Quantities and Covered-Interest Parity
Tobias J. Moskowitz, Chase P. Ross, Sharon Y. Ross, Kaushik Vasudevan∗
July 11, 2024
Abstract
Studies of intermediated arbitrage argue that bank balance sheets are an importantconsideration, yet little evidence exists on banks’ positioning in this context. Using
confidential supervisory data (covering $25 trillion in daily notional exposures) we
examine banks’ positions in connection with covered-interest parity (CIP) deviations.
Exploiting cross-sectional variation in CIP deviations that have largely challenged
existing theories, we document three novel forces that drive bases: 1) foreign safe asset
scarcity, 2) market power and segmentation of banks specializing in different markets,and 3) concentration of demand. Our findings shed empirical light on the interplay offrictions influencing banks’ provision of dollar funding.

JEL Codes: F3, F31, F65, G1, G13, G15, G2, G23
Keywords: basis, covered-interest parity deviation, foreign exchange, safe assets∗

T. Moskowitz is at the Yale School of Management, Yale University, NBER, and AQR Capital Management,
email: tobias.moskowitz@yale.edu. C. Ross is at the Board of Governors of the Federal Reserve System,
email: chase.p.ross@frb.gov. S. Ross is at the Board of Governors of the Federal Reserve System, email:sharon.y.ross@frb.gov. K. Vasudevan is at the Daniels School of Business, Purdue University, email:kvasude@purdue.edu. We thank Tristan D’Orsaneo for excellent research assistance. For comments andsuggestions, we thank our discussants Rashad Ahmed, Will Diamond, Ben Golez, Piotr Orłowski, Stephen
Szaura, and Mengbo Zhang, as well as Ricardo Correa, Wenxin Du, Nathan Foley-Fisher, Peter Hansen,
Toomas Laarits, Jinsook Lee, Borghan Narajabad, Stas Nikolova, Junko Oguri, David Rappoport, Bryan
Ricketts, Matt Seay, Alex Vardoulakis, Xiaochuan Xing, Chenzi Xu, Emre Yoldas, seminar participants at
the University of Nebraska and Fed Board, and conference participants at the Wabash River Conference,
2024 ASSA, 2024 MFA, the Spring 2024 NBER Financial Market Frictions and Systemic Risks meeting, the2024 Eastern Finance Association meeting, and the 2024 UCLA Fink Center Conference. AQR Capital is a
global asset manager who may or may not use the insights and methods in this paper. The analysis andconclusions set forth are those of the authors and do not indicate concurrency by members of the Board ofGovernors of the Federal Reserve System, AQR, or their staffs.

1Introduction

Spreads on bank-intermediated arbitrage trades, called bases, have persisted since the 2008
financial crisis, attracting substantial attention from academics and practitioners. The
existence of bases is often cited as evidence that financial intermediaries are not simply a veil,
as assumed in classical theories. The litany of frictions faced by intermediaries, therefore,
may have import on asset prices and, by extension, the broader economy. Prior work focuses
primarily on asset pricing data. In this paper, we use novel quantity data, to gain a better
understanding of intermediaries’ basis trading activity and of how intermediary constraints
affect asset prices.
We focus on covered-interest parity (CIP) as a simple and clear arbitrage trade intermediated by banks.1 CIP deviations have been used as a primary empirical laboratory todescribe the importance of intermediation frictions. A CIP arbitrage trade consists of thefollowing: to meet foreign customer demand to borrow dollars, an intermediary borrows
dollars, enters into a foreign exchange swap with the customer to exchange the dollars forforeign currency, and invests the proceeds in foreign safe assets. At maturity, the intermediaryreceives dollars from the customer and repays the initial dollar loan. CIP implies that thereturn on this transaction should be zero. Deviations from CIP are important because theyreflect frictions in the global provision of dollar funding, which occurs largely via currency
swaps and forwards. Most studies test and reject that CIP bases are zero and relate non-zerobases to measures of intermediary frictions (Du et al. (2018), Iida et al. (2018), Cenedese
et al. (2021), Wallen (2022), Du and Schreger (2022), Augustin et al. (2022)).
To better understand the role of intermediaries in asset prices and, specifically, the global
provision of dollar funding, we use granular confidential supervisory data. We also exploit
cross-sectional variation in CIP bases, whose existence is a puzzle for existing intermediary
theories for the basis. Our unique quantity data provide bank positions in these markets,
which when merged with prices, shed substantial light on what drives bases, including
explaining their puzzling cross-sectional heterogeneity.
1

There are several types of basis trades beyond covered-interest parity, including the equity index
futures/cash basis (Hazelkorn et al., 2023), the Treasury on-the-run/off-the-run spread (Krishnamurthy,
2002), the Treasury cash/futures basis (Barth and Kahn, 2021), the Treasury cash/swap basis (J Jermann,
2020; Boyarchenko et al., 2018b), the bond/CDS basis (Bai and Collin-Dufresne, 2019), and the CDX/CDS
basis (Boyarchenko et al., 2018c). We focus on CIP bases because our data provide detailed bank exposure
information to specific country interest rates and currencies, allowing a granular examination.

1

We find that three novel forces are important for driving bases. First, intermediaries
purchase foreign risky assets, rather than safe assets, corresponding with their synthetic
dollar lending to customers. Since CIP arbitrage requires a position in safe assets, banks
only imperfectly execute CIP arbitrage and take on meaningful risk. Second, markets aresegmented, with banks specializing in different currencies and tenors, so bases reflect bankspecific constraints. Segmentation also reduces the elasticity of bases to demand via reduced
risk sharing and market power. Third, intermediaries face concentrated demand in somemarkets from certain counterparties and require compensation associated with counterparty
risk. We break down and quantify how each of these channels generates time-series andcross-sectional variation in CIP deviations. Our results highlight the presence and importanceof segmentation and search frictions in even the largest and most liquid markets.
To better interpret our findings and guide our empirical investigation, we build a stylized
model where risk-averse intermediaries meet customers’ demand for dollars in exchange forforeign currency by engaging in basis trades but face several frictions: costs to expand theirbalance sheets, foreign safe asset scarcity (difficulty in locating scarce foreign safe bonds withlow yields), heterogeneous expertise in risky assets, and counterparty limits.
The model showcases how each friction contributes to bases. Balance sheet costs drive acommon component of bases across all currencies, which is the primary focus of the literature.
However, the other frictions we model have an impact, too, and, importantly, can capture
the cross-sectional heterogeneity in the data. For example, scarce foreign safe bonds that are
hard to find and have low yields lead intermediaries to hold risky bonds. In turn, we observe
differences in bases across currencies based on the amount of synthetic dollar borrowing
demand from those currencies. Heterogeneous expertise leads intermediaries to specialize incertain markets. This segmentation creates market power and impedes risk sharing, which
affects the elasticity of the basis differentially across currencies. Counterparty limits also
imply that the concentration of demand contributes to the inelasticity of bases. Thesefrictions vary across banks and markets, generating cross-sectional variation in bases.
To test the model’s implications and whether these channels can capture the variation inbases, we use the Federal Reserve’s FR2052a Complex Institution Liquidity Monitoring Report,
which provides granular, high-frequency data on the balance sheets of the largest banks
in the US. The data cover $25 trillion of daily notional exposure on average. Because thereport provides detailed snapshots of derivative exposures as well as the assets and liabilities2

side of banks’ balance sheets, we obtain a novel view of the otherwise opaque positioning ofintermediaries in currency markets. Analyzing the data, we find that banks net lend about$100 billion on average through swaps in the markets we study, indicating their importancein meeting global demand for dollar funding. Moreover, we show that banks syntheticallylend the most dollars in the same markets where the basis indicates dollar funding is the mostexpensive. This fact is consistent with the banking sector facing increasing marginal costs tomeet dollar demand from each currency. Guided by our model, we empirically investigate
how the outlined frictions contribute to these increasing marginal costs.
First, we find foreign safe asset scarcity is an important driver of CIP bases. To execute
CIP basis arbitrage, an intermediary must hold the equivalent of $1 of maturity-matchedforeign safe assets for every $1 it lends in order to earn the foreign risk-free rate. In practice,
however, banks hold $0.05 per dollar lent when matching maturities perfectly. Even whenemploying a generous definition of maturity-matched safe assets that ignores counterparty risk
and permits small maturity mismatches, banks hold only $0.48 of foreign “safe” assets per $1
lent. A significant component of intermediaries’ currency exposure is, therefore, hedged withmaturity-mismatched safe assets and with risky assets. The choice or constraint to execute
imperfect CIP arbitrage explains why dollar funding is most expensive in markets wherebanks are doing the most dollar lending since those are the markets where intermediaries aretaking the most risk.
In terms of magnitudes, we find that a one standard deviation change in dollar borrowing
demand increases the magnitude of the basis by 4 to 9 bps, without accounting for any
cross-currency differences in banks’ abilities to access (maturity-matched) foreign safe assets.
In addition, there are differences in the cost of locating maturity-matched safe assets acrosscurrencies. We capture this cost using cross-currency variation in the number of dollars of
foreign safe assets held per dollar of swap exposure, which we call the safe asset ratio. We
find that a one standard deviation difference in the safe asset ratio corresponds to a further
increase in the basis of 7 to 9 bps.
Second, we find that currency market segmentation contributes to CIP bases. We definea market as a tenor × currency pair, so the 1-month EURUSD, 1-year EURUSD, and 1-year
JPYUSD are all distinct markets. For each market, we calculate a Herfindahl-HirschmanIndex (HHI) of bank exposure and find a strong relationship between more concentrated
markets and larger bases: a one standard deviation more segmented market has a 10 to 14
3

bps larger basis. Combined with our results on safe asset scarcity, the relationship between
segmentation and bases highlights the importance of risk—and intermediaries imperfectly
sharing it—as drivers of CIP deviations, which may also be amplified by market power.
We also identify segmentation by examining the extent to which bank-specific constraints
are reflected in bases. We use the March 2023 banking turmoil as a natural experiment totest the relationship between bank-specific shocks and the basis. There was a notable shift ofdeposits toward the largest US banks following the Silicon Valley Bank (SVB) turmoil. We
show that currency markets intermediated by banks with comparatively larger deposit inflows
had comparatively smaller basis dislocations. This result is consistent with the model’s
prediction that constraints facing banks who specialize in certain markets will transmit toprices in those markets, consistent with market segmentation on the supply side.
The presence and importance of segmentation are surprising in our setting since global
currency markets are among the largest and most liquid markets in the world. We show thatsegmentation is persistent, with market shares displaying persistence and more segmented
currency markets remaining so over time. Our model ascribes segmentation to heterogeneous
expertise in executing arbitrage across markets, where banks specialize in markets where
they have the most expertise. We provide support for this mechanism by showing thatbanks specialize in counterparty segments. For example, a bank might specialize in Canadianinsurance counterparties, while another may cater to Asian sovereign wealth funds. We
further show that banks hold more loans in the currencies in which they have large currency
market shares in FX swap markets, suggesting that banks have market-specific expertise anda more readily available set of counterparties in the markets they specialize in.
Third, we find that some currency markets have concentrated demand. Markets with aless diverse mix of counterparties also have larger basis dislocations, controlling for foreignsafe asset scarcity and segmentation. More concentrated demand is associated with larger
bases, consistent with banks managing counterparty risk. A one standard deviation increasein demand concentration has a 7 bps larger basis.
Analysis of the cross-section of CIP deviations reveals several novel and important frictions
affecting prices: scarcity of foreign safe assets and supply and demand segmentation. Thesefrictions matter for arbitrage activity even in global currency markets, which are some of thelargest and most liquid markets.

4

Related Literature Our work is most closely related to work on CIP deviations (Du et al.
(2018), Iida et al. (2018), Cenedese et al. (2021), Wallen (2022), Du and Schreger (2022),
Augustin et al. (2022)) and bank-intermediated arbitrage spreads (e.g., Garleanu and Pedersen(2011), Pasquariello (2014), Boyarchenko et al. (2018a), Andersen et al. (2019), Anderson
et al. (2021), Foley-Fisher et al. (2020)). Previous work focuses primarily on increased bankfunding costs that give rise to CIP deviations following the 2008 financial crisis, for example,
due to bank regulation (Du et al. (2018)) or debt overhang frictions associated with the
expansion of bank balance sheets (Andersen et al. (2019)).
In contrast, our work shines a light on the asset side of intermediaries’ basis trades using
unique data on quantities. Our findings indicate that either the difficulty in locating foreignsafe assets for use in CIP trades or the choice to invest in higher-yielding assets results inintermediaries holding riskier securities in the foreign legs of their basis trades, thus making
CIP arbitrage activity risky. This conclusion complements the findings of Diamond and
Van Tassel (2021), who suggest that convenience yields on foreign safe assets may help
explain CIP bases; and those of Liao (2020), who documents a strong relationship between
CIP deviations and differences in corporate credit spreads across currencies. Our results
are also related to Du et al. (2023a), who find that the security holdings of the banking
and insurance sectors in the Euro-area far exceed the amount of government debt, withinstitutions tilting their portfolios towards risky corporate debt. Furthermore, given the realcosts of CIP deviations (Du and Huber (2023)), our result that safe asset scarcity may help
drive CIP deviations highlights the real effects of safe asset scarcity consistent (e.g., Caballero(2006), Caballero et al. (2017), Caballero and Farhi (2018)).
Uniquely, we focus on cross-sectional variation in bases. We conclude that intermediaryheterogeneity (Kargar (2021)) and the accompanying segmentation of intermediaries into
different markets are important drivers of bases.2 Using supervisory regulatory data, we
provide direct evidence of segmentation’s impact in transmitting idiosyncratic, bank-specificconstraints into asset prices. This result is consistent with other work (e.g., Rime et al. (2022);
Siriwardane et al. (2022); Kloks et al. (2023)). However, our empirical evidence uniquely sheds
light on the source of segmentation. Because we find that CIP arbitrage is risky, our results2

A necessary ingredient is that there are differences in dollar demand via currency forwards, which, forexample, may arise from differences in currency hedging demand across currencies (e.g., Liao and Zhang(2021) and Du and Huber (2023)). Similar heterogeneity in demand also exists in other markets, for example,
equity index futures markets (Hazelkorn et al. (2023)).

5

highlight another channel through which segmentation affects bases—reduced risk sharing.
The evidence supports the idea that investors specialize in “complex asset markets” dueto market-specific expertise (e.g., Glode and Opp (2020), Eisfeldt et al. (2023), Bryzgalova
et al. (2023)), emphasizing that the riskiness of arbitrage—and differences in intermediaries’
abilities to reduce that risk—are key to understanding segmented arbitrage.
Lastly, our work contributes to the literature on intermediary asset pricing (Brunnermeierand Pedersen (2009), He and Krishnamurthy (2013), Adrian et al. (2014), Gabaix andMaggiori (2015), He et al. (2017), and Du et al. (2023b)) that emphasizes the intermediary
sector’s marginal utility as a state variable determining asset risk premia, due to households’
limited participation in asset markets. Our findings indicate that limited participation ispresent even within the intermediary sector itself and suggest that the marginal utility of
specializing intermediaries contributes to market risk premia and not just the intermediary
sector in aggregate.

2

Model

We present a stylized model to organize and interpret our empirical investigation. The modelyields a set of predictions on the cross-sectional drivers of CIP bases and illustrates how
different frictions give rise to variation in bases.

2.1

Setup

There are Nk foreign currencies (against the USD), indexed by k = {1, . . . , Nk }. For simplicity,each currency faces a unitary exchange rate versus the USD. There are two types of investors:
Ni financial intermediaries, indexed by i = {1, 2, . . . , Ni } and Nc customers, indexed byc = {1, 2, . . . , Nc }. There are two periods, t = 1, 2. All investors invest in period 1 and realize
payoffs in period 2.
The U.S. offers safe bonds in perfectly elastic supply, with returns normalized to zero.
Each foreign currency features three types of assets: one-period currency forwards, safe bonds,
and ‘risky’ bonds, which are imperfect substitutes for safe bonds and which investors faceidiosyncratic risk to invest in, as detailed below. All risky foreign bonds offer a return of r.
Safe foreign bonds in currency k offer expected returns of rk ≤ r. Currency forwards are in6

zero net supply, and their price is endogenously determined.
Customer Demand for Currency Forwards and the Basis. Customers only transact
in currency forwards. In period 1, customer c exogenously demands to synthetically swap Xc,kdollars from currency k into USD via forwards. In aggregate, synthetic dollar demand to swap
currency k for dollars is given by Xk =c=1 Xc,k . For simplicity, we assume that customers’

PNc

trades are netted out, such that sign(Xk ) = sign(Xc,k ), ∀c, k. The price of currency forwards
for currency k is given as Pf,k .
The basis for currency k is the price of the forward contract minus the expected returnfrom borrowing in USD and investing in foreign safe assets:
Basisk ≡ Pf,k − rk .
With exogenous risk-free rates, the basis is determined by the forward price.
Foreign Bonds and Safe Asset Scarcity. There is a sufficient supply of safe bonds tohedge all currency trades, but each intermediary i faces a total search cost of 12 λs,k s2i,k tolocate safe bonds in currency k, where si,k is the safe bond position of intermediary i incurrency k and λs,k is a coefficient that captures how quickly search costs increase in currency
k. That is, safe bonds in currency k become increasingly difficult to locate as demand forthem increases.
Intermediaries may also take positions in risky bonds of each currency, in perfectly elastic2
supply. Intermediary i faces an idiosyncratic payoff variance of σi,kwhen purchasing risky
2bonds in currency k. Intermediaries with lower σi,kfor a given market face less risk in theirbasis trades when substituting away from safe bonds in the cash legs of their basis trades. Thisfeature can be interpreted as intermediaries having “market-specific expertise” in substituting
away from safe assets. The heterogeneity in risk faced by different intermediaries may reflect,for example, differences in technologies across intermediaries in producing information about
issuers in a given market, differences in access to counterparties, or alternatively differencesin trade execution (Glode and Opp (2020), Eisfeldt et al. (2023)).
Intermediary Hedging and Safe Asset Choice. Each intermediary i maximizes amean-variance objective function, E(Wi,2 ) − γ2i V(Wi,2 ), where Wi,2 is the terminal wealthof intermediary i, E(·) and V(·) are the expectations and variance operators, and γi is a7

coefficient that captures the risk-bearing capacity of intermediary i. Intermediaries take theother side of customer demand in synthetic funding markets. Intermediary i takes a positionof Zi,k in currency forward k. Forward market clearing is given byP

i Zi,k +c Xc,k = 0, ∀k.

PIntermediaries are constrained to fully hedge their currency exposure from meeting forward
demand via cash bonds. To satisfy their first-order conditions, intermediaries’ allocations
to safe and risky bonds in currency k must make them indifferent to obtaining marginal
hedge positions in either. This means that given their total position of −Zi,k in cash bonds of
r−r +λks,kcurrency k, intermediary i allocates a proportion αi,k ≡ λs,k +γ
to risky bonds and allocates
2
iσ
i,k

the remainder of their hedge position to the safe bond. Given their positions, intermediaryi’s profits in currency k are given by −(αi,k r + (1 − αi,k )rk − Pf,k )Zi,k .
Each intermediary also faces a set of constraints that we detail below.
Balance Sheet Cost: Intermediary i faces increasing marginal costs to expand its balance
sheet (e.g., because of regulation or debt overhang). This is captured by each intermediaryfacing a cost of the form
2
X
1
λBS
|Zi,k | .
2
k

!

(1)

We assume quadratic costs for mathematical ease, but conceptually, our results depend only
on the fact that constraints are (weakly) convex.
Counterparty Constraints: Intermediary i pays increasing marginal costs to meet demand
from specific counterparties. All else equal, intermediaries prefer to equalize positions acrossdifferent counterparties for diversification purposes.
Here, we assume that for each currency k and customer c, intermediaries’ counterparty
position is directly proportional to their holding in currency k, i.e.,
Zi,k,c = Zi,k

Xc,k
Xk

(2)

where Zi,k,c is defined as intermediary i’s position in currency k opposite customer c. For
each counterparty c, intermediary i faces a cost of the form
2
X
1
λCP
|Zi,k,c | .
2
k

!

(3)
8

Fixed Participation Costs: Each intermediary pays a fixed participation cost of λP C,k totrade in currency k. This can be thought of, for example, as the cost of setting up a trading
desk for currency k.

2.2

Model Predictions

We can write financial intermediary i’s problem asmax

XZi,k ,αi,k ∀k−−(αi,k r + (1 − αi,k )rk − Pf,k )Zi,k

k

X γi

2
(αi,k Zi,k )2 σi,k(Risk)

2
X1−
λs,k ((1 − αi,k )Zi,k )2
k 2
k(Safe Asset Scarcity)

λP C,k 1{Zi,k 6=0}

(Fixed Participation Costs)

2
X
1
− λBS
|Zi,k |
2k(Balance Sheet Costs)

−

X
k

!

2
X X
1
|Zi,k,c | .
− λCP
2
c
k

!

(Counterparty Costs)

Our first set of predictions does not rely on intermediary heterogeneity, so for clarity of
exposition, we (1) set fixed participation costs to zero and (2) assume there is no heterogeneity
across intermediaries in risk-bearing capacity or in risk faced (i.e., σi,k = σk for some value
σk and γi = γ for some value γ, ∀i, and ∀k).3
3

In the appendix, we present an expression for the basis without this simplification, which results in amore complicated expression that yields the same predictions as presented here.

9

Taking the first order condition with respect to Zi,k , and averaging across intermediaries,(r − rk )2 + (r − rk )λs,k Xk (r − rk )2 + λs,k γσk2+
λs,k + γi σk2
Niλs,k + γσk2
Sign(Xk ) X+λBS|Xk0 |
Ni
k0

Basisk =

+λCP(Safe Asset Scarcity)(Balance Sheet Costs)
2
X Xc,kX Xc,k X

Sign(Xk )  |Xk |+
|Xc,k0 |

.
2
Ni


c Xk
c Xk k0 6=k





|

{z

(Counterparty Costs)}

Demand Conc. in k

This expression for the basis yields the following predictions:
Prediction 1 (Sign of the Basis). The direction of synthetic demand for dollars from currency
k determines the sign of the basis.
Prediction 2 (Balance Sheet Costs). The magnitude of the basis is increasing in the totalbalance sheet usage of intermediaries across basis trades,

k0 |Xk0 |.

P

4Predictions 1 and 2 are predictions of standard explanations for CIP deviations. The basis
for each currency contains a common balance sheet cost component that reflects the marginal
cost that the intermediation sector faces in expanding its balance sheet. The post-GFC
increase in the magnitude of CIP bases reflects the increase in the magnitude of balance sheet
costs (e.g., an increase in λBS ).
The model also yields novel, cross-sectional predictions:
Prediction 3 (Safe Asset Scarcity). The cost of synthetic dollar funding for currency k isincreasing in demand to borrow dollars from currency k via forwards (more positive bases).
Corollary 1 (Heterogeneity in Safe Asset Scarcity). All else equal, currencies where foreignsafe assets are harder to find (high search costs, λs,k ) have larger magnitude bases. On average,
dollar funding is more expensive in currencies where safe rates (rk ) are lower.
Prediction 3 is novel and makes a prediction about the cross-section of bases that arise
from risk and safe asset scarcity. The basis embeds the search cost that intermediaries pay to
4

In principle, this reflects the balance sheet usage of the financial system.

10

locate foreign safe bonds, where the marginal search cost (and distaste for risk) is increasingin the amount of demand banks must intermediate.
Corollary 1 extends this prediction to consider the possibility that search costs for foreignsafe assets may systematically vary across currencies. For currencies with higher searchcosts, bases are larger per unit of demand, reflecting those costs and the additional risky
substitutions that intermediaries must make. A related point is that in currencies where
safe assets are scarce, foreign safe interest rates are low (Caballero (2006); Caballero et al.
(2017)); and as a result, we expect more substantial CIP deviations in those currencies in theform of more expensive dollar funding, due to intermediaries optimally choosing to substituteinto higher-yielding risky assets.
Prediction 4 (Demand Concentration). The magnitude of the basis for currency k increases
in the concentration of demand to swap foreign currency for dollars across counterparties c.
Prediction 4 reflects the fact that intermediaries demand larger compensation for concentrated demand from counterparties, due to more idiosyncratic counterparty risk.
For our last set of predictions, we focus on heterogeneity across intermediaries. We reintroduce non-zero fixed participation costs, heterogeneous risk in substituting away from safe
bonds, and heterogeneous risk-bearing capacity. For clarity of exposition, set λBS = λCP = 0
and r = rk .
Given fixed participation costs, intermediary i participates in currency k in equilibrium
only when it faces sufficiently low risk (σi,k ) in substituting away from risk-free assets incurrency k.5 The basis can be expressed as
Basisk = Np,k

+
λs,k

Xk
PNp,k
i

1
2
γi σi,k,where Np,k ≤ Ni is the number of participating intermediaries. Segmentation arises becausesome intermediaries cannot justify the fixed participation costs given the risk they face insubstituting away from currency k safe assets.
5

Derivations contained in Appendix A.1. In the main specification, as a consequence of customer demand
being perfectly inelastic, bases are exactly equal to intermediaries’ marginal costs including risk. Hence,
intermediaries with too low σi,k will also avoid participation because their capacity to execute the trade at
low risk may drive profits toward zero. However, this lower bound on σi,k disappears when customer demandis price sensitive, as in an extension presented in the appendix.

11

Prediction 5 (Supply Segmentation). The basis for currency k is larger in magnitude whenintermediary supply for currency k is more segmented (smaller Np,k ).
Prediction 5 arises from a combination of foreign safe asset scarcity, heterogeneity inthe risk faced by intermediaries in different markets, and fixed participation costs. It is a
novel and distinct prediction from other work on bases that suggests that funding markets
may be segmented (e.g., Rime et al. (2022); Siriwardane et al. (2022)). This prediction
arises in our model because CIP arbitrage is imperfect, and intermediaries take risks in theirpositions. With segmentation, intermediaries are less able to share risk and accordingly
demand larger compensation for meeting customer demand.6 An additional channel that
amplifies the risk-sharing effect is market power (Wallen (2022)), which also appears in a
simple Cournot-style variant of our model presented in the appendix. Segmentation may be
symptomatic of intermediary market power, which can create larger bases via markups.7
While we do not micro-found the sources of heterogeneity in our stylized model, we discussthese sources in our empirical analysis. We document that intermediaries that hold large
market shares in a particular currency market also tend to hold larger loan portfolios in thatmarket. This evidence suggests that lower risk in substituting away from safe assets may be
driven by expertise, such as informational advantages coming from greater familiarity with agiven market or a more easily accessible set of counterparties.8
Prediction 6 (Specializing Intermediaries’ Risk-Bearing Capacity). The basis for currency
k reflects the risk-bearing capacity of intermediaries that specialize in currency k.
When intermediaries specialize in different markets, the risk-bearing capacity of thespecializing intermediaries in a market is the relevant driver of bases in that market, not therisk-bearing capacity of the overall intermediary sector. Siriwardane et al. (2022) present
suggestive evidence consistent with this prediction by examining the correlations of different
arbitrage strategies across asset classes. We present direct evidence for this prediction within
an asset class, using detailed and direct data on intermediaries’ participation across markets.
6

This prediction is related to an implication of Eisfeldt et al. (2023), that participation should be lower inmarkets that have higher Sharpe ratios.
7
Because market power and risk-sharing make the same directional predictions, we cannot empiricallydisentangle the roles they play. Neuhann and Sockin (2023) present a dynamic model to study the ‘risk-rent’
tradeoff faced by large strategic traders (e.g., intermediaries).
8
Bryzgalova et al. (2023) similarly argue that intermediaries tend to specialize in options markets that are
their ‘natural markets,’ due to low fixed costs of entry from related business areas or economies of scale.

12

Corollary 2 (Safe Asset Ratios Across Intermediaries). Intermediaries with a larger sharein a given currency market hold fewer foreign safe bonds per dollar of lending (for currency
k, intermediary i’s allocation to risky bonds, αi,k , is larger when intermediary i’s position,
Zi,k , is larger in magnitude).
Corollary 2 arises from the fact that intermediaries hold larger positions in markets where
they face lower risk in substituting away from safe assets in the cash legs of their basis
trades. Intermediaries with a higher willingness and ability to substitute into risky foreign
bonds have a higher capacity for meeting currency forward demand and, accordingly, havehigher market shares. We note that Corollary 2’s prediction of lower shares of foreign safe
bonds for specializing intermediaries is distinctive from the prediction of another form ofexpertise by specializing intermediaries: lower search costs for safe bonds. An alternative
search-cost-based explanation would predict, if anything, a higher share of foreign safe asset
holdings by specializing intermediaries.
We turn next to describing the data used to test these predictions.

3

Data

We collect information on bank-specific FX positions from the FR 2052a Complex Institution
Liquidity Monitoring Report and use Bloomberg for exchange rates, prices, and interest rates.

3.1

FR 2052a Complex Institution Liquidity Monitoring Report

The Federal Reserve collects granular data on banks’ liquidity in the FR 2052a as part of its
capital adequacy framework as required by the Dodd-Frank Act and implemented by theFederal Reserve’s regulation YY. The data are confidential and not publicly available. The
data provide information by asset class, outstanding balance, and purpose, each reported
by maturity and date. The data cover large U.S. and foreign banks. Global systemicallyimportant banks (G-SIBs), category II, and category III banks with a weighted average shortterm funding of $75 billion must file the report each business day. Smaller banks report datamonthly.9 Banks have a strong incentive to report data fully and truthfully because of the9

For details on regulation YY and FR 2052, see https://www.federalreserve.gov/supervisionreg/
reglisting.htm. The reporting instructions for FR 2052a are available at https://www.federalreserve.

13possible consequences of making misrepresentations to government authorities, which could
result in enforcement actions. Unlike other public companies, regulators closely scrutinizebanks, so any misreporting would likely be identified and corrected quickly. Researchers haverecently begun using this dataset to study bank behavior: for example, Infante and Saravay(2020), Cooperman et al. (2023), and—more related to our work—Correa et al. (2020).
We use the data on banks’ foreign exchange swaps and forwards.10 Our sample includes
daily observations from January 2016 through March 2023, spanning over-the-counter (OTC)
and centrally-cleared transactions. Roughly 85 percent of the gross notional positions in
our sample are settled bilaterally on a value-weighted basis, while the rest clear centrally.
Firms report FX transactions for eight currencies: AUD, CAD, CHF, EUR, GBP, JPY, USD,
and other. The data cover cash-settled transactions settled with the physical exchange ofcurrency, so it does not include contracts for difference or other non-deliverable transactions.
The swaps in our sample include both FX forward swaps and cross-currency swaps, where thelatter involve periodic interest payments in addition to the exchange of notional currencies atthe beginning and end of the transaction. The data report maturities at daily increments up
to 60 days, weekly increments from 61 days to 90, monthly increments to 180 days, 6-monthincrements to 1 year, and yearly increments beyond that. We discuss additional data cleaningin Appendix A.2.
We focus on the subset of the data with transactions in the seven currencies against
the dollar. On average, our sample covers $25 trillion in gross notional daily contracts
across foreign exchange swaps and forwards. We plot the daily sample average by currency in
Figure 1. The sample is large and represents a material slice of the foreign exchange derivative
market. While not an apples-to-apples comparison, Bank of International Settlements (2022)
estimates the total notional amount of OTC foreign exchange derivatives at $110 trillion in
2022. Euro contracts are the largest ($9 trillion), and Swiss Franc contracts are the smallest($1 trillion). The tenors with the largest notional amounts are at the weekly increments—7,
14, 21, 28 days—and steadily grow in total beginning with 6-month tenors. We limit oursample to maturities less than 4 years since the 5-year bucket contains all maturities at five
years and beyond.
gov/apps/reportingforms/Report/Index/FR_2052a. Additional details on which large financial institutions must report daily are at https://www.federalreserve.gov/supervisionreg/large-institutionsupervision.htm.
10
The data on forwards includes forwards and futures. We will refer to them as forwards for brevity.

14

We also collect data on banks’ safe assets. We focus on unencumbered assets (assets thatface no restriction on use as collateral), assets pledged to central banks against which thebank could borrow, unrestricted central bank reserve balances, unsettled asset purchases, andencumbered assets (assets restricted from use as collateral). Encumbered assets are available
only from mid-2022. Across these categories, we measure banks’ safe assets as the subset ofthe level 1 high-quality liquid assets (HQLAs). HQLAs are securities considered the closest
proxy for risk-free securities if held to maturity. While the dataset does not provide the asset
CUSIPs, it does provide collateral categories. In some instances, we include a broader set ofassets beyond just level 1 HQLA.

3.2

Covered-Interest Parity Violations

We calculate covered-interest parity violations using interest rates, spot exchange rates,
forward points, and forward maturity dates from Bloomberg. We use OIS interest rates acrossthe maturity curve from 1 week to 4 years. Details on cleaning the OIS interest rate data areprovided in the online appendix.11 We calculate CIP violations following Du et al. (2018).
Define st as the spot exchange rate in units of foreign currency per US dollar available at date$t, yt,t+n
as the dollar interest rate available on date t and maturing at t + n, and ft,t+n as then-period outright forward exchange rate in foreign currency per USD. The CIP basis is,$Basist,t+n = yt,t+n− yt,t+n −1(ft,t+n − st ) .
n


(4)

When the basis is negative, Basist,t+n < 0, dollar arbitrageurs can profit by borrowing at
USD interest rates, simultaneously converting their USD to foreign currency at st , buying aforward ft,t+n to exchange that foreign currency back into dollars at maturity, and investing
abroad at the foreign interest rate. Intuitively, an investor should be indifferent between$
holding USD to earn yt,t+n
and exchanging USD for foreign currency invested at the foreignrisk-free rate and converting the foreign currency back to USD by buying a forward.
Figure 2 plots our estimates of CIP bases at the 1-week and 1-year tenor. Dislocations
are apparent during the 2008 financial crisis and the early stages of the Covid pandemic. We
11

The specific tenors we use are 1w, 2w, 3w, 1m, 2m, 3m, 4m, 5m, 6m, 1y, 2y, 3y, and 4y. We exclude the
5y tenor because the longest maturity category in the FR2052a data is five years and greater, so its average
maturity is likely much more than five years.

15

also provide the average and standard deviation of the 1-year CIP bases in Table 1. The
basis averages −24 bps across all currencies but ranges from +6 for AUD to −50 for JPY.
The first and second moments of the bases do not change much if we restrict the sample tobegin in 2016 when our bank data sample starts.
We merge the Bloomberg basis panel data with the FR 2052a panel data using the days
to maturity. For contracts with at least a month of maturity, we merge the panels using thecommonly reported days to maturity in the FR 2052a data.12
Table 2 provides the daily average and standard deviation by currency and by tenor after
merging with our estimates of CIP deviations using Bloomberg data—which limits the sample
to the tenors of the OIS contracts. The daily average gross notional of the merged sample is$10.7 trillion. Forwards are larger at maturities less than six months, while swaps are larger
beyond that. Shorter tenors also have more volatility in volumes. For example, the 1-weekswap averages $91 billion with a standard deviation of $78 billion.

4

Empirical Strategy and Results

We focus on the cross-sectional variation in currencies’ bases. Constructing a measure ofdollar lending against different currencies, we show that banks lend the most dollars in thecurrency and maturity markets with the largest CIP deviations. As indicated by our model,
this result is consistent with banks facing increasing marginal costs to meet currency- andmaturity-specific demand for dollar borrowing, or with banks seeking higher returns fromlending in specific currencies and maturities.
We use the model to guide an empirical decomposition of the basis into safe asset scarcity,
segmentation, and counterparty concentration in giving rise to bases. Finally, we use theevent surrounding the March 2023 Silicon Valley Bank (SVB) turmoil to show how demand
shocks trace through net dollar lending and their effects on bases.
12
Specifically, 1m is 28 days; 2m is 61 days; 3m is 83 days if the days to maturity are between 83 and 90,
inclusive, and 91 days if the days to maturity are greater than 90 days but less than 120 days; 4m is 121
days; 5m is 151 days; 6m is 181 days; 9m is 271 days; 1 year is 366 days; 2 years is 731 days; 3y is 1096 days;
4y is 1461 days.

16

4.1

Signed Time-series Variation of Bases

Figure 3 plots the cross-sectional standard deviation across currencies on a given day. The
variance across bases increases in tenor. The cross-sectional dispersion varies over time, withnotable spikes during the financial crisis and Covid pandemic. The existence of bases withdifferent magnitudes and signs across currencies and tenors at a point in time is prima facie
evidence that forces beyond an aggregate intermediary balance sheet constraint matter forthe bases. While aggregate leverage and other balance sheet constraints are important, theycannot capture the cross-sectional variation in bases observed in the data.
Our model offers insights into other drivers of the basis that help explain the cross-sectionalvariation. Prediction 1 states that the sign of demand for dollars from a currency determines
the sign of the basis. The summary statistics in Table 1 support the prediction. The AUD
basis is positive while the others are negative. The rank ordering is consistent across tenors:
AUD is typically the largest, CAD the second largest, and JPY the smallest. Prediction 1
explains this rank order since AUD and CAD have the least dollar demand and JPY themost. That different foreign economies have different dollar demands and are willing to
pay different prices for dollars, is intuitive. For example, countries with large commodity
exports invoiced in dollars, like Australia and Canada, have different demand for dollars thancountries without similar commodity-related dollar inflows.13
Prediction 2 states that the size of the basis—in absolute value—is decreasing in thebanking system’s balance sheet capacity across all basis trades. The size of the basis reflects
dislocations that prevent the banking system from pushing the basis back toward zero. Such a
prediction is consistent with work that studies the important role the aggregate intermediary
sector plays in basis dynamics (e.g., Du et al. (2023b)).
Predictions 1 and 2 reflect (signed) time-series variation in bases, the primary focus ofearlier work. We calculate the first principal component of the bases which we use to proxy
for an intermediary-wide factor. Figure A1 plots the variance across the bases in each tenor
that can be explained by the first principal component. The figure shows that the common
comovement across all the bases is important, but there is significant variation left to be
explained. The focus of our paper is on the cross-sectional variation in the size of bases,
13

Du et al. (2018) also discuss dollar demand in terms of interest rates—AUD is the highest interest ratecurrency, and therefore expected to have the least carry trade demand for dollars, and vice-versa for JPY.

17

which accounts for the remaining variation in bases.

4.2

Net Dollar Lending

Our remaining predictions depend upon the intermediaries’ dollar lending across currencies.
We begin by constructing a measure of net dollar lending for each market.
4.2.1

Construction

We use the FR 2052a data to calculate banks’ net lending position for each market, where we
define a market as a specific currency × tenor. We calculate banks’ net USD supplied fromdate t to t + n for a given currency pair k via FX swaps using:
Netkt,t+n =(USD in at t + n) − (USD out at t + n).
(USD in at t + n) + (USD out at t + n)

(5)

Table 3 provides a simple example to illustrate the logic underpinning Netkt,t+n construction.
Suppose a bank is buying and selling JPY swaps, with spot rate St = 115 and forward
exchange rate Ft,t+7 = 110. In the first swap, the bank lends $100 dollars at the near leg andreceives $100 × St = U11, 500. Separately, and simultaneously, the bank receives $95 in asecond swap and pays $95 × St = U10, 925. The bank has paid $5 more than it received,
equivalent to lending $5. At maturity, the two swaps unwind at the forward price. The net
variable is the ratio of the net dollars lent to the notional dollars: 2.56% = 5/195 = 5.23/204
at time t + 7. Notice that the net variable is the same regardless of whether it’s based on thenear or far leg flows (t or t + 7).
When Net kt,t+n > 0, the bank lends out more dollars today than it borrows against currency
k with maturity t + n, because the bank will receive more dollars in at maturity on t + n
than it pays out. We normalize by notional dollar flows since the size of markets varies.14
14

Although net lending is possible through swaps and a combination of spot transactions with forwards, we
calculate net using only swaps since there is no upfront exchange of principal for forwards and futures, andwe are unable to connect forward transactions with spot transactions forming basis trades. We expect our
results to be unaffected if forward and future demand is directionally similar to swap demand. We find little
evidence that including forwards and futures meaningfully affects our results, for example, in our analysis ofsafe asset ratios in Appendix Table A4. Similarly, our net lending measure excludes intermediate interest
payments since we are unable to match those payments to the underlying swap.

18

We aggregate the net lending measure at two levels. Net kt,t+n is our primary measure,reflecting the entire intermediary sector by aggregating lending across all banks at the date× maturity × currency level. We also calculate a bank-specific measure (indicated by thei superscript), Net k,it,t+n , that aggregates at the date × maturity × currency × bank level.
Since banks report only non-zero values, we set net lending to zero when there is no lending
or borrowing data for a given observation.
4.2.2

Net Summary Statistics

Table 4 gives the average and standard deviation of Net kt,t+n by currency and tenor. With few
exceptions, Net kt,t+n is small and near zero, indicating that the intermediary sector broadly
matches its dollars in and out. The table highlights three facts: first, the intermediary sector
tends to net borrow dollars at shorter tenors and net lend at longer tenors, with the average
flipping from negative (net borrowing) to positive (net lending) around 3 months. Second,
there is considerable variation in net dollar lending across the currencies—averaging acrossall tenors, intermediaries tend to borrow in AUD (on average Net AUD
t,t+n of 3.6 percent) andlend EUR (at 1.1 percent). Third, net lending is more volatile at shorter maturities than it is
at longer maturities. At less than a month, the average time-series standard deviation is 30
percent compared to less than 10 percent for maturities beyond 5 months.
Appendix Table A1 shows the level of Net kt,t+n in billions of dollars. Average Net kt,t+n islargest in level terms for JPY at $5.4b, and smallest for AUD at −$1.3b. Adding acrossthe rows shows that banks net lend dollars most against JPY ($75b) and EUR ($44b) whileborrowing the most against AUD (-$18b). Adding the columns together yields the average
net dollar provision by banks in these currencies and markets: $98b.
The net dollar lending of the banks in our sample is substantial. One way to contextualize
it is by comparing it to the total dollars the Fed provided through central bank swap lines
during the worst stage of the COVID-19 pandemic—a period when the dollar shortage wasparticularly acute. The Fed’s swap lines peaked at $450 billion in May 2020, compared to ourroughly $100 billion of average lending, most of which occurred outside stressed periods.15
Appendix Table A2 shows that Net kt,t+n is increasing in maturity, size, and an analogous
measure of net lending provided using the coarser data from the Traders in Financial Futures
15

The Federal Reserve’s swaps are publicly released in the H.4.1, see https://www.federalreserve.gov/
releases/h41/.

19

Report from the Commodity Futures Trading Commission, as used in Hazelkorn et al. (2023).
It is lower at quarter- and month-ends. Finally, Net kt,t+n is weakly pro-cyclical, given that it islarger when the VIX and the Baa-Aaa spread are lower. There is no obvious relationshipbetween Net kt,t+n and the SPX return.
To illustrate the variation over time and across tenors, we plot the net variable for 1-week,
6-month, and 1-year EUR in Figure A2. At shorter maturities, net lending is normally
negative but often turns positive for brief periods. The middle panel shows the level of net
lending in billions of dollars. Average net lending at the 1-week tenor is −$1.2 billion but
grows to $12 billion at the 1-year tenor. The bottom panel shows the gross notional dollar
flows—the denominator of our net variables. There is an increasing trend across all tenors,
but the trend is most obvious at the 1-year maturity, where notional values approached $800
billion in 2023. The recurring spikes in the shorter maturities reflect window dressing.
Figure A3 shows a histogram of Net kt,t+n by currency across all tenors. The peaks near
the zero net lending line indicate that the banking system generally runs a matched book,lending as much as it borrows. The figure makes clear that we expect Net kt,t+n to be near zeroor tightly bounded around zero, rather than large directional positions, either long or short.16

4.3

Net vs. Bases

We test Prediction 3, that banks lend more in markets where dollar funding is most expensive
by testing whether cross-sectional variation in Net kt,t+n captures cross-sectional variation inbases. We run the regression,
Basiskt,t+n = α + βNetkt,t+n + γ0Xt + εkt,t+n(6)where a negative basis indicates that swapping foreign currency for dollars is expensive, and
Xt is a vector of controls. Our model predicts β < 0, because bases should be the mostnegative in markets with the most dollar demand. This relationship arises from the searchcosts and risks that intermediaries face when supplying dollars in different markets, due tothe scarcity of foreign safe assets.17
16

In the online appendix, Figure A4 shows that net is closer to zero for the largest markets.
β < 0 is implicitly assumed in several other papers, for example, Greenwood et al. (2023), Liao andZhang (2021) and Du and Huber (2023). An analogous result is also shown to be the case in equity index
17

20

Table 5 reports the regression results, with β reliably negative across all specifications
using different controls. The first row shows a robust negative relationship between bases
and Net kt,t+n after including tenor and time fixed effects and weighting by the square root of
the market’s share of the total daily gross notional. We use weighted least squares regression
since markets can differ substantially in size. Column (4) is the benchmark estimate which
includes the full set of fixed effects and weights by notional share. The coefficient showsthat when Net kt,t+n is 1 percentage point (pp) larger, the basis is 0.4 bps smaller. A onestandard-deviation change in Net kt,t+n weighted by its daily notional share is about 10pp,
corresponding to a basis that is 4.3 bps lower using column 4’s coefficient. Columns (5) and(6) split the sample into short- and long-term tenors, with a threshold of one year. The
relationship is much stronger for longer-tenor lending, with a coefficient roughly 9 times
larger than short-dated tenors. A one-standard-deviation-change in Net kt,t+n for these longer
tenors corresponds to a basis that is 9 bps lower (5.9 × −1.5).
In the online appendix, we provide additional results on the relationship between bases
and lending. Figure A5 scatterplots the average basis against the average Net kt,t+n for a given
currency and tenor, and Figure A6 shows the average basis and Net kt,t+n for each currency
at several tenors. Second, we show the regression coefficients in Figure A7. The regression
coefficient on Net kt,t+n is near zero for maturities less than 3 months but is significant andnegative for all longer maturities.
The cross-sectional relationship between Net kt,t+n and bases is consistent with our model.
Intermediaries’ (shadow) costs that increase with dollar funding demand from a particularcurrency drive cross-sectional variation in bases. The remaining predictions of our model
explore the importance of different constraints in contributing to these costs.

4.4

Frictions Contributing to the Basis

We identify three potential sources that influence the size of the basis: foreign safe asset
scarcity, intermediary segmentation, and demand concentration. We construct proxies foreach measure, which we use together in a regression to jointly test their ability to explain the
cross-section of bases.
futures markets by Hazelkorn et al. (2023).

21

4.4.1

Foreign Safe Asset Scarcity

Prediction 3 links bases with dollar funding demand because it is either difficult for intermediaries to find foreign safe assets or because they seek higher returns by taking on some risk.
In this case, CIP arbitrage will be executed imperfectly if dollars lent are not matched $1 for$1 with foreign risk-free assets at the same maturity.
Intermediaries do not appear to be executing perfectly hedged CIP arbitrages. We findthat on average, intermediaries hold only 5 cents of perfectly maturity-matched foreign safe
assets per dollar of net lending. Banks put the rest of the cash leg of their CIP trades toward
maturity-mismatched safe assets and foreign currency risky assets, taking on risk to meetcustomer demand in currency swap markets.
To illustrate this point, we define foreign safe assets as the sum of level 1 HQLAs thatare unencumbered assets, unsettled asset purchases, and assets pledged to the central bank.
We include a full list of types of level 1 HQLA securities in the online appendix section A.2.
Sovereign bonds make up most of these securities. The BIS describes HQLAs as assets that“can be easily and immediately converted into cash at little or no loss of value.”18 The measureis specific to the market: there are daily observations for each currency and tenor. We define
Safe Asset Ratiokt,t+n =Level 1 HQLAs ($)kt,t+n
Net ($)kt,t+n.

If Safe Asset Ratiokt,t+n = 1, then banks perfectly match every dollar of net dollar lending for
currency k on date t with maturity t + n to a dollar of foreign safe asset in the same currency
and the same tenor. Safe asset ratios less than one indicate that intermediaries’ swap andcash positions do not offset one another and that the intermediary holds an imperfect CIP
position involving some risk.19
To better understand where intermediaries may place their foreign currency, we calculate
18

See https://www.bis.org/basel_framework/chapter/LCR/30.htm.
The denominator of safe asset ratios uses the maturity value of net swap lending rather than the present
value, and the numerator is the market value of the securities. Discounting this maturity value of net swap
lending to present value does not substantively affect the computed safe asset ratios due to the short maturityof the assets and low interest rates in the sample.
1922

a broader definition
Broad Asset Ratiokt,t+n =Broad Assets ($)kt,t+n
Net ($)kt,t+n,where Broad Asset Ratiokt,t+n covers assets beyond HQLAs in the three categories listed above.
It includes unencumbered assets, unsettled asset purchases, capacity, central bank deposits,
reverse repurchases, or securities borrowings.20 For example, a German government bondwould be in both ratios, but a German corporate bond would show up only in the broad asset
ratio since it is not a level 1 HQLA. Beginning in 2022, the data includes encumbered assets,
and we treat this subsample separately as a point of comparison given the large value of thebanking system’s encumbered assets.21 Note that these measures are gross long positions, sothey are an upper bound on the banking system’s net position in these markets. If a bankwere short the security, their net long measure would be lower.22
We separately address cases when banks net lend dollars from cases when banks net
borrow dollars. When Net kt,t+n > 0, banks lend dollars, receive foreign currency, and therefore
have demand for foreign safe assets. When Net kt,t+n < 0, banks lend foreign currency, receive
dollars, and thus need USD-denominated safe assets. We separate these two cases in thefollowing analysis since we have strong priors that USD safe asset holdings are different for
U.S.-based G-SIBs.
20

Unencumbered assets, unsettled asset purchases, capacity, and central bank deposits are market values.
Reverse repurchases and securities borrowings—along with short-term investments and firm shorts, which we
use later for robustness—are reported by amounts due at maturity rather than market value; since theseare short-term with negligible credit risk, we do not discount them into present value terms and instead, we
follow market convention to report these instruments at carrying value since it closely approximates fair value(see JP Morgan 10-K, 2022, pg 186).
21
Beginning in 2022, the data also begins including a new category of asset, short-term investments, which
includes time deposits held with other financial counterparties. We include this category in the post-2022
subsample, although it is small compared to the other asset categories included in our definition of broad
assets.
22
The broad asset ratio measure may include double counting when the bank pre-positions collateral it
receives from a secured financing transaction, like a reverse repo, with a central bank. For example, if abank lent AUD in a 1-month repo and pre-positioned the AUD-denominated collateral (say with a 2-year
maturity) it received with a central bank, then our broad ratios for 1-month and 2-year AUD would reflect
this exposure. The data does not allow us to distinguish if the pre-positioned securities are received througha secured financing transaction. For robustness, we calculate the broad asset ratios excluding pre-positioned
securities. The correlation between the broad asset ratios, including and excluding capacity is 0.997, so theeffect of any double counting is tiny.

23

Figure 4 summarizes our estimates of safe and broad asset ratios, which illustrate thepresence of foreign safe asset scarcity. This analysis focuses particularly on the case where
Net kt,t+n > 0, where intermediaries have demand for foreign safe assets. The top panel reports
the average median Safe Asset Ratio across currencies, which is 0.05. Intermediaries hold 5cents of unencumbered, maturity-matched safe assets per dollar of net lending. The panel
plots the ratios constructed using two alternative methods. First, plotted in orange, we
include forwards in the estimate of FX net lending, since a spot transaction paired with aforward transaction can be economically similar to an FX swap. The data don’t allow us topair these two separate transactions together, so including forwards will likely overestimate
the amount of net lending. Second, plotted in light blue, we calculate asset ratios net of thebanks’ short positions in those securities. Both methods yield nearly identical results.
The definition of maturity-matching used in the construction of the safe asset ratio isrestrictive because it requires the maturity of safe assets to exactly match the maturity of
net swap lending. For example, a 6-day maturity safe asset does not count as hedging 7-day
maturity swap lending. We relax this restriction by rounding the tenor of safe assets and FX
lending to the nearest benchmark tenor for which we have estimated CIP violations, and weround tenors less than 7 days to the 1-week bucket.23 For example, we round a 2-day swap
to the 1-week bucket, a 10-day swap to the 1-week bucket, and a 9-month swap to the 1-year
bucket. The resulting safe asset ratios allow small deviations in intermediaries’ cash positions
to count toward hedging their swap market positioning.
Figure 4 reports the rounded tenor results for safe asset ratios in the top panel. The
average median safe asset ratio increases to 0.17, indicating that small maturity mismatches
can account for an additional 12 cents of safe asset positioning, but is still markedly below 1.
We further relax the definition of safe assets to include encumbered assets—which cannot
be used as collateral, entail counterparty risk, and are not counted as level 1 HQLAs—butwhich may help intermediaries hedge the currency exposure coming from their net lending.
Figure 4 illustrates that this more permissive definition of the safe asset ratio has an average
median value of 0.13 across currencies when requiring strict maturity matching, and the value
23

Recall, the tenors in the confidential supervisory data are daily increments up to 60 days, weekly
increments from 61 days to 90, monthly increments to 180 days, 6-month increments to 1-year, and yearly
increments beyond that. The benchmark tenors for which we estimate CIP violations are 1w (7d), 2w (14d),
3w (21d), 1m (28d), 2m (61d), 3m (91d), 4m (121d), 5m (151d), 6m (181d), 1y (366d), 2y (731d), 3y (1096d),
and 4y (1461d).

24

jumps to 0.48 with maturity rounding. Still, even with these more liberal definitions of safe
asset matching, intermediaries hold only 48 cents of foreign safe assets per dollar lent. Whatdo banks do with the remaining unaccounted for 52 cents?
We turn to broad asset ratios, which include riskier foreign assets in addition to safe assets
in the numerator of the ratio calculation. The bottom panel of Figure 4 plots the same values
as the top panel, except for broad asset ratios. Focusing on unencumbered assets, the average
median broad asset ratio requiring exact maturity matching for broad assets and net lending is0.11, indicating that intermediaries hold only maturity-matched assets against approximately
11 cents per dollar of net lending they perform. Using the rounded tenor approach, the broad
asset ratio jumps to 0.59, indicating that we can account for 59 cents per dollar of net lendingby relaxing the perfect maturity matching requirement. Finally, the broad asset ratios jump
to over 1 after we further add encumbered assets. Although intermediaries hedge foreign
currency exposures, a large part of their hedging relies on maturity-mismatched assets thatare either encumbered or risky.
The analysis of safe and broad asset ratios indicates that intermediaries’ currency swap
transactions entail significant risk. Intermediaries, unable to, or choosing not to, take positions
in foreign safe assets that textbook CIP arbitrage requires, end up taking positions in risky
foreign assets. Interpreted through the lens of our model, imperfect hedging shows why
Net kt,t+n (intermediaries’ net dollar lending) helps explain cross-sectional variation in bases.
Figure 4 plots safe and broad asset ratio medians across markets. There is substantial
variation in safe asset ratios across currencies and maturities, as we present in more detail in
the appendix. Heterogeneity is relevant for understanding cross-sectional variation in bases
as well. Corollary 1 shows that differences in safe asset ratios—stemming from differences
in intermediaries’ ability to source safe bonds in foreign currency—amplifies cross-sectionalvariation in bases, even after we control for differences in net dollar lending across currencies.
This is because differences in safe asset ratios across currencies indicate different riskinessassociated with CIP trades. All else equal, lower safe asset ratios imply riskier CIP trades.
Appendix Table A3 reports details on safe and broad asset ratios across currencies andshows the median safe asset ratio across currencies ranges from $0.00 (CHF) to $0.16 (EUR).
For our broadest measures—broad assets including both unencumbered and encumberedassets—the ratios range between 0.01 (CHF) and 0.80 (EUR). The bottom of the table also
shows the corresponding measures for instances in which the banks are net borrowing dollars,
25which implied demand to hold USD-denominated safe assets. This number is consistently
much higher than 1, ranging from 2 to 29, consistent with our prediction that U.S.-based
G-SIBs hold substantially more dollar-denominated safe assets. Figure A8 breaks out themedian ratio by currency and tenor for unencumbered safe and broad assets, and Figure A9plots the median ratio when using the rounded tenor method. Across the maturity dimension,
the takeaway is that safe asset ratios are slightly higher at longer maturities than shorter
maturities, partially because the width of the maturity buckets is larger for the longer
maturities.
For robustness, Appendix Table A4 calculates the ratios using two different methods.
First, we include forwards in the estimate of FX net lending, since a spot transaction pairedwith a forward transaction can be economically similar to an FX swap. The data doesn’tallow us to pair these two separate transactions together, so including forwards will likely
overestimate the amount of net lending. Second, we calculate asset ratios net of the banks’short positions in those securities.
Our analysis of safe asset ratios provides an upper bound for the safe assets that intermediaries hold corresponding with their swap exposure, because intermediaries may holdsafe assets for reasons unrelated to meeting customer demand and hedging CIP trades. We
explore this dimension in a regression framework presented in Appendix Table A5, which
estimates the extent to which intermediaries may hold foreign currency assets even if their
net lending exposure is zero. For example, Column 1 of the table shows that banks hold an
average of $1.8 billion of unencumbered EUR-denominated safe assets across the tenors, after
controlling for Net kt,t+n .24
More closely analyzing intermediaries’ motivations for substituting away from safe assets,
Corollary 1 indicates that we might expect intermediaries to substitute away from safe
assets and into risky assets particularly when safe asset yields are low. We provide evidence
consistent with this dynamic by regressing the safe asset ratio on convenience yields. We
find that banks hold fewer safe assets and more risky assets when the convenience yield ishigh. We test this dynamic by using convenience yields estimated from derivative prices
from Diamond and Van Tassel (2021). The convenience yields are monthly and span the24Moreover, the currency fixed effects in the regression are lower bounds since we merge safe asset holdings
to the net lending data based on the net borrowing currency. Observations where the bank holds safe assets
in a currency but is not net borrowing that currency are not included in the panel.

26

six currencies in our sample. Each currency has a 1-year convenience yield estimate, except
JPY, which also has a 3m convenience yield. We merge the monthly convenience yields withmonthly asset ratios. The merged sample runs from 2016 to 2020.
We run the regression,
Safe Asset Ratiokt,t+n = α + β1 CYkt,t+n + β2 Risky Asset Ratiokt,t+n + εkt,t+n(7)where risky asset ratio is defined as the difference between the broad asset ratio and safe asset
ratio (since the broad asset ratio includes both safe and risky assets). We include the risky
asset ratio variable for banks’ propensities to maturity-match over time or across currencies.
Both safe asset and risky asset ratios are maturity-matched, so they are not required to sum
to 1—this would be the case only if the bank invests all its foreign currency proceeds from net
lending in maturity-matched assets, risky or safe. Table 6 shows that banks hold fewer safe
assets and more risky assets when convenience yields are higher. The positive and significant
coefficients on the risky asset ratio in the first three columns, and the safe asset ratio in thelast three, indicate that banks tend to increase both ratios simultaneously. Hence, when safe
asset ratios fall, it is not because banks are moving to maturity-matched risky assets, butinstead to unmatched assets. 25
4.4.2

Supply Segmentation

Prediction 5 states that variation in intermediaries’ expertise in substituting away from safe
assets in a market leads to market segmentation. The basis for a given currency is moreinelastic when intermediary supply is more segmented since the risk is more concentrated, an
25

Our discussion of foreign safe asset scarcity primarily focuses on cross-sectional variation. However, we
also note that foreign safe asset scarcity may be important for understanding the dramatic post-2008 increasein the size of bases as well. There has been a steep drop in the foreign safe assets available to intermediaries
because of decreases in the supply of safe assets (e.g., see Caballero et al. (2017)) and decreases in their re-use
as collateral in transactions. Systematic data on collateral re-use are not readily available before the 2008
financial crisis, but to the extent they are available, the evidence indicates a reduction. Gorton et al. (2020)
use data from the 10Qs of six broker-dealers and banks to show that collateral pledged was halved between
2007 and 2009, amounting to a decline of more than $2.5 trillion. Along these lines, the existing evidence
indicates there has been a large reduction in collateral velocity—the amount of re-use of the same safe asset
in multiple transactions. For example, per the numbers reported in Jank et al. (2022), there has been an
approximate 30 percent reduction in collateral re-use of European sovereign bonds from 2008 to 2017. As
Inhoffen and van Lelyveld (2023) note, the decrease in collateral velocity connects to higher balance sheet
costs after the crisis, attributed to the balance sheet-intensive nature of repos.

27

effect that is potentially amplified by intermediary market power.
We show that banks specialize in markets and provide some evidence consistent withheterogeneous bank expertise across markets. Without frictions or market segmentation,banks’ net exposures across tenors and currencies should be in equal proportion to the sizeof their FX books. In this case, banks face the same marginal search costs and risks acrossmarkets when lending dollars, with their net lending being highly correlated. But, if markets
are segmented, a bank lending more in its own market faces increasing marginal costs andrisk, pushing the basis increasingly negative—consistent with Prediction 5.
We empirically estimate dollar supply concentration by calculating a Hirschman-Herfindahl
index (HHI) of an individual bank’s notional exposure in each market using
Supply HHIkt,t+n =X

2(Market Sharek,it,t+n ) ,i∈bank,Market Sharek,it,t+n ≡

Bank i’s USD In + Bank i’s USD Out.
Industry USD In + Industry USD Out

Our HHI measure is not directly comparable to HHI measures in other settings because oursample is limited to nine banks, and therefore, the lowest possible value is 9 × [(1/9) × 100]2 ≈1, 111.
Figure 5 illustrates the cross-sectional variation in HHI, plotting the average measure bymarket against that market’s size. Higher HHI indicates more concentration and segmentation.
Larger markets are clearly less segmented. Short-term contracts for CAD and AUD are moresegmented, and CHF is the most segmented. The least segmented markets are JPY and EUR
at longer tenors, especially at 1-year.26
Why FX markets are segmented is an important question. We provide support forheterogeneous expertise as a possible mechanism using cross-sectional variation in HHI in
Section 5, where we also discuss another implication of segmentation—that shocks to thespecialists in a market should particularly impact that market.
26

We estimate similar supply HHI’s looking just across tenors and currencies in the online appendix, see
Figures A11 and A12.

28

4.4.3

Demand Concentration

Demand concentration also affects the basis. Prediction 4 claims that the basis is increasing
in demand concentration, which maps to high counterparty risk. A bank can manage its
counterparty risk by lending to a wide set of counterparties in each market. Lending becomesriskier when a bank deals with fewer counterparties, increasing its exposure to idiosyncratic
counterparty risk. Intermediaries may require compensation for this risk.
We proxy for demand concentration with a separate HHI measuring counterparty concentration. The data does not provide firm-specific counterparty names but instead provides
two dozen counterparty types—e.g., broker-dealer, non-financial corporate, non-regulated
fund.27 Since we do not view individual counterparties, we calculate demand concentration
across counterparty types, assuming that firms within a category have highly correlated risks.
Such an assumption is plausible: lending exclusively to levered funds is riskier than lending
to a mix of levered funds and non-financial companies, given the two lines of business arelikely exposed to different risks.
We calculate the demand HHI similarly to the supply HHI:
Demand HHIkt,t+n =2(Counterparty Market Sharek,jt,t+n )

X
j∈(c.p. type),Counterparty Market Sharek,jt,t+n ≡Counterparty j’s USD In + Counterparty j’s USD Out.
Industry USD In + Industry USD Out

Figure 6 plots demand concentration by market. There is no clear pattern between themarket size and its demand concentration, although currencies tend to bunch together—longer
maturity CAD is among the most demand-concentrated. The range of demand HHIs is muchhigher, partly reflecting banks’ large share as counterparties to one another.
Counterparty data are available only beginning in May 2022. We estimate the fullsample demand concentration using two different items available for the full sample: loancounterparties and settlement types. The data for unsecured and secured loans includes
27

The full list of FX counterparties: Bank, Broker Dealer, Central Bank, Debt Issuing Special Purpose
Entity, Financial Market Utility, Government Sponsored Entity, Investment Company or Advisor, MultilateralDevelopment Bank, Non-Financial Corporate, Non-regulated Fund, Other, Other Supervised Non-Bank
Financial Entity, Other Supranational, Pension Fund, Public Sector Entity, Retail, Small Business, Sovereign.

29

counterparty types, and we calculate loan demand concentration HHI analogously. Banks
also report FX settlement types: bilateral, continuous linked settlement (CLS), or other. CLStransactions are settled payment-versus-payment to reduce Herstatt risk and cover 30 percent
of all FX transactions.28 Bilateral trades are often, but not exclusively, transactions thatbanks do with their clients, like hedge funds. Hedge funds trade through their prime brokers
using give up trades, which are typically bilateral. The bilateral share is a rough measure of therelative importance of investors like hedge funds in each market. For each market, we calculatethat day’s share of bilateral transactions compared to the total transactions in that market.
We estimate Demand HHIkt,t+n for the full sample by running a regression of Demand HHIkt,t+n
on loan and bilateral demand in the short sample for which we have counterparty market
share data, and then use the estimated coefficients to project Demand HHIkt,t+n over the fullsample period where we do not have counterparty data.
Demand HHIkt,t+n = α + β1 Loan Demand HHIkt,t+n + β2 Bilateral Sharekt,t+n + εkt,t+n .
We show the estimated coefficients in the online appendix, Table A6. Both loan and bilateralconcentration of demand are strongly positively related to counterparty concentration. We usek\HHIt,t+n for the full sample. Both measures
the regression coefficients to estimate Demandof demand concentration—directly measured using the shorter sample with counterparty
information, or the longer sample that uses settlement type and loan counterparties to project
counterparty demand concentration—are less precise than the supply concentration measure.
28

See
https://www.newyorkfed.org/medialibrary/microsites/fxc/files/2020/FX_settlement_
risk_CLS.pdf.

30

4.5

Decomposing Deviations from CIP

We compare how each of the frictions measured above contributes to cross-sectional variation
in bases. We run the following regression:








|Basiskt,t+n | = α + β1 Demand HHIkt,t+n × I(Net kt,t+n ≥ 0)
+ β2 Demand HHIkt,t+n × I(Net kt,t+n < 0)








+ β3 Supply HHIkt,t+n × I(Net kt,t+n ≥ 0)
+ β4 Supply HHIkt,t+n × I(Net kt,t+n < 0)








+ β5 Safe Asset Ratiokt,t+n × I(Net kt,t+n ≥ 0)

+ β6 Safe Asset Ratiokt,t+n × I(Net kt,t+n < 0) .
The dependent variable is the absolute value of the basis for a given market on a given day.
A larger basis dislocation—either expensive or cheap dollar funding compared to benchmark
rates—increases the absolute value of the basis. We use the absolute value of the basis
because we expect increases in the magnitude of any of the frictions to push the basis awayfrom zero. Since we expect potential differences in the frictions when the bank is net lending
dollars or net borrowing dollars, we include dummies to capture this asymmetry. We expect
foreign safe asset scarcity to behave differently than USD safe asset scarcity since the banks
we study are based in the U.S.
We include controls for the risk of a safe asset sovereign issuer, which we proxy forusing the country’s CDS spread, and the value-weighted average CDS spread for the banks
lending in that market. We merge government CDS spreads based on which safe asset anarbitrageur would hold, the foreign government CDS when Net kt,t+n ≥ 0, and the U.S spreadwhen Net kt,t+n < 0.29 We include tenor and date fixed effects and weigh the regression by the29

CDS spreads are from Markit. For both banks and sovereigns, we use CDS spreads for 5y tenor of seniorunsecured tier for the primary curves and coupons, as identified by Markit. The euro CDS spread is a simple
average of Italian and German CDS spreads (both quoted in USD). We use the MM14 contract for DB since
Markit denotes both MM and MM14 as primary curves. We create a market-specific bank CDS spread byvalue-weighting the individual banks’ CDS spreads based on their gross position in the market as a share of
the total gross positions across all banks with CDS quotes from Markit in that market on that day. CDS
quotes for two banks in our sample are comparatively sparse. In the regression results, coefficients on thebank CDS controls carry large and negative values, due to the fact that the banks with smaller CDS spreads
do more net dollar lending in markets with the largest basis dislocations.

31

square root of the market’s share of the total daily gross notional. To make the coefficients
directly comparable, we transform the independent variables to modified z-scores using eachvariable’s full sample median and standard deviation. We use the sample median rather thanthe mean to mitigate the influence of the high skewness of the data.
Table 7 reports our main regression results using the sample from January 2016 to March
2023. Due to data availability, the regressions use estimated demand concentration rather
than the directly measured demand concentration HHI, and we compute safe asset ratios,
including unencumbered assets with matched maturities. The first two columns in the table
exclude estimated demand concentration, while the last two columns include it.
The first two rows show that supply concentration is an important friction and is symmetricacross markets with long or short dollar demand. A one standard deviation increase in the
supply HHI increases the absolute value of the basis by 10 to 14 bps. This result is consistent
with our expectation that markets relying on a more concentrated set of banks will havelarger dislocations. The elasticity of bases is affected by the concentration of intermediaries
that meet dollar funding demand in each market.
The next set of rows illustrates the importance of foreign safe asset scarcity and banks’
search costs for foreign safe bonds. The coefficients on Net kt,t+n are positive and significant,with values ranging from around 4 to 7.5 bps, indicating that a one standard deviation
increase in Net kt,t+n corresponds with a 4 to 7.5 bps higher basis, consistent with the resultspresented in Table 5. Interpreted through the lens of our model, these coefficients capture
the effect of foreign safe asset scarcity if safe asset ratios are the same across markets and if
differences across markets stem from the total amount of dollar demand.
However, there are also meaningful differences in safe asset ratios across markets. The
next four rows account for the effect of variation in foreign safe asset scarcity across different
markets, stemming, for example, from differences in search costs for foreign safe assets acrossmarkets. Focusing on markets where there is net dollar demand, we observe significant
negative coefficients of 8 to 9 bps in columns (1) and (3) on the safe asset ratio, indicatingthat a one standard deviation decrease in the safe asset ratio moves the basis by an additional
8 to 9 bps. When using the broad asset ratios in the regressions rather than safe asset ratios(in columns (2) and (4)), these coefficients increase in magnitude to 11 and 14.
The next two rows examine the effect of demand concentration on the basis. We observethat in markets where banks are net lending dollars, a one standard deviation increase in32

demand concentration coincides with an approximately 7 bps higher basis. This effect islimited to dollar net lending, and the coefficients are insignificant when looking at markets
where banks are borrowing in dollars.
The regression evidence indicates the importance of safe asset scarcity and supply concentration in explaining cross-sectional variation in CIP deviations. In addition, and despite
demand concentration being measured with more error, the regression suggests that demand
concentration may play a role in amplifying basis dislocations.
We also present additional variations of our regressions in Appendix Table A7, where we
present analysis including encumbered assets and using directly measured demand concentration from the shorter sample. In Appendix Table A8, we also present regression results
separately considering each independent variable. The results are similar to those reported
in the main regression, suggesting an even stronger relationship between the independent
variables and bases.
The results show that both foreign safe asset scarcity and intermediary segmentation areimportant frictions that help describe the cross-section of bases. We interpret these frictions
as arising from heterogeneous expertise across intermediaries and explore that theme further
in the next section.

5

Intermediary Segmentation

The data supports the idea that intermediary segmentation is a key driver of bases, making
them more inelastic to demand.
In this section, we take a deeper dive to try to understand the drivers of this segmentation.
First, we provide evidence that segmentation arises from heterogeneous expertise acrossintermediaries in substituting away from safe assets. Second, we use an event study analysis
around the Silicon Valley bank run to better identify the impact of financial constraints
and test an additional implication arising from segmentation (Prediction 6): specializing
intermediaries’ constraints should help explain cross-sectional variation in bases.

33

5.1

Sources of Intermediary Segmentation

We present several facts consistent with intermediary segmentation being driven by heterogeneity in banks’ expertise in different markets. In particular, we argue that banks gain
market-specific expertise from other business areas, providing better access to counterparties
in those markets.
We show that markets with higher supply segmentation rely on banks with larger FX
books. Column 1 of Table 8 reports results from a regression of a market’s supply HHI on(log of 1 plus) the FX swap notional of the banks active in that market, weighted by theirmarket share. The independent variable in the regression estimates the FX book size of theaverage bank in that market. There is a strong relationship between more segmented markets
and larger intermediaries.30
Segmentation is persistent. The first four columns of Table 8 report regression resultsfrom a market’s supply HHI on its 1-month, 1-year, and 5-year lags. A 1-point increase in
the supply HHI is associated with a 0.51-point increase in the supply HHI one year later.
The relationship is stronger at shorter lags, but the coefficient is still large and significantly
different from zero even with a 5-year lag.
Individual banks’ market shares are also persistent. We regress bank i’s market shareon lags of its market share. A bank’s market share is calculated as its share of the total
gross notional of swaps in that market. The last four columns of Table 8 show the regression
results using a panel at the bank-tenor-currency-date level. These persistence coefficients are
similarly large and reliably different from zero, ranging from 0.87 at a 1-month lag to 0.74 at
a 5-year lag. This evidence suggests that segmentation is present, with banks specializing inthe same markets over time, consistent with having and developing market-specific expertise.
We present further evidence supporting this story by showing that banks specialize notjust in specific markets—currency and tenor—but also in counterparty segments. One bank,for example, may have a large rolodex of Canadian insurance counterparties, while anothermay cater more to Asian sovereign funds. We calculate a bank’s market share of a given
counterparty and currency, collapsing across all maturities. We calculate the market shareas the notional FX swap exposures with that counterparty-currency pair as a share of thebank’s total notional FX swaps on that day. We denote this measure Bank FX Sharei,kt,ctpty ,30

Figure A13 in the online appendix provides a scatterplot of these two variables.

34

where i denotes the bank and ctpty denotes the counterparty.31
We calculate two related measures: first, we calculate the average market share of allbanks except bank i, which we call Other Bank FX Sharei,kt,ctpty . Second, we estimate abank’s Bank Loan Sharei,kt,ctpty to study how segmentation works across a bank’s lines ofbusiness. We define bank loans as the items reported by the bank in their inflows-secured
and inflows-unsecured tables in the FR2052a data.32
Table 9 shows how banks specialize in specific counterparty-by-currency markets. The firstcolumn shows that the bank’s specialization in counterparty-currency markets is persistentover time as the lag of Bank FX Sharei,kt,ctpty strongly predicts its current values. However,
one concern is that banks all uniformly service the same counterparty-currency markets.
Column two adds a variable, Other Bank FX Sharei,kt,ctpty , and rejects such a possibility since
the coefficient on the other bank variable is not different from 0. Column three shows thatbanks specialize in currencies and counterparties across their lines of business. We regress
the bank FX share on Bank Loan Sharei,kt,ctpty and find a strong positive relationship. Adding
controls for other banks’ loan shares (column 4) does not change the result. Column 5 adds
Other Bank FX Sharei,kt,ctpty along with the loan share variables, showing that the bank’s loan
book is still informative above and beyond what other banks’ FX activities are in predicting
a bank’s FX activities. The last column (column 6) includes the bank’s lagged FX share,
which dominates the results. Note, however, that the bank’s loan share variable is the only
other variable with a positive coefficient, even if not statistically significant.
Providing additional support for the idea that intermediary specialization may be driven
by expertise in reducing risk, Corollary 2 indicates that banks with larger market shares holdfewer foreign safe bonds per dollar of net lending. This is because such banks are better able
to manage the risks associated with substituting away from safe assets. We test this idea by
calculating bank-specific safe-asset ratios for each market.
31

For example, if bank z had $100 of gross notional swaps outstanding and had $10 of gross notional withD
insurance companies denominated in AUD, we would set Bank FX Sharez,AUt,insurance = 10%. We exclude bankcounterparties since they are the largest counterparty by an order of magnitude in most markets, and we
exclude two counterparty types that were removed in 2022. Recall that FX counterparty data is available
beginning in May 2022.
32
Values are reported on a gross basis and not netted. We exclude collateral swaps. These tables include
offshore and onshore placements, operational balances, outstanding draws on unsecured or secured revolving
facilities, other unsecured loans, short-term investments, reverse repos, securities borrowing, dollar rolls,
margin loans, other secured loans, synthetic customer longs, and synthetic firm sourcing.

35

Table 10 shows the regression results of bank-specific safe-asset ratios on the same bank’s
market share when banks are net borrowing foreign currency (Net kt,t+n > 0). The lattercondition matters because it indicates times when banks receive foreign currency and havedemand for foreign safe assets. The market share is defined as the bank’s notional FX swap
exposure in that market as a share of all banks’ total notional FX swaps in the market on thatday. The results show that there is a strong negative relationship between the two variables
across several specifications. Using the third column, which controls for date and bank fixed
effects, a one-standard-deviation increase in Bank FX Sharei,kt,t+n (16pp) corresponds to a
decline in that bank’s safe asset ratio of 0.15, an economically large relationship given the
average bank-specific safe asset ratio is 0.42.
The evidence supports the notion that banks specialize in particular markets because they
have expertise and easier access to counterparties in those markets, stemming, for example,
from other parts of their business. This expertise and access likely make it easier to manage
risky assets and lower operating costs, and are distinct other potential forms of expertise, forexample lower search costs for foreign safe assets.

5.2

Event Study: Silicon Valley Bank Run

In March 2023, Silicon Valley Bank suffered a bank run following disclosures about losses
on its hold-to-maturity portfolio. Data from the publicly available H.8 shows that some
depositors moved from smaller to larger banks, including the G-SIBs in our sample. Between
March 8 and March 15, the H.8 data shows that large banks gained $120bn of deposits whilesmall banks lost $108bn.33 The average large bank had deposit inflows of $120/25 =$4.8
billion since H.8 data spans the largest 25 domestically charted commercial banks. Publicly
available call report data shows that the average bank in our sample lost $11 billion in
deposits between 2022 Q4 and 2023 Q1, ranging from −$44 billion to $32 billion, with astandard deviation of $21 billion.34
The unexpected flow of deposits to large banks is an exogenous increase in those banks’33

See https://www.federalreserve.gov/releases/h8/20230324/.
We match the banks in our FX data with their main affiliated banks using the following RSSDs: JPM(852218), BAC (480228), WFC (451965), C (476810), GS (2182786), BK (541101), MS (1456501 and 2489805),
SST (35301), and DB (214807). We calculate the change in deposits using RCON2200 (domestic deposits)
and RCFN2200 (foreign deposits).
34

36

risk-bearing capacity, at least in the short term. We use this plausibly exogenous variation
in banks’ abilities to intermediate FX markets to test Prediction 6 of our model: thatmarkets should reflect the risk-bearing capacity of their specializing banks. The particularsource of variation exploited by the shock is the cross-sectional variation in deposit inflows
corresponding with the shock.
We calculate the change in the dollar-denominated deposits between March 9, one day
before the event, and two weeks after the event for each bank. We calculate the value-weighted
change in deposits for each market (tenor and currency pairs) in our sample, which we define
as ∆Deposits kt+n , where value weights are a bank’s gross notional in a given market relative
to the total gross notional for that market. We fix value weights on March 9, 2023 to exclude
the effects of banks rebalancing after the SVB shock. Hence, the variable has no t subscript:
it is fixed through the sample and varies only over tenor and currency. Our measure provides
a market-specific deposit inflow. For example, if two banks each had half of the 1-year JPY
gross notional on March 9, and bank 1 had deposit flows of x and bank 2 had deposit flowsof y, then the value-weighted deposit flow we assign to 1-year JPY would be (x + y)/2. We
transform ∆Deposits kt+n into a modified z-score using the median rather than the mean. To
give a sense of magnitudes, the average deposit inflow to a given market was $20.3 billion,
with a standard deviation of $6.5 billion.
The shock is an inflow of dollars, so we expect markets where banks lend dollars will
have an effect. Therefore, we limit the sample to the markets where Net kt,t+n was positive on
average over the two weeks leading into the event. In Table 11, we run a regression:|Basiskt,t+n | = α + γ1 I(Post) + γ2 ∆Deposits kt+n + γ3 I(Post) × ∆Deposits kt+n + εkt,n .

(8)I(Post) is equal to 1 for days after March 9 and 0 otherwise. Our window is the two weeks
before and after the event, providing the longest time frame before the last week of March,when quarter-end window-dressing could confound the estimates.
The first three columns of Panel A show the main results, with specifications that vary
fixed effects and whether the regression is weighted by market size. The coefficient on the post
dummy is positive and significant, indicating that basis dislocations worsened following the
SVB event, consistent with a risk-off sentiment. The bases are larger on average in markets
with larger deposits. This finding is likely because deposit flows go to banks perceived as the37safest, who lend dollars most in markets with larger CIP dislocations.
The key result is the interaction term between the post dummy and the change in deposits.
After the event, markets that relied more on banks with the largest inflows had smaller
dislocations, as indicated by the significant and negative coefficient. The result is robust
across specifications. The specification with all of the controls (column 3) finds that a one
standard deviation increase in deposit inflows decreases the absolute value of the basis by 2
bps after the event. The effect is economically large: the average basis absolute value is 30
bps, so the coefficients imply a 7 percent decline.
Why would the basis relatively improve in the treated markets? The last three columnsof Panel A show that treated markets experienced increased net dollar lending. The deposit
inflows likely allowed banks to lend some marginal dollars. The panel runs the same event
study regression except changes the dependent variable to Net kt,t+n . The main result is thethird row: markets with larger deposit inflows were the same in which banks increased
Net kt,t+n . The result holds when markets are weighted by their size, so small markets behave
differently. Using column 6, a one standard deviation increase in deposit flows after the event
increased Net kt,t+n by 0.6 percentage points.
Panel B runs a placebo event study using data from one month before the actual SVB
event. The regression is the same except the post dummy equals 1 for days after February 9.
With no salient market volatility in mid-February 2023 or abnormal deposit flows, we do not
expect the interaction term to have a significant effect. The third row of Panel B shows thatthe placebo event had no effect on the basis, and if anything, the basis increased during theperiod. The last three columns of the table also show no obvious pattern in the interaction
term’s effect on net lending.
The evidence from the SVB bank run supports the notion that segmented markets
reflect their specializing banks’ risk-bearing capacity, which appears to be important forintermediaries’ impact on asset prices.

6

Conclusion

In this paper, we shed new light on the role of intermediaries in covered interest parity
arbitrage and the global provision of dollar funding using granular confidential supervisorydata. Exploiting cross-sectional variation in CIP bases that is puzzling to prevailing theories,38

we uncover three key forces that drive these bases: imperfect execution of CIP arbitrage due toforeign safe asset scarcity, market segmentation resulting from intermediaries’ specialization indifferent currencies and tenors, and concentrated demand from certain types of counterparties
in some markets. More closely studying intermediary segmentation, the March 2023 banking
turmoil following the Silicon Valley Bank run provides a natural experiment, demonstrating
how bank-specific shocks transmit to prices in the markets where those banks specialize.
Intermediary segmentation persists over time and is linked to banks’ heterogeneous expertiseand specialization in different counterparty segments. Our findings highlight the importanceof specialized supply and demand forces in banks’ provision of global dollar funding.

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41

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43

7

Figures$ Billion
10,000Swaps
Forwards/Futures

8,000

6,000

44

4,000

2,000

0

EUR

JPY

GBP

AUD

CAD

CHF

Figure 1: Foreign Exchange Notional Exposure. Figure provides the average notional FX exposures in our sample bycurrency across swaps and forwards/futures before limiting to the tenors with OIS rates. Sample includes only transactions inthe six currencies versus the dollar.

Basis Points

1-Week Bases

Basis Points

500

1-Year Bases

100

0

0

45
JPY
EUR
CHF
GBP
AUD
CAD

-500

-1,000
2005

2010

2015

2020

2025

-100

-200
2005

2010

2015

2020

2025

Figure 2: Covered-Interest Parity Violations. Figure plots the CIP bases across two tenors—1-week and 1-year—acrossseveral currencies.

Basis Points
3m

6m

1y

4y

80
60

46

40
20
0
2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

Figure 3: Cross-Sectional Standard Deviation of Covered-Interest Parity Violations. Figure plots the cross-sectional
standard deviation of CIP bases for the given tenor. Sample includes only periods when we have observations for all of the six
currencies against the dollar.

Safe Asset Ratio
Unencumbered

Encumbered and Unencumbered

Matched Tenor

Main
Main incl. forwards
Main incl. shorts

0.05
0.04
0.05

Rounded Tenor

0.17
0.11
0.12

Matched Tenor

0.13
0.10
0.13

Rounded Tenor

0.25

0.48
0.35

0.00

0.50

1.00
Ratio

1.50

2.00

Broad Asset Ratio

47

Unencumbered

Encumbered and Unencumbered

Matched Tenor

0.11
0.08
0.10

Rounded Tenor
Matched Tenor

0.31
0.34
0.21

0.32
0.29

Rounded Tenor
0.00

Main
Main incl. forwards
Main incl. shorts

0.59

0.88

0.50

1.00
Ratio

1.77
1.36

1.50

2.00

Figure 4: Summary of Safe Asset Ratios Figure plots the average median safe and broad asset ratio across all currencies
when Net kt,t+n is positive excluding the USD. Matched tenor shows the asset ratio when the tenor of the underlying asset and theswap have the same maturity. Rounded tenor buckets swaps and the assets into the nearest benchmark tenor. Figure also shows
the analogous net measurement when including forwards and futures (affecting the denominator of the ratios) and when nettingout the firms’ short positions in the asset (affecting the numerator).

Supply HHI
6,000
CHF-1w
CHF-3w
CHF-1m
CHF-2w

EUR-1w
EUR-2w
EUR-3w
EUR-1m
JPY-1w
JPY-1m
JPY-2w
GBP-1w
CAD-1m
GBP-2w JPY-3w
CAD-1w
CHF-2m
GBP-3w
CAD-2w
AUD-1w GBP-1m
CAD-3w
AUD-1m
AUD-2w
AUD-3w
CAD-2m

5,000

4,000

48

CHF-3m3,000

CHF-4m
CHF-5m

2,000
22

23

EUR-2m
JPY-2mCAD-3m

EUR-3m
JPY-3m

GBP-2m
CAD-4m
AUD-2mCAD-5m

JPY-3yJPY-2yJPY-4m
JPY-4yEUR-4m
JPY-5m
JPY-1yGBP-3m
EUR-5m
JPY-6m
AUD-4yAUD-3mCAD-6m
JPY-9m
AUD-2y AUD-1y
AUD-3yEUR-6m
EUR-3yEUR-2y EUR-1y
EUR-4yAUD-5m
AUD-4m
CHF-4yCHF-2yCHF-6m
GBP-4m
CHF-3yCHF-9m
CHF-1y AUD-9m
AUD-6m
GBP-4yGBP-3yEUR-9m
GBP-5m
GBP-2y GBP-1y
CAD-4yGBP-6mCAD-3y CAD-9m
GBP-9mCAD-2yCAD-1y24ln(1+Notional)

25

26

27

Figure 5: Supply Segmentation. Figure plots the average of daily Supply HHIkt,t+n against the (log of 1 plus) the averagenotional of that market.

Demand HHI
8,000

CAD-4yCAD-3yCHF-4yCAD-2yCAD-1yCAD-6m
CAD-9mCAD-5m
CHF-1yCHF-3yCAD-4m
CHF-6m
EUR-4yCHF-1w
EUR-3yCHF-5m
EUR-1yCHF-2w
CHF-9m
JPY-9m
GBP-5m
CHF-3w
CHF-1m
GBP-6m
GBP-9m
CHF-4m
JPY-6m
EUR-9m
JPY-5m
CHF-3m
GBP-4yJPY-4yGBP-4m
JPY-4m
GBP-1yCAD-3m
6,000
GBP-2yJPY-3yGBP-3yJPY-1yEUR-6m EUR-2y
JPY-3m
GBP-3m
CHF-2m
EUR-5mCAD-2m
CHF-2yAUD-5m
EUR-4m
AUD-4m
AUD-3m JPY-2mAUD-6m
JPY-2yAUD-2m
AUD-9m
EUR-3m
GBP-1w
GBP-3w
GBP-2m
GBP-2w
JPY-3w
JPY-1m
GBP-1m
CAD-1m
JPY-2w
EUR-2m AUD-2y AUD-1y
JPY-1w
CAD-3w
CAD-1w
CAD-2w
EUR-1w
5,000
EUR-2w
EUR-3w
AUD-1w
AUD-1m
AUD-3w
EUR-1m
AUD-4yAUD-2w
AUD-3y

7,000

49

4,000
22

23

24
ln(1+Notional)

25

26

27

Figure 6: Demand Segmentation. Figure plots the average of daily Demand HHIkt,t+n against the (log of 1 plus) the averagenotional of that market.

8

Tables

All

AUD

CAD

CHF

EUR

GBP

JPY

2008–2023
Mean
Std. Dev.

−24.1
28.4

6.2
21.9

−12.8
16.9

−46.6
24.3

−26.8
20.3

−14.7
14.8

−49.9
23.3

2016–2023
Mean
Std. Dev.

−26.3
27.0

9.6
14.8

−14.3
6.6

−45.6
22.9

−30.9
15.1

−19.4
9.9

−57.5
20.5

Summary Statistics, bps

50

Table 1: 1-Year Covered-Interest Parity Violations. Table shows mean and standard deviation of the CIP deviations at
a one-year tenor. See section 3.2 for the calculation details.

Swapsby currency

Forwards & Futures$ billions

Mean

Std. Dev.

Mean

Std. Dev.

AUD
CAD
CHF
EUR
GBP
JPY

689
411
215
2,344
914
1,827

92
92
28
345
172
180

331
290
207
1,610
729
1,146

137
99
55
270
166
181

Swaps

by tenor

Forwards & Futures$ billions

Mean

Std. Dev.

Mean

Std. Dev.

1w
2w
3w
1m
2m
3m
4m
5m
6m
9m
1y
2y
3y
4y

91
78
71
69
188
384
324
302
730
679
1,396
868
632
589

78
72
67
68
120
147
78
67
104
101
201
133
101
92

308
251
222
208
456
721
420
338
564
352
277
109
53
34

295
260
240
226
334
338
122
94
127
71
43
9
6
7

Table 2: Average daily gross FX notional across all banks. Table shows average
daily notional across all banks and tenors for the matched covered-interest parity tenors bycurrency.

51

Bank’s Cash Flowst

Swap #1: lend USD vs. JPY
USD pay
JPY receive−$100.00
U11,500

USD receive
JPY pay
Swap #2: lend JPY vs. USD
USD receive
JPY pay$104.55−U11,500$95.00−U10,925

USD pay
JPY receive
Total
(a)
(b)
a/b

Net Dollars Lent
Notional Dollars
Net JPYt,t+7

t+7−$99.32
U10,925$5.00$195.00
2.6%$5.23$203.87
2.6%

Table 3: Net Calculation Example. Table shows the bank’s cash flows across two swaps,
one receiving dollars and the other paying dollars with spot exchange rate St = 115 and theforward exchange rate is Ft,t+7 = 110.

52

Mean (Percent)
AUD

CAD

CHF

EUR

GBP

JPY

Mean

−5.2
−5.5
−6.2
−5.8
−5.6
−3.7
−3.5
−3.6
−3.9
−2.8
−1.9
0.3
−1.9
−1.2

−4.3
−4.3
−3.3
−3.9
−0.2
3.0
1.8
0.9
−0.6
−5.0
−3.1
−3.4
−1.4
−0.3

−4.2
−0.5
−1.0
−2.0
−7.0
−1.6
−0.1
0.0
−1.5
−2.4
−3.7
−6.1
−7.3
−3.1

−3.0
−1.6
−1.6
−1.6
2.4
2.0
2.5
3.2
4.3
4.8
2.3
1.4
1.2
−1.2

−6.6
−7.3
−8.1
−8.2
−2.1
−0.2
2.5
2.4
2.3
3.5
3.4
1.1
0.3
1.2

−6.6
−5.1
−4.1
−3.2
−3.8
0.8
2.5
2.1
4.5
3.5
5.7
5.4
5.0
7.9

−5.0
−4.0
−4.0
−4.1
−2.7
0.1
0.9
0.8
0.8
0.3
0.4
−0.2
−0.7
0.5

Mean −3.6

−1.7

−2.9

1.1

−1.1

1.1

1w
2w
3w
1m
2m
3m
4m
5m
6m
9m
1y
2y
3y
4y

Standard Deviation (Percent)
AUD

CAD

CHF

EUR

GBP

JPY

Mean

1w
2w
3w
1m
2m
3m
4m
5m
6m
9m
1y
2y
3y
4y

30.2
32.3
34.1
34.2
18.8
11.2
10.6
11.2
7.4
9.0
5.6
6.9
4.6
4.6

30.2
33.1
34.6
35.7
20.4
13.7
14.1
15.2
9.8
10.5
7.2
7.5
8.6
8.2

41.4
44.3
45.8
45.9
27.6
20.1
19.8
19.6
12.5
12.7
6.1
5.5
3.3
4.4

21.3
22.9
24.0
23.6
12.9
8.5
8.2
8.8
6.1
6.3
3.4
3.2
3.2
3.3

28.9
31.0
31.6
31.9
18.3
12.8
14.3
14.7
8.0
8.4
5.1
5.8
4.1
4.9

23.5
24.7
25.5
26.0
12.8
9.7
9.5
9.9
9.0
8.5
5.8
7.2
6.0
6.7

29.3
31.4
32.6
32.9
18.5
12.7
12.8
13.2
8.8
9.2
5.5
6.0
4.9
5.4

Mean

15.8

17.8

22.1

11.1

15.7

13.2

Table 4: Net Summary Statistics. Top panel plots the average daily Net kt,t+n for a given
currency k and maturity t + n. Bottom panel plots the time-series standard deviation of
Net kt,t+n .

53

All Tenors

Short-Term

Long-Term

54

(1)

(2)

(3)

(4)

(5)

(6)

Net kt,t+n−0.0544∗(−1.82)−0.601∗∗∗(−3.97)−0.432∗∗∗(−3.36)−0.431∗∗∗(−3.40)−0.171∗∗(−2.55)−1.548∗∗∗(−3.67)NR2
Tenor FETime FE
Weighted

151,655
0.00
No
No
No

149,337
0.04
No
No
Yes

149,337
0.03
No
Yes
Yes

149,337
0.02
Yes
Yes
Yes

106,005
0.01
Yes
Yes
Yes

43,332
0.11
Yes
Yes
Yes

Table 5: Net and Bases. Table presents the regression of the basis on Net kt,t+n : Basiskt,t+n = α + βNetkt,t+n + γ0Xt + εkt,t+n .
Currencies include AUD, CAD, CHF, EUR, GBP, and JPY and tenors include: 1w, 2w, 3w, 1m, 2m, 3m, 4m, 5m, 6m, 9m, 1y,
2y, 3y, and 4y. Constant omitted. Columns with weights are weighted by the square root of the market’s daily gross notional
share. Short-term column limits swaps to less than 1-year maturities, and long-term is greater than or equal to 1-year maturities.
Net kt,t+n is in percent and basis is in basis points. Within R2 reported. t-statistics shown using robust standard errors clusteredby market and date where ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Safe Asset Ratio(1)
CY kt,t+n
Risky Asset Ratiokt,t+n
Safe Asset Ratiokt,t+n

55

N
R2
Tenor FETime FE
Weighted(2)

Risky Asset Ratio
(3)

(4)
0.120
(3.07)−0.490(−2.64)4.072∗∗∗(4.25)−0.568(−2.63)4.067∗∗∗(4.31)−0.319(−1.50)3.047∗∗∗(2.84)

321
0.80
No
No
No

321
0.81
Yes
Yes
No

321
0.67
Yes
Yes
Yes

∗∗

∗∗

(5)

(6)
∗∗∗

0.143
(3.29)

0.134∗∗∗(3.20)

0.196∗∗∗(16.92)

0.198∗∗∗(15.69)0.219∗∗∗(6.95)

321
0.80
No
No
No

321
0.81
Yes
Yes
No

321
0.68
Yes
Yes
Yes

∗∗∗

Table 6: Safe Asset Ratios and the Convenience Yield. Table presents the regression of the asset holdings as a share of
the banking system’s total assets on that day on the convenience yield with the matching currency and tenor, both in percentage
points: Safe Asset Ratiokt,t+n = α + β1 CYkt,t+n + β2 Risky Asset Ratiokt,t+n + εkt,t+n . Asset ratios are the matched tenor version
that uses unencumbered assets. Convenience yield is from Diamond and Van Tassel (2021) and in basis points. Panel merges
monthly averages of asset ratios with the convenience yield measures, matched by currency and tenor. t-statistics shown using
robust standard errors clustered by month where ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Supply Segmentation
Supply HHIkt,t+n × I(Net kt,t+n ≥ 0)
Supply HHIkt,t+n × I(Net kt,t+n < 0)
Safe Asset Scarcity
Netkt,t+n × I(Net kt,t+n ≥ 0)
Netkt,t+n × I(Net kt,t+n < 0)
Safe Asset Ratiokt,t+n × I(Net kt,t+n ≥ 0)
Safe Asset Ratiokt,t+n × I(Net kt,t+n < 0)
Broad Asset Ratiokt,t+n × I(Net kt,t+n ≥ 0)

(1)

(2)

(3)

(4)

10.81∗∗∗(6.00)
13.75∗∗∗(6.63)

10.81∗∗∗(6.00)
13.76∗∗∗(6.63)

10.12∗∗∗(6.05)
11.65∗∗∗(6.23)

10.12∗∗∗(6.05)
11.65∗∗∗(6.23)

5.22∗∗(2.11)
7.14∗∗∗(3.13)−7.77∗(−1.94)−0.50(−1.39)5.22∗∗(2.11)
7.14∗∗∗(3.12)

4.35∗∗(2.00)
7.50∗∗∗(3.23)−9.34∗(−1.97)−0.36(−1.15)

4.34∗∗(2.00)
7.50∗∗∗(3.23)

Broad Asset Ratiokt,t+n × I(Net kt,t+n < 0)−10.82∗(−1.94)−0.25(−1.06)

Demand Concentrationk\HHIt,t+n × I(Net kt,t+n ≥ 0)
Demandk\HHIt,t+n × I(Net kt,t+n < 0)DemandControls
Bank CDSkt,t+n × I(Net kt,t+n ≥ 0)
Bank CDSkt,t+n × I(Net kt,t+n < 0)
Govt CDSkt,t+n × I(Net kt,t+n ≥ 0)
Govt CDSkt,t+n × I(Net kt,t+n < 0)N
R2
Tenor FETime FE
Weighted−13.52∗∗(−2.24)−0.17(−0.87)

7.42∗∗∗(3.37)

7.42∗∗∗(3.38)

1.27(0.82)

1.26(0.82)−97.97∗∗∗(−6.50)−99.21∗∗∗(−6.47)−2.14∗∗∗(−2.85)−2.55(−1.22)−97.97∗∗∗(−6.50)−99.22∗∗∗(−6.47)−2.14∗∗∗(−2.85)−2.53(−1.21)−97.35∗∗∗(−6.86)−98.13∗∗∗(−6.85)−1.71∗∗(−2.43)−4.32∗(−1.75)−97.34∗∗∗(−6.86)−98.13∗∗∗(−6.85)−1.71∗∗(−2.43)−4.31∗(−1.75)

149,337
0.21
Yes
Yes
Yes

149,337
0.21
Yes
Yes
Yes

149,293
0.23
Yes
Yes
Yes

149,293
0.23
Yes
Yes
Yes

Table 7: Regression of the absolute value of the basis on marginal cost measures.
Table presents the regression described in section 4.5 using demand HHI measures estimatedfrom Table A6. The dependent variable is the absolute value of the basis. To make thecoefficients directly comparable, we transform the independent variables to modified z-scores
using each variable’s full sample median and standard deviation. Regression includes tenor
and date fixed effects and we weight the regression by the square root of the market’s share of
the total daily gross notional. Within R2 reported. t-statistics shown using robust standard
errors clustered by market and date where ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

56

HHI Supplykt,t+n(1)
ln(1 + Avg. Book Sizekt,t+n )HHI Supplykt−1m,t−1m+n122.6∗∗∗(35.63)

HHI Supplykt−1y,t−1y+n(2)0.510∗∗∗(20.18)

HHI Supplykt−5y,t−3y+n(3)0.510∗∗∗(22.36)

Market Sharek,it−1m,t−1m+nMarket Sharek,i
t,t+n
(4)

0.377∗∗∗(15.78)57

Market Sharek,it−1y,t−1y+n(5)

0.866∗∗∗(24.09)Market Sharek,it−5y,t−5y+nN
R2Time FE

151,655
0.08
Yes

149,595
0.26
Yes

130,487
0.28
Yes

46,6550.19
Yes

1,408,437
0.75
Yes

(6)

0.838∗∗∗(18.23)1,230,588
0.72
Yes

(7)

0.736∗∗∗(9.39)
441,360
0.57
Yes

Table 8: Segmentation Persistence. Table presents the regression of segmentation measures on its lags as well as (1 plus
the log of the) average notional book size of banks in that market. The notional book size of the banks active in that market,
weighted by their market share in that market. Lags for 1 month are 21 business days, 1 year is 250 business days, and 5 years is
1,250 business days. Within R2 reported. t-statistics shown using robust standard errors clustered by market and date for the
first four columns and clustered by bank and date for the last three columns, where ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Bank FX Sharei,kt,ctpty
Bank FX Sharei,kt−6m,ctpty
Other Bank FX Sharei,kt,ctpty(1)(2)

0.951∗∗∗(43.50)0.938∗∗∗(32.10)0.0539(1.50)

Bank Loan Sharei,kt,ctpty58

Other Bank Loan Sharei,kt,ctptyNR2

93,636
0.59

93,636
0.59

(3)

(4)

(5)

0.234∗∗∗(3.95)

0.174∗∗(2.36)
0.265∗∗∗(3.39)

0.571∗∗∗(4.41)
0.155∗∗(2.33)
0.0332(0.74)

208,386
0.05

208,386
0.06

208,3860.14(6)0.927∗∗∗(24.60)0.0211(0.66)
0.108(1.19)−0.00849(−0.28)93,636
0.60

Table 9: Segmentation across Counterparties. Table presents the regression of Bank FX Sharei,kt,ctpty on several variables.
i,kBank FX Sharet,ctpty is the bank’s notional FX swap exposures with that counterparty-currency pair as a share of the bank’s
total notional FX swaps on that day. Other Bank FX Sharei,kt,ctpty is the average market share of all banks except bank i.
i,ki,kBank Loan Sharet,ctpty and Other Bank Loan Sharet,ctpty are calculated analogously using secured and unsecured loans rather
than FX positions. Constant omitted. Within R2 reported. t-statistics shown using robust standard errors clustered by date andbank where ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Bank Safe Asset Ratioi,k
t,t+n

59

(1)

(2)

(3)

(4)

Market Sharek,it,t+n−0.159∗∗∗(−3.02)−0.169∗∗∗(−2.91)−0.087∗∗∗(−3.00)−0.010∗∗∗(−4.08)N
R2Time FE
Bank FE
Weighted

693,041
0.00
No
No
No

693,041
0.00
Yes
No
No

693,041
0.00
Yes
Yes
No

502,684
0.00
Yes
Yes
Yes

Table 10: Banks with larger market shares have lower safe asset ratios. Table presents the regression ofi,kkBank Safe Asset Ratioi,kt,t+n on the bank’s FX market share, Bank FX Sharet,t+n when Net t,t+n > 0, which corresponds to times
when banks receive foreign currency and have demand for foreign safe assets. The market share is defined as the bank’s notional
FX swap exposures in that market as a share of all banks’ total notional FX swaps in the market on that day. Regression
includes date, bank, and tenor fixed effects, and we weight the regression by the square root of the market’s share of the total
daily gross notional. Constant omitted. Within R2 reported. t-statistics shown using robust standard errors clustered by dateand bank where ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Panel A: Event Study.
Dependent Var.: |Basiskt,t+n |Dependent Var.: Net kt,t+n

(1)

(2)

(3)

(4)

(5)

(6)

14.59∗∗∗(7.82)
16.38∗∗∗(6.10)−2.11∗∗∗(−3.21)14.96∗∗∗(7.94)
23.70∗∗∗(6.96)−2.37∗∗(−2.23)−1.78∗∗∗(−3.93)−0.26(−0.29)0.53∗∗(2.52)−1.89∗∗∗(−3.30)1.65(1.51)
0.60∗(1.73)

Constant

10.07∗∗∗(4.72)

3.56(1.06)

14.52∗∗∗(7.47)
21.11∗∗∗(5.21)−1.95∗(−1.98)
11.60(1.54)
7.68∗(1.79)

5.66∗∗∗(4.63)

4.01∗∗∗(3.10)−1.90∗∗∗(−3.13)
1.63(1.34)
0.60(1.61)
0.10(0.05)
4.04∗∗(2.57)N
R2
Tenor FE
Weighted

959
0.51
No
Yes

959
0.54
Yes
Yes

959
0.55
Yes
Yes

959
0.01
No
Yes

959
0.04
Yes
Yes

959
0.04
Yes
Yes

I(Post)∆Depositskt+n
I(Post) × ∆Depositskt+n
Supply HHIkt,t+nPanel B: Placebo Event Study.
Dependent Var.: Net kt,t+n

Dependent Var.: |Basiskt,t+n |
(1)

(2)

(3)

∗∗∗

−1.46
(−5.66)
15.61∗∗∗(7.19)
0.47(1.31)−1.64(−41.16)
20.26∗∗∗(7.81)
0.62∗∗(2.81)

Constant

12.49∗∗∗(6.50)N
R2
Tenor FE
Weighted

910
0.60
No
Yes

I(Post)∆Depositskt+n
I(Post) × ∆Depositskt+n
Supply HHIkt,t+n

(4)

(5)

(6)

1.44
(2.73)
0.01(0.01)
0.19(0.71)

1.72(2.93)
1.95(1.54)
0.00(0.00)

8.36∗∗∗(3.15)−1.76(−9.70)17.91∗∗∗(5.57)
0.70∗∗∗(3.35)
11.22(1.62)
12.37∗∗∗(3.40)

2.49(1.59)

0.70(0.47)

1.70∗∗∗(2.91)
1.54(1.01)
0.02(0.05)
1.94(0.73)
1.40(0.70)

910
0.57
Yes
Yes

910
0.59
Yes
Yes

910
0.01
No
Yes

910
0.04
Yes
Yes

910
0.05
Yes
Yes

∗∗∗

∗∗∗

∗∗

∗∗∗

Table 11: March 2023 Event Study. Table shows the results of the regression|Basiskt,t+n | = α+γ1 I(Post)+γ2 ∆Depositskt+n +γ3 I(Post)×∆Depositskt+n +εkt,n . We transform∆Depositskt+n to a modified z-score using their median and standard deviation; supply HHI
is transformed to a modified z-score using its full sample median and standard deviation.
I(Post) is equal to 1 for days after March 9, and 0 otherwise. The window is the 2 weeks
before and after the event. Panel B is a placebo test which shifts the treatment date to
February 9. Regression includes tenor fixed effects and we weight the regression by thesquare root of the market’s share of the total daily gross notional. Within R2 reported.
t-statistics shown using robust standard errors clustered by market and date where ∗ p < 0.10,∗∗
p < 0.05, ∗∗∗ p < 0.01.

60

A

Online Appendix

A.1

Model

Derivation of Expression for the Basis (No Heterogeneity)
First, we observe that∂
(Pf,k − αi,k r − (1 − αi,k )rk )Zi,k =Pf,k − αi,k r − (1 − αi,k )rk∂Zi,k2(r − rk )2 + rλs,k + rk γi σi,k=Pf,k −
2
λs,k + γi σi,kFor risk, and safe asset scarcity, the relevant part of the first-order condition can be
expressed as
∂ Xγ
λs,k0
2
2
2
(αi,k0 Zi,k0 )2 σi,k((1 − αi,k0 )Zi,k0 )2 = αi,kγσi,kZi,k + λs,k (1 − αi,k )2 Zi,k .
0 +∂Zi,k k0 2
2
We combine the risk and safe asset scarcity terms by noting that22αi,kZi,k + λs,k (1 − αi,k )2 Zi,k =γσi,k2(r − rk )2 + λs,k γσi,kZi,k .
2
λs,k + γi σi,kWhen intermediaries are identical, this reduces to:−(r − rk )2 + λs,k γσk2 Xk
λs,k + γσk2
Ni

(9)For balance sheet costs, the relevant part of the first-order condition can be expressed as
2
X
X
X
∂
|Zi,k0 | = Sign(Zi,k )|Zi,k0 | = −Sign(Xk )|Zi,k0 |.
∂Zi,k k0
k0
k0!

Averaging across intermediaries yields −Sign(Xk )P

k0 |Xk0 |.

For counterparty costs, therelevant part of the first-order condition is22X X
X X Xc,k0
∂ 1
∂ 1
λCP
|Zi,k0 ,c | =λCP|Zi,k0 |∂Zi,k 2
∂Zi,k 2
Xk 0
c
c
k0
k0
X Xc,k X Xc,k0= − λCP Sign(Xk )|Zi,k0 |c Xk k 0 X k 0

!

!





X2
X X Xc,k0
 c,k |Zi,k | + c,k= − λCP Sign(Xk )|Zi,k0 | .
2
0
X
X
X
0
k
k
c
k
k 6=k
X

61

Averaging across intermediaries, we observe that:1
λCP × Sign(Xk )Nii

X2
X X Xc,k0
 c,k |Zi,k | + c,k|Zi,k0 |Xk2
Xk k0 6=k Xk0
c

XX


=




1
λCP × Sign(Xk ) 

Ni



|Xk |
|

2
X Xc,k

+

c

Xk2

{z

}

X Xc,k Xc

Xk k0 6=k|Xc,k0 |.
Demand Concentration in k

Putting everything together, we have that(r − rk )2 + rλs,k + rk γσk2
λs,k + γi σk2
Xk (r − rk )2 + λs,k γσk2+
Niλs,k + γσk2
Sign(Xk ) X+λBS|Xk0 |Ni
k0

Pf,k =(Risk and Safe Asset Scarcity)(Balance Sheet Costs)2
X Xc,kX Xc,k X

Sign(Xk ) 

+λCP
|X
|
+
|Xc,k0 |
k

.
2
Ni


c Xk
c Xk k0 6=k





|

{z

}

Demand Conc. in k

Note that Basisk = Pf,k − rk . Observing that2(r − rk )2 + rλs,k + rk γi σi,k(r − rk )2 + (r − rk )λs,k− rk =,
22
λs,k + γi σi,kλs,k + γi σi,kwe get the expression provided in the main text.
Derivation of Segmentation Predictions
For the segmentation predictions, for simplicity, we set λCP = λBS = 0 and rk = r. We first
derive equilibrium quantities in the model focusing only on intermediaries that participate
in the basis trade. Based on these quantities, we then turn to financial intermediaries’
participation decisions based on the fixed participation costs.
Equilibrium for Participating Intermediaries. Assuming they participate (Zi,k 6= 0),intermediary i’s first order condition can be written as
Basisk = −2
λs,k γσi,kZi,k .
2
λs,k + γσi,k(10)This holds for each participating intermediary i, meaning that for any two intermediaries i62and j,2
2
λs,k γj σj,kλs,k γi σi,kZ=
Zj,k ,i,k2
2
λs,k + γi σi,kλs,k + γj σj,k(11)

or equivalently,
22λs,k γi σi,kλs,k + γj σj,kZj,k =
Zi,k .
22
λs,k + γi σi,kλs,k γj σj,k

The market clearing condition is
Zi,k

(12)

j Zj,k = −Xk , which can be written asP

2
2
X λs,k + γj σj,kλs,k γi σi,k
= −Xk ,
22
λs,k + γi σi,kλs,k γj σj,kj

(13)which provides a solution for Zi,k :2
λs,k + γi σi,kZi,k = − P λ +γ σ2 ×
.
2
j j,k
s,k
λs,k γσi,k

Xk

j

(14)

2
λs,k γj σj,kWe can substitute this into the first-order condition to derive an expression for the basis.
Basisk = P λjXk2s,k +γj σj,k2
λs,k γj σj,kWe can further write
2
X λs,k + γi σi,ki

2λs,k γi σi,k

(15)

.

2
P λs,k +γj σj,kj λs,k γj σ 2
j,k

as

Q
2
2
i (λs,k + γi σi,k ) j6=i λs,k γi σj,k=Q
2i λs,k γi σi,k
Q
2
2 Q
2
X j6=i λs,k γj σj,kX γi σi,kj6=i λs,k γj σj,k
=λs,k
+
Q
Q
2
2
j λs,k γj σj,kj λs,k γj σj,k
i
i
P

=

Np,k X 1+,
2λs,ki γi σi,kwhere Np,k ≤ Ni is the number of intermediaries participating in the basis trade. Hence, we
can write the basis asXk.
P+ i γi σ12λs,kBasisk = Np

(16)

i,k

Intermediaries’ Participation Decisions. We focus on Nash equilibria. In order toparticipate in the basis trade in currency k, intermediaries must find it worthwhile to pay
the fixed cost of participation in equilibrium. Using the equilibrium quantities previously
63derived, this means that, assuming intermediary i participates,
λs,k
γi
2
2
(αi,k Zi,k )2 σi,k−(1 − αi,k )2 Zi,k2222λs,k γi σi,k1 λs,k γi σi,k
2
2
=
Z
−
Zi,k
i,k
2
2
λs,k + γi σi,k2 λs,k + γi σi,kλP C,k < − Basisk Zi,k −

|

=

{z

}

Basisk Zi,k

|

{z

}

Risk and Safe Asset Scarcity

2
1 λs,k γi σi,k
2
Zi,k
.
2
2 λs,k + γi σi,kλs,k +γi σ 2

1For ease of notation, we denote A ≡ λs,k γi σ2i,k = λs,k+ γi σ12 , and B ≡
i,k

i,k

2
λs,k +γj σj,kj6=i λs,k γj σ 2 , wherej,kP

j are the participating intermediaries (other than intermediary i). We want to solve for A tocharacterize i’s participation threshold (where a higher A indicates that substitution fromsafe assets is less risky for intermediary i). We can re-write our participation condition as:
λP C,k <=2
1 λs,k γi σi,k2Zi,k22 λs,k + γi σi,k1 A2 Xk2.
2A (A + B)2

This yields a quadratic inequality for A to justify intermediary i’s participation:
1
λP C,k A + 2BλP C,k − Xk2 A + λP C,k B 2 < 0.
2
2





This yields a lower and upper bound for A:
1 2
X − 2BλP C,k −
2 k

r

1 2
X − 2BλP C,k2 k

2− 4λ2P C,k B 2

2λP C,k≤

1 2
X − 2BλP C,k +
2 k

r

1 2
X − 2BλP C,k2 k

2λP C,k

2≤A− 4λ2P C,k B 2.

Denoting the lower and upper bounds as A and A, we note that the bounds imply thatintermediary i enters if and only if
A≤

1
1
+
≤ A,
2
λs,k γi σi,kwhich implies that1
1
2
≤ γi σi,k≤.
(A − 1/λs,k )(A − 1/λs,k )

(17)

64

2
The upper bound for γi σi,kmeans that intermediary i must not face too much risk in

substituting into risky assets (relative to participating intermediaries); otherwise, the fixed2
participation cost is too high for them. The lower bound for γi σi,kmeans that intermediary icannot have a risky alternative that is too close to risk-free; otherwise, they could drive theprofits of the basis trade to zero, making the trade sufficiently unprofitable to justify their
participation.
2
Importantly, with sufficient heterogeneity in σi,kacross intermediaries, we have limited

participation in market k.
Basis with Balance Sheet Costs, Counterparty Costs, and Heterogeneity
In currency k, we denote the number of participating intermediaries as Np,k . We takeparticipating intermediary i’s first order condition with respect to Zi,k . We let rk = r for
simplicity.
For risk, and safe asset scarcity, the relevant part of the first-order condition can be
expressed as
∂ Xγ
λs,k
2
2
2
(αi,k Zi,k )2 σi,k+((1 − αi,k )Zi,k )2 = αi,kγσi,kZi,k + λs,k (1 − αi,k )2 Zi,k .
∂Zi,k k 2
2
We combine the risk and safe asset scarcity terms by noting that
2
2
γσi,k
αi,k
Zi,k + λs,k (1 − αi,k )2 Zi,k =2
λs,k γσi,kZi,k .
2
λs,k + γi σi,kFor balance sheet costs, the relevant part of the first-order condition can be expressed as
2
X
X
∂ 1
0
λBS
|Zi,k | =λBS Zi,k + λBS Sign(Zi,k )|Zi,k0 |∂Zi,k 2
0
0
k
k 6=k

!

=λBS Zi,k − λBS Sign(Xk )X|Zi,k0 |.

k0 6=kFor counterparty costs, the relevant part of the first-order condition is22X X
X X Xc,k0
∂ 1
∂ 1
λCP
|Zi,k0 ,c | =λCP|Zi,k0 |∂Zi,k 2
∂Zi,k 2
Xk 0
c
c
k0
k0
X Xc,k X Xc,k0= − λCP Sign(Xk )|Zi,k0 |c Xk k 0 X k 0!

!

=λCP Zi,k2
X Xc,kc

65

Xk2− λCP Sign(Xk )X X Xc,k Xc,k0
c k0 6=kX k Xk 0|Zi,k0 |.

So, the first order condition can be expressed as
22
X Xc,kλs,k γσi,k
+
λ
+
λ
Zi,k
Basisk = −
BS
CP
2
2
λs,k + γi σi,kc Xk!

+ λBS Sign(Xk )|Zi,k0 | + λCP Sign(Xk )X

X X Xc,k Xc,k0

Xk Xk 0c k0 6=kk0 6=k|Zi,k0 |,which we can re-write in terms of Zi,k :Zi,k = −Basisk + λBS Sign(Xk )X

X X Xc,k Xc,k0|Zi,k0 | + λCP Sign(Xk )Xk Xk 0c k0 6=kk0 6=k2
2
X Xc,kλs,k γσi,k
×
+
λ
+
λ
BS
CP
2
2
λs,k + γi σi,kc Xk

|Zi,k0 |!−1.

Summing over participating intermediaries,
Xk =Np,k BasisXk − λBS Sign(Xk )k0 6=k |Zi,k0 | − λCP Sign(Xk )P

Xc,k Xc,k0
k0 6=k Xk Xk0 |Zi,k0 |

P P
c

2
2
P Xc,kλs,k γσi,k
+
λ
+
λ
2
BS
CP
c
λs,k +γi σi,k
Xk2

i

.

This can then be re-written as:
−1


Np,k

Basisk =Xk 


1

X
i




2
P X2 
λs,k γσi,k+ λBS + λCP c Xc,k2
2
λs,k +γi σi,kk

+

λBS Sign(Xk ) 

X X
i

|

−1





Np,k

Np,k

1

X



|Zi,k0 |

k0 6=k2
λs,k γσi,k2λs,k +γi σi,k

i

{z

}

+ λBS + λCP

2
Xc,k
c X2
k

P





Balance Sheet Usage of Participating Intermediaries

+ λCP Sign(Xk ) 

X X Xc,k Xc,k0
c k0 6=k|Xk Xk 0

{z



−1


Np,k

1

X

|Zi,k0 | }iCounterparty Exposure of Participating Intermediaries

The expression for the basis yields the same predictions as the main text. In addition, wenote that the expression highlights the logic behind Prediction 6 in a more general sense: thebalance sheet usage and counterparty exposure of the participating intermediaries are thefeatures that are relevant for understanding the basis in currency k, not the balance sheet
usage and counterparty exposure of the overall financial sector.
66




2
2 
P Xc,kλs,k γσi,k
+
λ
+
λ
2
BS
CP
c X2
λs,k +γi σi,kk.

Segmentation with Markups
We illustrate how segmentation also gives rise to a larger basis via a markup channel, in
addition to the risk and safe asset scarcity channels that we focus on in our main analysis.
Rather than assuming exogenous customer demand, as in the main analysis, we assume
that customer demand can be represented by an aggregate inverse demand curve of the form
Basisk = πk − βXk .
This form can represent, for example, currency hedging demand via forwards from customers
who adjust their amount of hedging based on the cost of hedging (the basis), as studied in
Du and Huber (2023).
To simplify our analysis, as before, we consider the case where λBS = λCP = 0 and rk = r.
The financial intermediary i’s problem ismaxXZi,k ,αi,k ∀kXγ−Basisk Zi,k −

k

k

2

2
(αi,k Zi,k )2 σi,k
−

X1
k 2

λs,k ((1 − αi,k )Zi,k )2 ,which can be re-written as
max

X

Zi,k ∀k−(πk + βX

Zj,k )Zi,k −jk2
λs,k γi σi,k1X
2ai Zi,k, where ai ≡.
22 k
λs,k + γσi,kTaking the first order condition with respect to Zi,k , we get that(ai + β)Zi,k = −πk − βXZj,k .

j

The right-hand side of the equation is common for all participating intermediaries, so ∀i, j,
Zj,k =

ai + βZi,k .
aj + β

Then, we have that(ai + β)Zi,k = −πk − β(ai + β)Zi,k

X
j

1
,
aj + βwhich provides a solution for Zi,k :Zi,k = −πk(ai + β) 1 + β

1j aj +β

P

(18)

.

67

The market clearing condition is Xk = −
Xk =

πk
1+β

X
1
j aj +βPij Zj,k , soP1.
ai + βPlugging this into customers’ inverse demand functions, we can solve for the basis:
Basisk =πk − βXkπk=
PNp,k 1 .
1 + β j aj +β
To consider the impact of markups on the basis, we can compare the basis with intermediary
i’s marginal costs:
Markupk =Basisk − (−ai Zi,k )
πk
πk

=
− ai
P
P
1
1 + β j aj +β(ai + β) 1 + β j
=

βπk


(ai + β) 1 + β

PNp,k
j

1
aj +β

1
aj +β



.

We observe that Basisk increases with segmentation, as we decrease the number of participatingintermediaries. Part of this effect comes from risk and safe asset scarcity, captured by theai terms, which are the focus of our analysis in the main text. An additional component of
the effect arises from oligopolistic pricing, with intermediaries using their market power tocharge markups.

A.2

Data Details

FR 2052a Complex Institution Liquidity Monitoring Report
We limit the sample to firms that report daily data through the sample, focusing on the largest
consolidated entity firm rather than individual material entities. We exclude transactions
with internal counterparties. We drop foreign exchange options except when it appears theforeign exchange flag is a data entry error. Foreign exchange options represent a small shareof the notional positions for banks in our sample, so including foreign exchange options that
could provide dollar lending in our sample does not meaningfully change the results. We droptransactions in which one leg’s currency is denominated as “other” and keep transactions that
include USD. We exclude transactions where the first leg has not yet settled, since we areinterested in actual lending rather than future obligations to lend. We include transactions
that have likely already settled, as is the case for a handful of transactions when the forward68

start date and maturity date are reported to be the same although this convention typicallyindicates a forward transaction that will have an open maturity. We drop roughly half adozen dates with outliers, and we drop dates where there are fewer than seven filers’ data
available. Since the set of banks reporting daily has changed over time, we focus on a subsetof the largest banks that have consistently been daily reporters over the whole sample.35 The
data collection instructions were modestly updated in April 2022 to provide additional details
on certain segments, and we clean the data so the data before and after 2022 are directlycomparable. During the brief period that banks reported two sets of data, one to satisfy
the pre-April 2022 instruction data and the other to satisfy post-April 2022 instructions,
we use the previous instruction data. We also adjust the maturities of the contracts to beconsistent through the sample as the maturity buckets for some increased by 1 day with theupdated instructions (e.g., 1 to 2 year contracts, previously reported at 366 days, started
being reported at 365).
Level 1 HQLA Assets
The following security types are considered level 1 HQLAs so long as they meet the assetspecific tests in section 20 of Regulation WW:• Cash• Debt issued by the U.S. Treasury• U.S. Government Agency-issued debt (excluding the U.S. Treasury) with a US Government guarantee• Vanilla debt (including pass-through MBS) guaranteed by a U.S. Government Agency,
where the U.S. Government Agency has a full U.S. Government guarantee• Structured debt (excluding pass-through MBS) guaranteed by a U.S. Government
Agency, where the U.S. Government Agency has a full U.S. Government guarantee• Other debt with a U.S. Government guarantee• Debt issued by non-U.S. Sovereigns (excluding central banks) with a 0% RW• Debt issued by multilateral development banks or other supranationals with a 0% RW
35Over time, a handful of firms move from daily to monthly filing, or vice versa. Including these firms does
not materially affect our results since the firms changing their filing frequency account for a comparatively
small share of dollar lending.

69• Debt with a non-U.S. sovereign (excluding central banks) or multilateral developmentbank or other supranational guarantee, where guaranteeing entity has a 0% RW• Debt issued or guaranteed by a non-U.S. Sovereign (excluding central banks) that does
not have a 0% RW, but supports outflows that are in the same jurisdiction of thesovereign and are denominated in the home currency of the sovereign• Securities issued or guaranteed by a central bank with a 0% RW• Securities issued or guaranteed by a non-U.S. central bank that does not have a 0%
RW, but supports outflows that are in the same jurisdiction of the central bank andare denominated in the home currency of the central bank
OIS and FX Rates
We adjust the OIS rates for two currencies: CHF and EUR. CHF OIS rates were based on
TOIS fixings until December 29, 2017 when it switched to SARON fixings. As a result, we
split the CHF OIS rates to use the TOIS swaps before that date and the SARON swaps afterthat date. Bloomberg also does not have a full time-series for the 3w, 4m, and 5m OIS rate;
when it is missing, we linearly interpolate the rate by estimating the curve each day. For the
3w CHF tenor, we estimate it based CHF OIS tenors with fewer than 100 days maturity; forthe 4m and 5m, we use CHF OIS tenors with maturities between 28 and 181 days, exclusive.
EURO OIS rates were based on EONIA until January 2, 2022, when the benchmark changed
to ESTR. ESTR was introduced in October 2019 but Bloomberg provides backfilled rates
for all tenors in our sample except for 3 weeks. We use the ESTR OIS rates when they areavailable, otherwise we use EONIA OIS rates.
We clean five data points for JPY forward points in April 2019 that appear incorrect
because the days to maturity for consecutive contracts are the same. For example, the 2w
and 3w forwards list the same days to maturity on April 11, 2019. In these five cases, we
manually change the days to maturity to 7 × the contract’s maturity in weeks.

70

A.3

Figures

Percent
80
75

79

77

78

78

70

75

74

67

60

70

67
59

58
54

53

40

71
20

0

1w

2w

3w

1m

2m

3m

4m

5m

6m

9m

1y

2y

3y

4y

5y

Figure A1: Proportion of CIP Deviation Variance Explained by First Principal Component. Figure plots the
proportion of the variance explained by the first principal component after estimating 6 principal components across the signed
CIP deviations by tenor.

Net (Percent)
1w
40

6m
20

20

5

10

0

0

0

-20
-40

-5

-10
16 17 18 19 20 21 22 23

1y
10

-10
16 17 18 19 20 21 22 23

16 17 18 19 20 21 22 23

Net ($ Billion)
1w

6m

1y

10

60

60

5

40

40

0

20

20

-5

0

0
-20

-20

-10
16 17 18 19 20 21 22 23

16 17 18 19 20 21 22 23

16 17 18 19 20 21 22 23

Notional ($ Billion)
1w
100

6m
400

80

800

300

60

1y
1,000

600

40

200

20
0

400

100
16 17 18 19 20 21 22 23200
16 17 18 19 20 21 22 23

16 17 18 19 20 21 22 23

Figure A2: Net for EUR across several tenors. Top panel plots our main measure,Net kt,t+n , for EUR contracts across various tenors. Middle panel plots the level of net dollar
lending. Bottom panel plots the notional dollars across borrowing and lending transactions
for EUR across the highlighted tenors. All figures plot weekly averages based on daily
observations.

72

AUD

CAD

CHF

EUR

GBP

JPY

.06

.04

Density

.02

0
.06

73
.04

.02

0

-100

-50

0

50

100 -100

-50

0

50

100 -100

-50

0

50

100

Net (Percent)Figure A3: Most Markets are Nearly Matched Books. Figure plots the histogram of Net kt,t+n across all tenors within agiven currency.

Total USD Lent + USD Borrowed ($ Billions)
150

100

74

50

0
-100

-50

0

50

Net (Percent)Figure A4: Size vs. Net. Figure presents the binscatter of the size of the market—defined as the sum of dollars lent andborrowed in billions—at the daily-currency-tenor level.

Basis (bps)
-10
-20
-30

75

-40
-50
-60
-20

-10

0

10

20

30

Net (Percent)Figure A5: Basis vs. Net. Figure presents the binscatter of the basis on Net kt,t+n after averaging on a monthly frequency.
Net kt,t+n is at the month by currency by tenor level.

AUD
GBP CADEUR
JPY CHF

0
Net (Percent)

5

10

AUD
GBP CAD EUR
JPY
CHF

-10

-5

0
Net (Percent)

AUD
CAD EUR
CHF
JPY

-5

0
Net (Percent)

5

20
0
-20
-40
-60
-80

10

CHF

-10

-5

AUD
CAD
GBP
EUR
JPY CHF-10-50
Net (Percent)-50
Net (Percent)

5

20
0
-20
-40
-60
-80

0
Net (Percent)

JPY

5

10

20
0
-20
-40
-60
-80

10

CAD GBP
EUR
JPY

CHF

-10

-5

-10

JPY

-5

0
Net (Percent)0
Net (Percent)

5

10

4y

AUD
CAD
GBP
EUR

CHF

10

AUD

3y
GBP
EUR

5

6m

CAD
GBP EURCHF JPY-10

Basis (bps)

AUD
CAD

10

AUD

1y
20
0
-20
-40
-60
-80

20
0
-20
-40
-60
-80

3m
Basis (bps)

GBP

-10

Basis (bps)76Basis (bps)

1m
20
0
-20
-40
-60
-80

5

Basis (bps)-5

3w

Basis (bps)

-10

20
0
-20
-40
-60
-80

Basis (bps)

2w
Basis (bps)Basis (bps)

1w
20
0
-20
-40
-60
-80

5

10

20
0
-20
-40
-60
-80

AUD
CAD
GBP
EUR
CHF
JPY

-10

-5

0
Net (Percent)5

10

Figure A6: By Tenor: Basis vs. Net. Figure presents the scatter of the average basis on the average Net kt,t+n .

β by Tenor
0

-1

77

-2

-3
1w

2w

3w

1m

2m

3m

4m

5m

6m

9m

1y

2y

3y

4y

Figure A7: Regression Coefficient by Tenor: Basis vs. Net. Figure the β estimated by running the following regression
separately for each tenor: Basiskt,t+n = α + βNetkt,t+n + εkt,t+n .

Unencumbered Safe Assets
1w
2w
3w
1m
2m
3m
AUD 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
CAD 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
CHF 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
EUR 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
GBP 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
JPY 4m
5m
6m
9m
1y
2y
3y
4y

Unencumbered Broad Assets
1w
2w
3w
1m
2m
3m
AUD 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
CAD 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
CHF 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
EUR 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
GBP 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
JPY 4m
5m
6m
9m
1y
2y
3y
4y

0

1
2
3
Safe Asset Ratio

4

0

1
2
3
Broad Asset Ratio

4

Figure A8: Safe Asset Scarcity by Market. Figure plots median safe asset and broad
asset ratios when Net kt,t+n is positive and matching the tenor of the net dollar lending and theforeign safe asset.

78

Unencumbered Safe Assets

Unencumbered Broad Assets

1w
2w
3w
1m
2m
3m
AUD 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
AUD 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
CAD 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
CAD 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
CHF 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
CHF 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
EUR 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
EUR 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
GBP 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
GBP 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
JPY 4m
5m
6m
1y
2y
3y
4y

1w
2w
3w
1m
2m
3m
JPY 4m
5m
6m
1y
2y
3y
4y
0

2

4
6
8
Safe Asset Ratio

10

0

2

4
6
8
Broad Asset Ratio

10

Figure A9: Safe Asset Scarcity by Market with Rounded Tenors. Figure plots
median safe asset and broad asset ratios when Net kt,t+n is positive and when bucketing net
dollar lending and foreign safe assets into the nearest benchmark tenor. Unlike Figure A8
this measure allows for some maturity mismatch in the trade. Values are truncated at 10.

79

Safe Assets

Broad Assets

Unencumbered and Encumbered
1w
2w
3w
1m
2m
3m
AUD 4m
5m
6m
9m
1y
2y
3y
4y
2y
1w
2w
3w
1m
2m
CAD 3m
4m
5m
6m
9m
1y
3y
4y
1y
1w
2w
3w
1m
2m
CHF 3m
4m
5m
6m
9m
2y
3y
4y
1w
2w
3w
1m
2m
3m
EUR 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
GBP 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
JPY 4m
5m
6m
9m
1y
2y
3y
4y

Unencumbered and Encumbered
1w
2w
3w
1m
2m
3m
AUD 4m
5m
6m
9m
1y
2y
3y
4y
2y
1w
2w
3w
1m
2m
CAD 3m
4m
5m
6m
9m
1y
3y
4y
1y
1w
2w
3w
1m
2m
CHF 3m
4m
5m
6m
9m
2y
3y
4y
1w
2w
3w
1m
2m
3m
EUR 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
GBP 4m
5m
6m
9m
1y
2y
3y
4y
1w
2w
3w
1m
2m
3m
JPY 4m
5m
6m
9m
1y
2y
3y
4y

0

2

4
6
8
Safe Asset Ratio

10

0

2
4
6
8
Broad Asset Ratio

10

Figure A10: Safe Asset Scarcity by Market, including encumbered assets. Figure
plots median safe asset and broad asset ratios when Net kt,t+n is positive when matching thetenor of the net dollar lending and the foreign safe asset. Values are truncated at 10.

80

Supply HHI, by tenor
1w

4,500
3w
1m

4,000

2w

2m

3,500
81

3m

3,000
4m
5m

2,500
23.0

24.0

25.0
ln(1+Notional)

4y3y 6m 2y
9m

1y

26.0

Figure A11: Supply Segmentation. Figure plots the average of daily Tenor Supply HHIt,t+n against (log of 1 plus) the
average notional of that market.

Supply HHI, by currency
JPY

2,500
2,400

AUD
EUR

2,300 CHF
82

2,200
CAD

2,100
23.5

24.0

GBP

24.5
25.0
ln(1+Notional)

25.5

26.0

Figure A12: Supply Segmentation by Currency. Figure plots the average of daily Currency Supply HHIkt against (log of
1 plus) the average notional of that market.

Supply HHI
6,000
CHF-1w
CHF-3w
CHF-1m
CHF-2w

5,000

4,000

83
3,000

2,000

CAD-4yCAD-3yCAD-2y28.4

CAD-1yEUR-1w
EUR-2w
EUR-3w
EUR-1m
JPY-1w
JPY-1m
JPY-2w
GBP-1w
CAD-1m
JPY-3w
GBP-2w
CAD-1w
GBP-1m
CHF-2m
GBP-3w
CAD-2w
AUD-1w
CAD-3w
AUD-1m
AUD-2w
EUR-2m
AUD-3w
CAD-2m
JPY-2mCAD-3m
CHF-3m
EUR-3m
JPY-3m
GBP-2m
CAD-4m
JPY-3yJPY-2yCAD-5m
AUD-2m
JPY-4m
JPY-4yEUR-4m
JPY-5m
CHF-4m EUR-5m
JPY-1yGBP-3m
CHF-5m
AUD-4yAUD-3m
JPY-9mJPY-6m
AUD-2yAUD-1yCAD-6m
AUD-3yEUR-3yEUR-6m
EUR-4yAUD-5m
AUD-4m
CHF-4yCHF-2yEUR-2yCHF-6m
GBP-4m
CHF-3yCHF-9m
CHF-1yEUR-1yAUD-6m
GBP-4yGBP-3yEUR-9m
AUD-9mGBP-5m
GBP-2yCAD-9m
GBP-6m
GBP-1yGBP-9m28.5

28.6
ln(1+Bank Notional)

28.7

28.8

Figure A13: Supply Segmentation vs. Bank Size Figure plots the average of daily Supply HHIkt,t+n against (log of 1 plusthe) the notional book size of the banks active in that market weighted by their market share.

Mean ($ Billions)
AUD

CAD

CHF

EUR

GBP

JPY

Mean

−0.4
−0.4
−0.4
−0.4
−1.0
−1.6
−1.5
−1.5
−3.7
−2.8
−3.0
0.7
−1.2
−0.8

−0.2
−0.2
−0.2
−0.2
0.0
1.2
0.7
0.4
−0.9
−2.9
−2.0
−1.2
−0.4
0.0

0.0
0.2
0.1
0.1
−0.4
0.0
0.0
−0.1
−0.3
−0.5
−1.8
−2.0
−1.9
−0.7

−1.2
−0.8
−0.8
−0.7
1.1
2.4
2.6
3.1
10.0
10.2
12.9
6.1
2.9
−3.6

−1.1
−1.2
−1.2
−1.2
−0.9
0.0
1.3
1.2
2.3
3.1
6.3
0.5
−0.1
0.6

−1.7
−1.1
−0.9
−0.8
−1.9
2.3
3.6
2.7
12.9
9.7
24.3
11.0
6.6
8.3

−0.8
−0.6
−0.5
−0.5
−0.5
0.7
1.1
1.0
3.4
2.8
6.1
2.5
1.0
0.6

Mean −1.3

−0.4

−0.5

3.2

0.7

5.4

1w
2w
3w
1m
2m
3m
4m
5m
6m
9m
1y
2y
3y
4y

AUD

Standard Deviation ($ Billions)
CAD
CHF
EUR
GBP

JPY

Mean

1w
2w
3w
1m
2m
3m
4m
5m
6m
9m
1y
2y
3y
4y

2.0
1.9
1.8
1.9
3.2
4.2
3.9
4.0
6.1
6.9
7.9
6.6
3.1
3.4

1.8
1.7
1.7
1.8
3.1
4.4
3.8
3.5
5.0
4.9
5.2
3.0
2.6
2.3

1.7
1.5
1.4
1.4
1.9
2.5
2.0
1.8
2.4
2.3
3.1
1.8
1.0
1.0

6.9
6.7
6.1
5.8
7.6
10.0
8.3
8.3
15.1
13.9
17.7
11.5
8.3
8.9

4.0
4.0
3.8
3.9
5.0
6.4
6.4
6.1
8.6
8.1
9.9
7.4
4.0
4.7

4.4
4.1
4.0
4.1
6.8
14.1
14.5
12.7
31.1
28.1
25.5
15.5
8.5
7.3

3.5
3.3
3.2
3.1
4.6
6.9
6.4
6.1
11.4
10.7
11.5
7.6
4.6
4.6

Mean

4.1

3.2

1.8

9.6

5.9

12.9

Table A1: Net Level Summary Statistics. Top panel plots the average level of daily
net dollar lending aggregated across all intermediaries in the sample, equal to the numeratorof Net kt,t+n for a given currency k and maturity t + n. Bottom panel plots the time-series
standard deviation of Net kt,t+n .

84

Days to Maturityln(1 + Gross Notional)

I(Quarter End)I(Year End)HMV ktNet kt,t+n0.046∗∗∗(0.00)

0.046∗∗∗(0.00)−0.006∗∗(0.01)−0.008∗∗∗(0.00)

0.007∗∗(0.02)

N

151,655

151,655

151,655

151,655

129,059

85

RtSP X

V IXt

Baat − Aaat

Net kt,t+n−0.002(0.38)−0.017∗∗∗(0.00)−0.012∗∗∗(0.00)

N

143,507

143,507

143,507

Table A2: Correlations. Table presents the correlation of Net kt,t+n at the day, by currency, by tenor level on days to maturity,ln(1 + Gross Notional), dummies equal to 1 for quarter or year ends (defined as the last week of the quarter or years) and 0
otherwise and HMV kt which is a measure of net lending by dealers from CFTC data analogous to Hazelkorn et al. (2023)’s
measure. Bottom panel compares the return on the SPX, and the levels of the VIX and Baa-Aaa spread. Correlations given
with ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Matched
Safe Asset
Unencumbered

Borrowing Currency

RoundedBroad Assets

Safe Asset

Broad Assets

Unencumbered
Unencumbered
Unencumbered& Encumbered Unencumbered & Encumbered Unencumbered & Encumbered

Unencumbered
Unencumbered & EncumberedMedianMean
Std. Dev.

0.03
0.73
18.40

0.04
0.67
3.69

0.10
1.44
32.79

0.46
2.47
11.73

0.25
6.46
87.41

0.60
14.76
165.500.84
31.98
676.372.46
119.42
1,924.34

CAD

MedianMean
Std. Dev.

0.01
0.61
18.78

0.06
2.06
60.50

0.03
1.01
26.70

0.13
3.20
79.98

0.17
5.55
73.17

0.45
3.12
30.69

0.64
24.05
438.501.93
17.74
163.84CHF

MedianMean
Std. Dev.

0.00
0.02
0.53

0.00
0.00
0.02

0.00
0.73
34.88

0.01
0.68
6.73

0.00
9.73
179.170.00
45.01
866.070.53
34.78
608.15

1.21
118.54
2,054.87

EUR

MedianMean
Std. Dev.

0.16
1.02
11.21

0.44
2.16
19.84

0.29
2.12
21.00

0.80
4.49
35.41

0.28
2.54
36.93

0.70
3.87
25.71

0.64
16.91
705.191.57
33.77
835.99GBP

MedianMean
Std. Dev.

0.07
0.93
31.64

0.10
2.85
23.69

0.18
2.11
55.94

0.32
7.58
59.49

0.21
3.10
49.81

0.91
5.55
31.45

0.66
13.08
211.453.05
37.85
535.56JPY

MedianMean
Std. Dev.

0.05
0.44
5.46

0.14
0.61
5.18

0.08
1.19
19.53

0.18
0.89
7.63

0.12
3.32
136.16

0.25
3.53
57.34

0.23
13.34
328.150.41
24.13
666.67USD

MedianMean
Std. Dev.

2.01
50.12
1,394.56

8.56
94.32
1,142.77

5.38
107.772,982.31

24.79
234.33
3,123.05

2.19
55.67
4,609.31

7.80
97.10
1,187.54

7.25
335.4460,064.85

28.72
342.05
5,107.52

Average of All excl. USD

MedianMean
Std. Dev.

0.05
0.63
14.34

0.13
1.39
18.82

0.11
1.43
31.81

0.32
3.22
33.49

0.17
5.12
93.77

0.48
12.64
196.130.59
22.36
494.63

1.77
58.57
1,030.2186

AUD

Table A3: Safe Asset Ratios. Table presents the average ratio of safe assets and broad assets, which reflect the value of
assets (either unencumbered or both unencumbered and encumbered) relative to the level of net lending in the given market.
Unencumbered and encumbered asset column data begins in May 2022.

Matched, incl. Swaps and Forwards
Safe Asset
Unencumbered

Borrowing Currency

Matched, incl. Firm Shorts

Broad Assets

Safe Asset

Broad Assets

Unencumbered
Unencumbered
Unencumbered& Encumbered Unencumbered & Encumbered Unencumbered & Encumbered

Unencumbered
Unencumbered & Encumbered

87

AUD

MedianMean
Std. Dev.

0.01
0.52
10.90

0.02
1.57
27.26

0.05
1.25
23.64

0.13
4.26
64.98

0.03
0.72
18.38

0.04
0.67
3.69

0.09
1.18
23.08

0.36
2.12
10.74

CAD

MedianMean
Std. Dev.

0.01
0.19
1.91

0.04
0.32
1.59

0.01
0.39
3.91

0.10
0.86
3.94

0.01
0.60
18.74

0.06
2.05
60.50

0.03
0.89
25.96

0.13
3.15
79.98

CHF

MedianMean
Std. Dev.

0.00
0.47
40.98

0.00
0.00
0.00

0.00
0.91
45.30

0.01
1.82
43.28

0.00
0.02
0.51

0.00
0.00
0.02

0.00
0.18
5.31

0.01
0.18
0.95

EUR

MedianMean
Std. Dev.

0.12
1.11
10.57

0.30
0.70
1.93

0.20
2.68
44.88

0.55
1.47
3.91

0.15
0.95
10.49

0.43
2.08
19.20

0.24
1.88
20.02

0.78
4.20
34.28

GBP

MedianMean
Std. Dev.

0.06
2.49
109.260.11
9.21
226.640.15
5.53
207.990.29
22.44
512.93

0.07
0.90
31.62

0.10
2.75
23.34

0.15
1.95
55.74

0.31
7.19
57.98

JPY

MedianMean
Std. Dev.

0.05
0.80
42.26

0.15
0.68
13.44

0.08
1.16
43.91

0.18
0.79
14.63

0.05
0.40
4.99

0.14
0.59
5.17

0.07
0.81
11.49

0.16
0.82
7.61

USD

MedianMean
Std. Dev.

1.39
51.31
1,398.32

5.45
91.14
963.803.72
108.042,900.46

16.48
225.04
2,396.41

1.62
44.16
1,328.84

8.15
91.63
1,100.99

4.25
77.27
2,612.97

24.02
210.52
2,407.17

Average of All excl. USD

MedianMean
Std. Dev.

0.04
0.93
35.98

0.10
2.08
45.14

0.08
1.99
61.60

0.21
5.27
107.28

0.05
0.60
14.12

0.13
1.36
18.65

0.10
1.15
23.60

0.29
2.94
31.92

Table A4: Safe Asset Ratios. Table presents the average ratio of safe assets and broad assets, which reflect the value of
assets (either unencumbered or both unencumbered and encumbered) relative to the level of net lending in the given market.
Unencumbered and encumbered asset column data begins in May 2022. First two columns include net forward positions inaddition to net swap lending (which changes the denominator of asset ratios), and last two columns net out firm shorts frombanks’ net long position in the assets (which changes the numerator).

Panel A: Safe Assets.
Unencumbered Safe Assets(1)
Net kt,t+n ($ Level)
AUD
CAD
EUR
GBP
JPY
Constant
N
R2Time FE
Sample(2)0.020(2.18)
265.068∗∗∗(3.33)
175.109∗∗∗(3.40)
1803.268∗∗∗(4.11)
592.817∗∗∗(3.59)
735.252∗∗∗(3.88)−27.346∗(−1.71)
74,997
0.29
Yes
Non-USD∗∗

Unencumbered & Encumbered Safe Assets(3)−1.149(−2.01)(4)22557.509∗∗∗(5.12)

0.166(4.74)
180.328∗(1.91)
96.310(0.56)
3899.425∗∗∗(3.94)
901.963(1.36)
530.823(1.32)−214.863∗∗∗(−4.91)61711.154∗∗∗(5.17)

76,658
0.01
Yes
USD

10,145
0.49
Yes
Non-USD

8,923
0.01
Yes
USD

∗∗

∗∗∗

−2.325(−1.60)Panel B: Broad Assets.
Unencumbered Broad Assets(1)
Net kt,t+n ($ Level)
AUD
CAD
EUR
GBP
JPY
Constant
N
R2Time FE
Sample(2)0.187(3.87)
822.035∗∗(2.20)
239.727(0.79)
4492.373∗∗∗(3.16)
2040.245∗(1.83)
18.434(0.03)−117.260(−1.10)10,099
0.38
Yes
USD

∗∗∗

Unencumbered & Encumbered Broad Assets(3)−4.749(−1.76)(4)126810.843∗∗∗(5.37)

0.303(3.72)
986.092∗∗(2.59)
369.490(0.73)
8279.851∗∗∗(3.31)
2593.438(1.65)
66.421(0.08)−264.553∗(−1.97)145684.497∗∗∗(5.40)

8,885
0.01
Yes
Non-USD

10,0990.41
Yes
Non-USD

8,885
0.01
Yes
USD

∗

∗∗∗

−5.599∗(−1.78)

Table A5: Net vs. Safe Asset Ratios. Table presents the regression of the level of
Net kt,t+n on the level of the assets—either HQLAs or broad assets—with the same maturity
and currency in millions of dollars: Asset (Level)kt,t+n = α + βNetkt,t+n (Level) + γ k + εkt,t+nwhere γ k is a currency fixed effect. Columns 1 and 3 limit the sample to observations withnet dollar lending (e.g., Net kt,t+n > 0). Columns 2 and 4 limit the sample to observations withnet dollar borrowing (e.g., Net kt,t+n < 0). Base level is CHF. Within R2 reported. t-statistics
shown using robust standard errors clustered by market and date where ∗ p < 0.10, ∗∗p < 0.05, ∗∗∗ p < 0.01.

88

(1)
Loan Demand HHIkt,t+nBilateral Sharekt,t+n
89

Constant
N
R2

0.0704∗∗∗(10.76)2165.9∗∗∗(22.47)3640.6∗∗∗(38.99)18,752
0.06

Table A6: Demand HHI estimate for full sample. Table presents the regression of Demand HHIkt,t+n = α +β1 Loan Demand HHIkt,t+n + β2 Bilateral Sharekt,t+n + εkt,t+n , where Loan Demand HHIkt,t+n is the unsecured and secured loancounterparty HHI calculated over the full sample, 2016 to 2023, and bilateral share is the share of bilateral FX transactions
compared to the total FX transactions in that market.

Unencumbered Assets
Supply Segmentation
Supply HHIkt,t+n × I(Net kt,t+n ≥ 0)
Supply HHIkt,t+n × I(Net kt,t+n < 0)
Safe Asset Scarcity
Netkt,t+n × I(Net kt,t+n ≥ 0)
Netkt,t+n × I(Net kt,t+n < 0)
Safe Asset Ratiokt,t+n × I(Net kt,t+n ≥ 0)
Safe Asset Ratiokt,t+n × I(Net kt,t+n < 0)
Broad Asset Ratiokt,t+n × I(Net kt,t+n ≥ 0)

(1)

(2)

(3)

(4)

21.73∗∗∗(4.93)
20.29∗∗∗(5.63)

21.67∗∗∗(4.94)
20.27∗∗∗(5.63)

21.71∗∗∗(4.93)
20.29∗∗∗(5.63)

21.67∗∗∗(4.94)
20.27∗∗∗(5.63)

1.65(0.35)
3.06(0.96)−27.02∗(−1.76)−0.37(−1.40)1.48(0.31)
3.05(0.96)

1.58(0.33)
3.06(0.96)−18.42∗∗(−2.49)−0.20(−1.36)1.45(0.31)
3.05(0.96)−57.00∗∗∗(−2.64)−0.19(−1.29)Broad Asset Ratiokt,t+n × I(Net kt,t+n < 0)Demand Concentration
Demand HHIkt,t+n × I(Net kt,t+n ≥ 0)
Demand HHIkt,t+n × I(Net kt,t+n < 0)Controls
Bank CDSkt,t+n × I(Net kt,t+n ≥ 0)
Bank CDSkt,t+n × I(Net kt,t+n < 0)
Govt CDSkt,t+n × I(Net kt,t+n ≥ 0)
Govt CDSkt,t+n × I(Net kt,t+n < 0)N
R2
Tenor FETime FE
Weighted

Unencumbered & Encumbered Assets−42.22∗∗∗(−3.38)−0.17(−1.30)4.54(1.39)
2.72(0.85)

4.56(1.40)
2.72(0.85)

4.55(1.39)
2.72(0.85)

4.57(1.40)
2.72(0.85)−84.28∗∗∗(−2.85)−88.45∗∗∗(−3.01)−1.41(−1.50)−3.94(−1.30)−83.97∗∗∗(−2.84)−88.21∗∗∗(−3.00)−1.42(−1.52)−3.98(−1.31)−84.22∗∗∗(−2.85)−88.42∗∗∗(−3.01)−1.40(−1.50)−3.96(−1.30)−83.99∗∗∗(−2.84)−88.23∗∗∗(−3.00)−1.41(−1.51)−3.99(−1.31)

18,755
0.28
Yes
Yes
Yes

18,755
0.28
Yes
Yes
Yes

18,755
0.28
Yes
Yes
Yes

18,755
0.28
Yes
Yes
Yes

Table A7: Regression of the absolute value of the basis on measures of frictions.
Table presents the regression described in section 4.5 using the sample where our demand
concentration measure is available. The dependent variable is the absolute value of the basis.
To make the coefficients directly comparable, we transform the independent variables tomodified z-scores each variable’s full sample median and standard deviation. Regression
includes tenor and date fixed effects and we weight the regression by the square root of
the market’s share of the total daily gross notional. Within R2 reported. t-statistics shownusing robust standard errors clustered by market and date where ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗p < 0.01.
90

(1)Supply Segmentation
Supply HHIkt,t+n × I(Net kt,t+n ≥ 0)
Supply HHIkt,t+n × I(Net kt,t+n < 0)

(2)

(3)

(4)

(5)

18.27∗∗∗(4.55)
17.18∗∗∗(4.87)

Safe Asset Scarcity
Netkt,t+n × I(Net kt,t+n ≥ 0)

8.13∗∗(2.28)
4.29∗∗(2.07)Netkt,t+n × I(Net kt,t+n < 0)
Safe Asset Ratiokt,t+n × I(Net kt,t+n ≥ 0)
Safe Asset Ratiokt,t+n × I(Net kt,t+n < 0)
Broad Asset Ratiokt,t+n × I(Net kt,t+n ≥ 0)

−17.35∗∗(−2.04)−0.60(−1.55)

Broad Asset Ratiokt,t+n × I(Net kt,t+n < 0)−23.29∗(−1.91)−0.36(−1.23)

Demand Concentration
Demand HHIkt,t+n × I(Net kt,t+n ≥ 0)

−0.25(−0.07)−0.57(−0.16)

Demand HHIkt,t+n × I(Net kt,t+n < 0)k

12.19∗∗∗(3.07)

k

3.40(1.58)\HHIt,t+n × I(Net kt,t+n ≥ 0)
Demand\HHIt,t+n × I(Net kt,t+n < 0)DemandN
R2
Tenor FETime FE
Weighted(6)

149,337
0.08
Yes
Yes
Yes

149,337
0.02
Yes
Yes
Yes

149,337
0.00
Yes
Yes
Yes

149,337
0.00
Yes
Yes
Yes

18,755
0.00
Yes
Yes
Yes

149,293
0.04
Yes
Yes
Yes

Table A8: Regression of the absolute value of the basis on marginal cost measures.
Table presents the regression described in section 4.5. The dependent variable is the absolutevalue of the basis. To make the coefficients directly comparable, we transform the independent
variables to modified z-scores each variable’s full sample median and standard deviation.
Regression includes tenor and date fixed effects and we weight the regression by the squareroot of the market’s share of the total daily gross notional. Within R2 reported. t-statistics
shown using robust standard errors clustered by market and date where ∗ p < 0.10, ∗∗p < 0.05, ∗∗∗ p < 0.01.

91