Managing currency risk with foreign operations:

Evidence from international banks

Karen Y. Jang

Florida International University (yejang@fiu.edu)

Minho Wang

Florida International University (minwang@fiu.edu)

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