically fund
their asset-side activities during the turbulent, jump period is especially important since almost all of the
foreign banks in our sample are restricted from collecting FDIC-insured deposits in the U.S.5 This means
that they lack a stable source of funding, so the ability of foreign operations to borrow from other sources
seems to be important.6 To this end, we test how currency risk affects their liability growth. In columns
[1] and [2] of Table 11, we find that the coefficients of the variable JPos beta are negative and statistically
significant, while those of the variable JNeg beta are positive. This result suggests that a low-currency risk
slows down a rate of liability growth, whereas a higher currency risk during the turbulent jump periods
substantially increases their liability growth. Economically, a one-standard deviation increase in the
variable JPos beta is associated with a 40 percent decrease in quarterly liability growth, but the same
5 Foreign banks in existence as insured deposit-taking entities before December 20, 1991 were grandfathered in under
the Foreign Bank Supervision Enhancement Act of 1991 and we have a few of those foreign banks in our sample.
However, a firm-fixed effect should soak up any confounding factors. 6 The aggregate deposits-to-assets ratio of foreign banks hovers around 0.41 in 2017 Q4, while the ratio for U.S.
domestic-owned banks is in the neighborhood of 0.77.
19
movement in the variable JNeg beta is associated with a 52 percent increase in liability growth on a
quarterly basis. International banks’ flexible funding behaviors at foreign operations with respect to the
direction of currency price jumps could create firm value. Especially, the marginal value of the borrowings
foreign banking operations can tap in the U.S. funding market in the face of extreme, negative currency risk
should be greater because the negative currency risk corresponds to the extreme depreciation in individual
currencies.
Next, we conduct a test on how currency risk relates to growth rates of capital from parent banks
and related offices. Columns [3] and [4] in Table 11 show that international banks strategically increase
raising capital from their parent bank only in response to a lower currency risk represented by a positive
jump beta in our model. In economic terms, a one-standard deviation increase in the variable JPos beta is
associated with a 30 percent increase in a rate of growth in capital from parent banks. We replace the
variable Equity Growth with the variable Equity Ratio in columns [5] and [6] and the thrust of our finding
does not change.
We think the results of our tests on liability-side activities should be combined with our asset-side
analysis: our empirical findings suggest that negative jump risks substantially increase international banks’
asset growth and commercial & industrial loan growth at their foreign operations in the U.S. And the asset
expansion during the period of negative jumps is mainly funded by a growth of borrowings from non-
related parties, which are valuable sources of funding when parent banks find it expensive to transport the
home capital to the foreign market due to a considerable depreciation in the local currency.
6.5. Activities of foreign-owned U.S. commercial banks
In our tests with a sample of foreign branches, we restrict our sample to U.S. branches and agencies
of international banks and exclude foreign-owned U.S. commercial banks. Foreign-owned U.S.
commercial banks hold approximately $ 1.3 trillion of assets and provide 8.2 percent of commercial &
industrial loans in 2017:Q4. Unlike international banks we thus far focus on, they are separately capitalized
20
entities. In addition, foreign-owned U.S. banks have an access to federal deposit insurance system, so they
must comply with all U.S. consumer laws and federal fair lending issues, and pay deposit insurance
premiums to the FDIC. So they are essentially treated as domestic-owned U.S. commercial banks by
regulators and that is why we exclude these banks from our sample. Still, they are subject to currency risk
given that more than 25 percent of the ownership is held by a non-U.S. banking organization. To see
whether they behave in a similar or different manner with respect to currency risk compared to international
banks, we run some of our tests with a sample of foreign-owned U.S. commercial banks and report the
regression results in Table 12. Columns [1] and [2] present the effect of currency risk on asset growth of
foreign-owned U.S. banks. The variable JNeg beta is weakly significant so the effect is in general less
pronounced for foreign-owned U.S. banks. Next, we test whether and how currency risk measures are
associated with their commercial & industrial loan growth. The coefficients of the variable JNeg beta in
columns [3] and [4] get positive and statistically significant at 5 percent level. This statistical significance
is weaker than that of foreign operations of international banks, and more importantly the economic
magnitude is much smaller as well: a one-standard deviation increase in the variable JNeg beta is associated
with a 17 percent increase in quarterly business loan growth rates.
We note that foreign-owned commercial banks face different regulatory restrictions and
prohibitions from foreign banking operations of international banks. This difference in restrictions is
reflected in the types of banking assets each of these forms would hold. For instance, foreign operations of
international banks hold a relatively high proportion of commercial & industrial loans, while foreign-owned
commercial banks hold assets more in line with those of domestically-owned commercial banks. This
might explain their rather subdued behavior with the negative, downside currency risk.
7. Conclusion and policy implications
21
In this research, we study the effect of foreign operations on the relation between nondiversifiable
currency risk and firm performance and provide new evidence that multinational firms can achieve the
benefits of corporate international diversification by strategically managing currency risk through foreign
operations. We start this line of inquiry by focusing on international banks and sensibly capturing
nondiversifiable currency risk. We first document that exchange rates can affect the performance of all
international banks. But those banks with foreign operations in the U.S. show better stock performance
when highly sensitive to downside currency risk. To offer an explanation to this finding, we investigate
the investment and funding activities international banks take with their foreign operations. When highly
sensitive to extreme, negative currency movement, they significantly expand their balance sheet by making
more business loans. Further, we find that international banks try to transfer the interest risk associated
with business loans to borrowers. Their asset expansion is mainly funded by the borrowing in the U.S.
market, which is valuable when the negative, extreme currency risk corresponds to the significant
depreciation in local currencies. Our study is the first that sheds light on the channel through which
corporate international diversification creates value with a strategical use of foreign operations.
We conclude our study with one caveat. While the actions and strategies the foreign operations of
international banks take appear to be value-enhancing at individual international bank-level, it might bring
unintended consequences to the U.S. financial system. If the asset expansion during the period of negative
jumps is mainly financed by a growth of borrowings from non-related parties, those assets and loans might
be vulnerable and would get easily called in by international banks when there are exogenous, market-side
funding shocks. Thus, the foreign operations’ lending behavior with respect to negative jump risk might
deserve the attention of bank regulators.
22
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24
Figure 1. Foreign currency exposure at international banks
0.000
0.300
0.600
0.900
Economic exposure Dollar factor exposure Local factor exposure
With FO W/O FO
25
Figure 2. Trends of aggregate assets, loans, C&I loans, and deposits held by foreign banks
A. Aggregate assets, loans, C&I loans, and deposits in dollar amounts
B. Aggregate assets, loans, C&I loans, and deposits as a percentage (share) of all banking offices in
the U.S.
* We use the totals of (1) U.S. branches and agencies of international banks, (2) U.S. commercial
bank subsidiaries of international banking organizations, and (3) U.S. commercial bank subsidiaries
of U.S. banking organizations as the denominator in calculating a percentage.
0
300,000
600,000
900,000
1,200,000
1,500,000
1,800,000
2,100,000
2,400,000
2,700,000
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
Aggregate assets, loans, C&I loans, and deposits(in millions)
Assets Total loans C&I loans Deposits
0
5
10
15
20
25
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
Aggregate assets, loans, C&I loans, and deposits(in percent)
Assets in percent Total loans in percent
C&I loans in percent Deposits in percent
26
Table 1. International banks with foreign operations vs. without foreign operations
This table compares international banks with foreign banking operations in the U.S. with those without
foreign operations. Our sample includes 452 international banks in 48 countries between 1997 and 2017.
We compute the quarterly averages of the variables across the sample and then take the time-series average.
All With FO W/O FO
Stock return Mean 0.013 0.012 0.013
Stdev 0.349 0.332 0.355
Skewness 0.236 0.205 0.247
Kurtosis 6.792 4.783 7.487
Sharpe ratio 0.400 0.356 0.416
Financial information PE ratio 24.080 23.041 24.490
EPS (in USD) 30.185 22.763 32.582
Dividend yield (%) 3.177 2.851 3.298
Net income (in millions of USD) 14.305 26.711 9.928
Market value (in millions of USD) 6,627 19,833 2,335
Revenue (in millions of USD)
1,452
4,272
454
Number of banks 452 106 346
27
Table 2. Foreign currency exposure at international banks
This table shows how international banks are exposed to changes in foreign exchange rates with a sample
of 452 banks. Economic exposure is measured by estimating equation (1); dollar factor exposure is
measured by estimating equation (2); and local factor exposure is measured by estimating equation (3). ***
indicates statistical significance at the 1% level.
All With FO W/O FO Difference
Panel A
Economic exposure 0.182 0.358 0.125 0.234***
Panel B
Dollar factor exposure 0.552 0.791 0.478 0.312***
Panel C
Local factor exposure 0.097 0.012 0.129 -0.117**8
28
Table 3. The effect of foreign operations on the relation between currency exposure and stock performance
This table shows the effect of foreign operations on the relation between currency exposure and stock performance. We estimate equation (4) with
the variable Economic expo. as a measure of currency exposure in Panel A and with the variable Dollar expo. as a measure of currency exposure in
Panel B. Robust standard errors appear in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Economic exposure
[1] [2] [3] [4] [5] [6] [7] [8] [9]
VARIABLES Stock return Stock return volatility Sharpe ratio
Economic expo. -0.009
(0.045)
-0.052
(0.048)
-0.111**
(0.046)
0.170***
(0.041)
0.212***
(0.053)
0.061*
(0.032)
-0.003
(0.005)
-0.009
(0.006)
-0.012*
(0.006)
Economic expo.* I(FO) 0.303***
(0.099)
0.179*
(0.095)
-0.286***
(0.094)
-0.001
(0.076)
0.037***
(0.013)
0.022*
(0.013)
I(FO) 0.028
(0.100)
0.147
(0.520)
-1.041***
(0.308)
0.257
(0.292)
0.000
(0.033)
-0.007
(0.034)
Constant 3.078**
(1.386)
3.119**
1.386
14.702
(12.784)
34.710***
(1.633)
34.632***
(1.641)
55.764***
(6.900)
0.148
(0.118)
0.153
(0.118)
1.780**
(0.856)
Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 19,613 19,613 19,613 19,613 19,613 19,613 19,613 19,613 19,613
R-squared 0.001 0.001 0.109 0.015 0.016 0.238 0.001 0.001 0.100
Number of banks 452 452 452 452 452 452 452 452 452
Time fixed effects No No Yes No No Yes No No Yes
Country fixed effects No No Yes No No Yes No No Yes
29
Panel B: Dollar factor exposure
[1] [2] [3] [4] [5] [6] [7] [8] [9]
VARIABLES Stock return Stock return volatility Sharpe ratio
Dollar expo. -0.932***
(0.197)
-1.123***
(0.273)
-0.480
(0.294)
5.375***
(0.226)
0.061***
(0.003)
4.756***
(0.336)
-0.157***
(0.016)
-0.195***
(0.022)
-0.127***
(0.026)
Dollar expo.* I(FO) 0.397
(0.393)
0.099
(0.380)
-1.367***
(0.440)
-0.734*
(0.403)
0.084***
(0.032)
0.035
(0.032)
I(FO) 0.145
(0.280)
0.313
(0.283)
-1.796***
(0.308)
-0.023
(0.292)
-0.001
(0.000)
0.018
(0.033)
Constant 2.705*
(1.38)
2.793**
1.39
14.505
(12.72)
36.871***
(1.54)
36.552***
(1.57)
58.320***
(6.88)
0.085
(0.12)
0.103
(0.12)
1.740**
(0.85)
Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 19,613 19,613 19,613 19,613 19,613 19,613 19,613 19,613 19,613
R-squared 0.003 0.003 0.109 0.074 0.079 0.270 0.005 0.006 0.100
Number of banks 452 452 452 452 452 452 452 452 452
Time fixed effects No No Yes No No Yes No No Yes
Country fixed effects No No Yes No No Yes No No Yes
30
Table 4. Jump detection
This table shows the number of jumps that are detected at the 5% significance level by using daily exchange
rate data from September 1992 to December 2017. “# test”, “# jump”, “+ jp”, and “- jp” indicate the number
of returns that are tested for jumps, the total number of detected jumps, the number of positive jumps, and
the number of negative jumps, respectively.
Country # tests # jumps # jp (+) # jp (-) Mean Stdev Mean Stdev
Argentina 6767 1307 509 798 124.585 17.285 -101.966 12.399
Australia 6767 1027 472 555 309.834 10.710 -321.484 11.129
Austria 6249 1039 540 499 275.283 7.658 -268.633 6.722
Bahrain 6748 998 511 487 6.698 0.742 -6.627 0.755
Belgium 4943 772 392 380 279.167 7.329 -277.457 7.091
Bolivia 2703 304 166 138 94.162 5.330 -89.613 4.695
Brazil 6518 1014 429 585 377.919 19.127 -359.627 21.183
Canada 6767 1007 493 514 209.728 6.794 -213.533 7.323
Chile 6767 1037 490 547 226.310 8.667 -233.074 8.974
China 6745 643 363 280 53.040 5.115 -52.344 3.263
Columbia 6173 1015 459 556 254.511 11.778 -264.833 12.790
Denmark 6767 1075 539 536 277.669 7.385 -274.156 6.909
Ecuador 5992 210 49 161 659.951 45.921 -419.115 38.978
Egypt 6767 1337 603 734 89.558 9.369 -95.076 11.515
Finland 6249 1044 547 497 270.306 7.424 -265.652 6.597
France 6249 1037 542 495 272.784 7.589 -266.255 6.572
Germany 6249 1028 531 497 277.002 7.614 -268.767 6.718
Greece 6249 1047 539 508 270.885 7.670 -267.638 6.666
Hong Kong 6249 981 465 516 10.240 0.692 -9.476 0.622
India 6249 1051 455 596 131.458 7.089 -140.875 7.562
Indonesia 6235 1019 464 555 317.104 27.506 -307.819 30.122
Ireland 6249 1029 528 501 266.866 7.587 -263.191 6.791
Israel 6249 968 457 511 190.557 6.462 -193.609 7.622
Italy 6249 1043 541 502 265.466 7.518 -262.027 6.791
Japan 6767 1018 518 500 310.454 10.905 -286.182 9.253
Jump frequency Jp (+) size Jp (-) size
31
Table 4. Jump detection (continued)
Country # tests # jumps # jp (+) # jp (-) Mean Stdev Mean Stdev
Jordan 6767 1303 644 659 38.987 2.083 -41.714 2.474
Korea (South) 6757 1090 520 570 225.343 14.592 -235.136 15.389
Kuwait 6767 1127 565 562 56.961 3.154 -56.352 3.100
Malaysia 6767 1036 547 489 134.127 10.335 -147.072 11.544
Mexico 6758 1053 453 600 244.702 12.883 -286.509 16.860
Netherland 6249 1037 543 494 273.814 7.666 -268.588 6.677
Nigeria 5853 1100 515 585 316.625 24.559 -310.448 23.539
Norway 6767 1016 492 524 314.472 8.756 -329.202 9.514
Pakistan 4925 843 381 462 112.672 7.569 -118.513 8.060
Peru 6767 1110 497 613 117.448 6.813 -136.997 8.506
Phillippines 6673 1122 524 598 173.147 9.858 -184.374 11.666
Poland 6392 951 432 519 318.982 11.488 -336.796 13.992
Portugal 6249 1040 544 496 270.007 7.573 -266.919 6.608
Saudi Aribia 6762 1318 650 668 4.194 0.556 -3.844 0.453
Singapore 6767 1028 531 497 139.223 5.798 -146.307 6.094
Spain 6249 1043 543 500 267.627 7.500 -264.614 6.623
Sweden 6767 1001 500 501 317.562 9.026 -336.495 9.638
Switzerland 6767 1067 561 506 311.578 8.563 -298.548 7.758
Taiwan 6767 1074 531 543 112.535 5.357 -117.488 5.997
Thailand 6767 1053 527 526 170.255 13.964 -181.469 14.101
Turkey 6767 1023 366 657 376.215 24.326 -384.028 23.042
United Arab Emirates 6757 1348 669 679 2.774 0.293 -2.768 0.295
United Kingdom 6767 1047 515 532 248.833 7.566 -249.829 7.717
Market (USD) 6767 1034 468 566 119.901 3.662 -122.440 3.842
Avg of 48 FX 6391 1018 493 526 216.034 9.824 -212.772 9.764
Jp (-) sizeJump frequency Jp (+) size
32
Table 5. Beta estimation
This table shows the time-series means of standard, continuous, positive jump, and negative jump betas. The betas are estimated every quarter by
using the previous 12-quarter observations (i.e., betas in quarter p are based on the observations from quarter p – 11 to quarter p) using equation (5).
Country Standard Continuous Positive Negative Country Standard Continuous Positive Negative
beta beta jump beta jump beta beta beta jump beta jump beta
Argentina 0.094 0.031 0.791 0.393 Jordan 0.020 0.009 0.084 0.170
Australia 1.334 0.944 2.598 2.480 Korea (South) 0.667 0.374 1.499 2.051
Austria 1.674 1.262 2.538 2.259 Kuwait 0.158 0.102 0.385 0.388
Bahrain -0.001 -0.001 0.013 0.020 Malaysia 0.517 0.224 1.132 1.114
Belgium 1.714 1.402 2.441 2.318 Mexico 0.626 0.482 1.334 1.930
Bolivia -0.007 0.001 0.178 0.482 Netherland 1.677 1.261 2.516 2.250
Brazil 0.987 0.447 2.042 2.857 Nigeria 0.005 -0.033 0.574 1.622
Canada 0.705 0.479 1.427 1.589 Norway 1.793 1.302 2.920 2.653
Chile 0.710 0.462 1.539 1.793 Pakistan 0.038 0.006 0.432 0.654
China 0.039 0.017 0.469 0.281 Peru 0.163 0.089 0.522 0.698
Columbia 0.630 0.376 1.407 1.773 Philippines 0.488 0.232 1.220 1.615
Denmark 1.695 1.327 2.597 2.349 Poland 1.692 1.273 2.757 2.787
Ecuador 0.027 -0.010 -0.878 1.508 Portugal 1.649 1.244 2.473 2.229
Egypt 0.014 -0.012 0.176 0.370 Saudi Aribia 0.001 -0.001 0.017 0.019
Finland 1.596 1.156 2.544 2.261 Singapore 0.769 0.593 1.413 1.185
France 1.606 1.216 2.585 2.273 Spain 1.624 1.221 2.470 2.202
Germany 1.629 1.167 2.611 2.304 Sweden 1.711 1.197 2.938 2.731
Greece 1.647 1.318 2.490 2.244 Switzerland 1.533 1.060 2.996 2.576
Hong Kong 0.015 0.007 0.065 0.062 Taiwan 0.353 0.220 0.729 0.998
India 0.294 0.169 0.805 0.804 Thailand 0.680 0.354 1.634 1.473
Indonesia 0.666 0.085 2.138 2.368 Turkey 1.191 0.865 2.469 2.781
Ireland 1.537 1.090 2.466 2.241 United Arab Emirates 0.000 0.000 0.002 0.010
Israel 0.519 0.313 1.327 1.209 United Kingdom 1.090 0.709 2.247 2.132
Italy 1.551 1.179 2.341 2.168 Avg of 48 FX 0.831 0.575 1.535 1.598
Japan 0.759 0.397 2.210 2.018 Std of 48 FX 0.668 0.519 1.042 0.897
33
Table 6. The effect of foreign operations on the relation between nondiversifiable currency risk and
stock performance
This table shows the effect of foreign operations on the relation between nondiversifiable currency risk
and stock performance. We estimate equation (6) with the variable Stock return as a dependent variable
in Panel A, with the variable Stock return volatility as a dependent variable in Panel B, and with the
variable Sharpe ratio as a dependent variable in Panel C. Robust standard errors appear in parentheses.
***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Stock return
[1] [2] [3] [4]
VARIABLES Stock return
JNeg beta 1.128***
(0.268)
1.945***
(0.326)
1.536***
(0.366)
1.908***
(0.393)
JNeg beta* I(FO) 1.728***
(0.498)
1.827***
(0.566)
JPos beta -0.761**
(0.303)
-2.628***
(0.384)
-2.216***
(0.422)
-2.903***
(0.459)
JPos beta*I(FO)
-1.608***
(0.519)
-0.969*
(0.584)
Cont beta
-0.448
(0.467)
2.597***
(0.759)
2.529***
(0.757)
2.136***
(0.806)
Constant 3.474**
(1.460)
14.326
(13.023)
14.320
(13.018)
-8.963
(9.240)
Controls Yes Yes Yes Yes
Observations 18,648 18,648 18,648 18,648
R-squared 0.002 0.111 0.111 0.136
Number of banks 452 452 452 452
Time fixed effects No Yes Yes Yes
Country fixed effects No Yes Yes Yes
Bank fixed effects No No No Yes
34
Panel B: Stock return volatility
[1] [2] [3] [4]
VARIABLES Stock return volatility
JNeg beta 1.717***
(0.310)
2.657***
(0.352)
3.126***
(0.401)
1.338***
(0.378)
JNeg beta* I(FO) -1.924***
(0.441)
0.060
(0.470)
JPos beta 0.673**
(0.334)
-0.531
(0.394)
-1.320***
(0.445)
0.818*
(0.434)
JPos beta*I(FO)
2.999***
(0.473)
1.334***
(0.469)
Cont beta
-1.473***
(0.217)
-6.560***
(0.854)
-6.548***
(0.854)
-6.637***
(0.810)
Constant 35.955***
(1.696)
73.620***
(6.486)
73.317***
(6.479)
91.145***
(8.626)
Controls Yes Yes Yes Yes
Observations 18,648 18,648 18,648 18,648
R-squared 0.028 0.241 0.243 0.441
Number of banks 452 452 452 452
Time fixed effects No Yes Yes Yes
Country fixed effects No Yes Yes Yes
Bank fixed effects No No No Yes
Panel C: Sharpe ratio
[1] [2] [3] [4]
VARIABLES Sharpe ratio
JNeg beta 0.195***
(0.032)
0.308***
(0.033)
0.281***
(0.038)
0.267***
(0.035)
JNeg beta* I(FO) 0.112*
(0.060)
0.142**
(0.062)
JPos beta -0.118***
(0.034)
-0.323***
(0.045)
-0.292***
(0.050)
-0.326***
(0.045)
JPos beta*I(FO)
-0.122**
(0.061)
-0.072
(0.067)
Cont beta
0.074
(0.056)
0.077
(0.063)
0.073
(0.092)
0.174**
(0.087)
Constant 0.098
(0.124)
1.804**
(0.883)
1.808**
(0.884)
-0.379
(0.649)
Controls Yes Yes Yes Yes
Observations 18,648 18,648 18,648 18,648
R-squared 0.004 0.106 0.106 0.271
Number of banks 452 452 452 452
Time fixed effects No Yes Yes Yes
Country fixed effects No Yes Yes Yes
Bank fixed effects No No No Yes
35
Table 7. Summary statistics for foreign banking operations
This table shows the summary statistics of all the variables used to study the activities of foreign banking
operations.
Test Variable obs. mean st. dev. 5th 25th 50th 75th 95th
C beta 17,667 0.7674 0.6816 -0.0025 0.1618 0.7067 1.2360 1.9394
JPos beta 17,667 1.9808 1.3804 0.0121 0.9109 2.0901 2.7208 4.6332
JNeg beta 17,667 1.8807 1.1463 0.0577 0.9747 1.9637 2.5745 3.8281
Control Variable obs. mean st. dev. 5th 25th 50th 75th 95th
ln TA 17,667 13.686 2.4129 8.9770 12.344 13.609 15.436 17.531
Equity 17,667 0.3353 0.3646 0.0000 0.0000 0.1848 0.6453 0.9969
FX derivative dummy 17,667 0.3600 0.4800 0.0000 0.0000 0.0000 1.0000 1.0000
IR derivative dummy 17,667 0.4403 0.4964 0.0000 0.0000 0.0000 1.0000 1.0000
Dependent Variable obs. mean st. dev. 5th 25th 50th 75th 95th
Total Asset Growth 17,667 0.0345 0.2936 -0.3505 -0.0845 0.0037 0.1070 0.5031
Liquid Asset Growth
RE Loan Growth
16,664
10,249
0.4227
0.0206
2.2001
0.4461
-0.6945
-0.4991
-0.1927
-0.0648
0.0000
-0.0084
0.2356
0.0408
2.1873
0.4673
Business Loan Growth 15,044 0.0349 0.3665 -0.3862 -0.0829 0.0000 0.0967 0.5273
Flt-rate Loan Growth
Fix-rate Loan Growth
13,905
9,056
0.0945
0.1262
0.7337
1.0336
-0.5418
-0.8857
-0.0973
-0.1434
-0.0012
-0.0048
0.1098
0.1161
0.7472
1.0807
Total Liability Growth
Equity Growth
Equity
17,182
12,227
17,667
0.0798
0.2151
0.3393
0.5724
1.2948
0.3660
-0.5038
-1.0000
0.0000
-0.1202
-0.2213
0.0000
0.0036
0.0000
0.1901
0.1391
0.2097
0.6567
0.7703
1.8818
0.9974
Table 8. Definition of the variables
This table presents the definitions of all the variables used to study the activities of foreign banking
operations.
Variable Definition
Cont beta [Decomposed component] A measure of sensitivity of continuous exchange rate
changes to market components, lagged
JPos beta [Decomposed component] A measure of sensitivity of discontinuous positive exchange
rate changes to market components, lagged
JNeg beta [Decomposed component] A measure of sensitivity of discontinuous negative
exchange rate changes to market components, lagged
ln TA Natural logarithm of total assets, lagged
Equity Capital from foreign parents and related offices scaled by total assets, lagged
FX derivative dummy A dummy variable that equals one for a bank which uses foreign currency derivatives
and equals zero otherwise, lagged
IR derivative dummy A dummy variables that equals one for a bank which uses interest rate derivatives and
equals zero otherwise, lagged
Asset Growth Quarterly growth rate of total assets
Liquid Asset Growth
RE Loan Growth
Quarterly growth rate of cash and U.S. government securities
Quarterly growth rate of real estate loans
Business Loan Growth Quarterly growth rate of commercial and industrial loans
Flt-rate Loan Growth
Fix-rate Loan Growth
Quarterly growth rate of floating-rate commercial and industrial loans
Quarterly growth rate of fixed-rate commercial and industrial loans
Liability Growth
Equity Growth
Equity Ratio
Quarterly growth rate of total liabilities
Quarterly growth rate of capital from foreign parents and related offices
Capital from foreign parents and related offices scaled by total assets
36
Table 9. Asset Growth, Liquid Asset Growth and Business Loan Growth
This table shows the regression results of estimating equation (7) with a dependent variable Asset Growth in columns [1] and [2], with a dependent
variable Liquid Asset Growth in columns [3] and [4] and with a dependent variable Business Loan Growth . All the variables are defined in Table
8. Robust standard errors appear in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
[1] [2] [3] [4] [5] [6]
VARIABLES Asset Growth Liquid Asset Growth Business Loan Growth
C beta 0.0101 0.0099 0.1704** 0.1705** -0.0331** -0.0334**
(0.0117) (0.0118) (0.0779) (0.0780) (0.0165) (0.0165)
JPos beta -0.0049 -0.0048 0.0034 0.0034 -0.0064 -0.0064
(0.0051) (0.0051) (0.0319) (0.0319) (0.0067) (0.0067)
JNeg beta 0.0136** 0.0136** -0.0173 -0.0173 0.0315*** 0.0316***
(0.0067) (0.0067) (0.0367) (0.0367) (0.0111) (0.0111)
ln TA -0.0616*** -0.0616*** -0.0872** -0.0872** -0.0332*** -0.0331***
(0.0066) (0.0066) (0.0373) (0.0373) (0.0090) (0.0090)
Equity -0.0834*** -0.0834*** -0.1884 -0.1884 -0.0618*** -0.0618***
(0.0190) (0.0190) (0.1159) (0.1160) (0.0204) (0.0204)
FX derivative dummy 0.0159 0.0159 -0.1596* -0.1595* 0.0000 -0.0001
(0.0103) (0.0103) (0.0819) (0.0820) (0.0138) (0.0138)
IR derivative dummy 0.0177* 0.0178* -0.0607 -0.0605 0.0128 0.0128
(0.0097) (0.0097) (0.0696) (0.0699) (0.0114) (0.0115)
Constant 0.9116*** 0.9121*** 1.4325*** 1.4332*** 0.5939*** 0.5937***
(0.0859) (0.0859) (0.5113) (0.5115) (0.1235) (0.1235)
Observations 17,667 17,667 16,664 16,664 15,044 15,044
R-squared 0.054 0.054 0.026 0.026 0.026 0.026
Number of branches 491 491 483 483 459 459
Branch fixed effects Yes Yes Yes Yes Yes Yes
Time fixed effects Yes Yes Yes Yes Yes Yes
State fixed effects Yes Yes Yes Yes Yes Yes
Country fixed effects No Yes No Yes No Yes
37
Table 10. Floating-rate Business Loan Growth, Fixed-rate Business Loan Growth and Real estate Loan Growth
This table shows the regression results of estimating equation (7) with a dependent variable Flt-rate Loan Growth in columns [1] and [2], with a
dependent variable Fix-rate Loan Growth in columns [3] and [4] and with a dependent variable Real estate Loan Growth. All the variables are
defined in Table 8. Robust standard errors appear in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively.
[1] [2] [3] [4] [5] [6]
VARIABLES Flt-rate Loan Growth Fix-rate Loan Growth Real estate Loan Growth
C beta -0.0252 -0.0259 -0.1532*** -0.1520*** -0.0111 -0.0118
(0.0438) (0.0440) (0.0502) (0.0502) (0.0337) (0.0337)
JPos beta -0.0203 -0.0202 0.0164 0.0162 -0.0258* -0.0256*
(0.0139) (0.0139) (0.0266) (0.0266) (0.0148) (0.0148)
JNeg beta 0.0712*** 0.0715*** 0.0460 0.0456 0.0301 0.0303
(0.0238) (0.0239) (0.0392) (0.0392) (0.0200) (0.0200)
ln TA -0.0466*** -0.0465*** -0.0527** -0.0532** -0.0044 -0.0043
(0.0172) (0.0172) (0.0247) (0.0247) (0.0121) (0.0121)
Equity -0.0778** -0.0779** -0.0551 -0.0548 -0.0320 -0.0320
(0.0332) (0.0332) (0.0532) (0.0532) (0.0334) (0.0334)
FX derivative dummy -0.0273 -0.0277 0.0125 0.0137 0.0276 0.0273
(0.0233) (0.0233) (0.0354) (0.0354) (0.0237) (0.0237)
IR derivative dummy -0.0046 -0.0051 0.0179 0.0197 0.0009 0.0014
(0.0222) (0.0223) (0.0428) (0.0427) (0.0211) (0.0211)
Constant 0.8574*** 0.8560*** 1.0415*** 1.0483*** 0.0907 0.0913
(0.2413) (0.2416) (0.3597) (0.3601) (0.1650) (0.1650)
Observations 13,905 13,905 9,506 9,506 10,249 10,249
R-squared 0.016 0.016 0.013 0.013 0.013 0.013
Number of branches 433 433 398 398 359 359
Branch fixed effects Yes Yes Yes Yes Yes Yes
Time fixed effects Yes Yes Yes Yes Yes Yes
State fixed effects Yes Yes Yes Yes Yes Yes
Country fixed effects No Yes No Yes No Yes
38
Table 11. Liability Growth, Equity Growth and Equity ratio
This table shows the regression results of estimating equation (7) with a dependent variable Liability Growth in columns [1] and [2], with a dependent
variable Equity Growth in columns [3] and [4] and with a dependent variable Equity ratio. All the variables are defined in Table 8. Robust standard
errors appear in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
[1] [2] [3] [4] [5] [6]
VARIABLES Liability Growth Equity Growth Equity ratio
C beta 0.0043 0.0040 0.0557 0.0547 -0.0044 -0.0045
(0.0227) (0.0227) (0.0594) (0.0596) (0.0055) (0.0055)
JPos beta -0.0232** -0.0231** 0.0468** 0.0471** 0.0050** 0.0050**
(0.0091) (0.0091) (0.0235) (0.0235) (0.0025) (0.0025)
JNeg beta 0.0359** 0.0359** -0.0365 -0.0362 -0.0032 -0.0031
(0.0146) (0.0146) (0.0328) (0.0328) (0.0033) (0.0033)
ln TA -0.0672*** -0.0672*** -0.1844*** -0.1843*** -0.0106*** -0.0106***
(0.0108) (0.0108) (0.0266) (0.0266) (0.0026) (0.0026)
Equity 0.4497*** 0.4497*** -1.7686*** -1.7685*** 0.8065*** 0.8065***
(0.0422) (0.0422) (0.1165) (0.1165) (0.0122) (0.0122)
FX derivative dummy -0.0002 -0.0002 -0.0460 -0.0465 -0.0044 -0.0044
(0.0186) (0.0187) (0.0677) (0.0678) (0.0049) (0.0049)
IR derivative dummy 0.0244 0.0249 -0.0933* -0.0926* -0.0077* -0.0077*
(0.0199) (0.0200) (0.0526) (0.0527) (0.0046) (0.0046)
Constant 0.9468*** 0.9483*** 3.4377*** 3.4398*** 0.1835*** 0.1834***
(0.1432) (0.1431) (0.3755) (0.3754) (0.0349) (0.0348)
Observations 17,182 17,182 12,227 12,227 17,667 17,667
R-squared 0.052 0.052 0.089 0.089 0.681 0.681
Number of branches 485 485 470 470 491 491
Branch fixed effects Yes Yes Yes Yes Yes Yes
Time fixed effects Yes Yes Yes Yes Yes Yes
State fixed effects Yes Yes Yes Yes Yes Yes
Country fixed effects No Yes No Yes No Yes
39
Table 12. A sample of foreign-owned U.S. commercial banks
This table shows the regression results with a sample of foreign-owned U.S. commercial banks of
estimating equation (7) with a dependent variable Asset Growth in columns [1] and [2] and with a dependent
variable Business Loan Growth in columns [3] and [4]. All the variables are defined in Table 8. Robust
standard errors appear in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and
10% levels, respectively.
[1] [2] [3] [4]
VARIABLES Asset Growth Business Loan Growth
C beta 0.0088 0.0051 0.0014 -0.0029
(0.0092) (0.0096) (0.0113) (0.0118)
JPos beta -0.0106* -0.0102 -0.0094* -0.0091
(0.0059) (0.0062) (0.0054) (0.0056)
JNeg beta 0.0103* 0.0110* 0.0165** 0.0175**
(0.0058) (0.0061) (0.0071) (0.0073)
ln TA -0.0559*** -0.0663*** -0.0546*** -0.0654***
(0.0088) (0.0099) (0.0099) (0.0125)
Equity 0.0027 -0.0230 -0.0009 -0.0316
(0.0587) (0.0548) (0.0677) (0.0706)
FX derivative dummy 0.0159 0.0159 0.0158 0.0270
(0.0103) (0.0103) (0.0235) (0.0263)
IR derivative dummy 0.0177* 0.0178* 0.0285 0.0376
(0.0097) (0.0097) (0.0308) (0.0308)
Constant 0.8232*** 0.9955*** 0.8183*** 1.0271***
(0.1267) (0.1516) (0.1441) (0.1891)
Observations 4,196 4,196 4,181 4,181
R-squared 0.082 0.089 0.064 0.068
Number of banks 138 138 138 138
Bank fixed effects Yes Yes Yes Yes
Time fixed effects Yes Yes Yes Yes
State fixed effects Yes Yes Yes Yes
Country fixed effects No Yes No Yes