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This paper makes three contributions to our understanding of the price discovery process in currency markets. First, it provides evidence that this process cannot be the familiar one based on adverse selection and customer spreads, since such spreads are inversely related to a trade's likely information content. Second, the paper suggests three potential sources for the pattern of customer spreads, two of which rely on the information structure of the market. Third, the paper suggests an alternative price discovery process for currencies, centered on inventory management strategies in the interdealer market, and provides preliminary evidence for that process.

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PRICE DISCOVERY IN CURRENCY MARKETS

Carol L. Osler, Brandeis University, USA*

Alexander Mende, University of Hannover, Germany

Lukas Menkhoff, University of Hannover, Germany

Abstract

This paper makes three contributions to our understanding of the price discovery process in cur-

rency markets. First, it provides evidence that this process cannot be the familiar one based on adverse

selection and customer spreads, since such spreads are inversely related to a trade's likely information

content. Second, the paper suggests three potential sources for the pattern of customer spreads, two of

which rely on the information structure of the market. Third, the paper suggests an alternative price dis-

covery process for currencies, centered on inventory management strategies in the interdealer market, and

provides preliminary evidence for that process. We suggest more broadly that the price discovery process

will vary with market structure, and that our proposed mechanism may apply to liquid two-tier markets in

general.

March 2007

Corresponding author: Carol Osler, cosler@brandeis.edu or Brandeis International Business School, Brandeis Uni-

versity, Mailstop 32, Waltham, MA 02454, USA. Tel. (781) 736-4826. Fax (781) 736-2269. We are deeply grateful

to the bankers who provided the data and to William Clyde, Pete Eggleston, Keith Henthorn, Valerie Krauss, Peter

Nielsen, Peter Tordo, and other bankers who discussed dealing with us. Special thanks go to Clyde, Eggleston, and

Tordo, for reading and commenting on drafts of the paper. We thank, without implicating, Geir Bjønnes, Alain

Chaboud, Yin-Wong Cheung, Joel Hasbrouck, Thomas Gehrig, Michael Goldstein, Rich Lyons, Albert Menkveld,

Anthony Neuberger, Paolo Pasquariello, Uday Rajan, Stefan Reitz, Dagfinn Rime, Erik Theissen, and Dan Weaver

for insightful comment

PRICE D ISCOVERY IN C URRENCY MARKETS

This paper makes three contributions to our understanding of the price discovery process in cur-

rency markets. First, it provides evidence that this process cannot be the familiar one based on adverse

selection and customer spreads, including the observation that the cross-sectional pattern of customer

spreads is directly contrary to the predictions of that theory. Second, the paper suggests three potential

sources for the pattern of customer spreads, two of which rely on the information structure of the market.

Third, the paper suggests an alternative price discovery process for currencies, centered on inventory

management strategies in the interdealer market, and provides preliminary evidence for that process.

The paper's message is in fact broader than the foreign exchange market. We suggest that the price

discovery process in any market depends on the market's structure. The standard adverse selection proc-

ess assumes a one-tier market, and thus it may not be relevant in markets where dealers can trade with

each other separately, such as FX. Our proposed price discovery process is potentially relevant to all liq-

uid, two-tier markets, including the U.S. Treasury market, the U.S. corporate bond market, and the Lon-

don Stock Exchange. Evidence shows that the cross-sectional pattern of customer spreads in those mar-

kets violates the predictions of adverse selection in much the same way it does in currency markets.

The adverse-selection-based price discovery process asserts that dealers build into their price

quotes the potential information revealed by a given customer transaction. The theory, articulated most

notably in Kyle (1985) and Glosten and Milgrom (1985), highlights the information advantage customers

often have over dealers. Dealers will rationally protect themselves from this adverse selection risk, as

shown in Glosten and Milgrom (1985), by quoting a spread such that both bid and ask prices incorporate

the expected information content of each trade. In this way prices move, on average, in the direction re-

quired by the information coming into the market. In this framework, spreads should be higher when cus-

tomers are more likely to have information, such as when they undertake large trades (Glosten and Mil-

grom (1985), Easley and O'Hara (1987), Glosten (1989)). If dealing is not anonymous, then spreads

should also be higher for specific customers, or customer types, that typically have information.

Most FX microstructure papers draw on adverse selection as their primary interpretive framework.

This originally gained support from Lyons (1995), which shows that trade size and the interbank spreads

of a particular dealer were positively related in 1992. The implicit adoption of adverse selection is clear in

Marsh and O'Rourke (2005), for example, which estimates Easley, Kiefer, and O'Hara's (1996, 1997) ad-

verse-selection-based measure of private information on daily FX customer data. Similarly, Payne (2003)

estimates a VAR decomposition of interdealer trades and quotes and interprets the results, following Has-

brouck (1991), through the lens of adverse selection.

Our evidence indicates, however, that the behavior of customer spreads in FX is inconsistent with

adverse selection. This holds whether we estimate the Huang and Stoll (1997) model or the Madhavan-

Smidt model (1991). Among other violations, we find that customer spreads are widest for the trades least

likely to carry information. More specifically, customer spreads are inversely related to trade size, and are

narrower for the customers that dealers consider most informed. These reportedly informed customers are

financial firms, meaning asset managers such as hedge funds or mutual funds; the other broad category of

customers is commercial customers, meaning firms that import or export.1

If adverse selection doesn't drive customer spreads in FX, what does? The paper's second contribu-

tion is to outline three factors that seem likely to be important. Of necessity, these represent just a subset

of the factors that influence dealers; we focus on these three because practitioners indicate they are par-

ticularly important. The first factor, fixed operating costs, can explain the negative relation between trade

size and customer spreads if some costs are fixed, but cannot explain the cross-sectional variation across

customer types. To explain why FX spreads are larger for commercial than financial customers we sug-

gest that asymmetric information – in the broad sense of information that is held by some but not all mar-

ket participants – may influence spreads through two channels distinct from adverse selection, one involv-

ing market power and a second involving strategic dealing.

2

The market power hypothesis suggests that firms gain market power, even in a market with hun-

dreds of competitors like FX, from holding information. It can be costly for customer firms to search out

the best available quotes in the FX market, so each individual dealer can exert a certain amount of market

power despite the competition. As suggested in Green et al. (2004), dealers may quote the widest spreads

when their market power is greatest, and market power in quote-driven markets depends on knowledge of

current market conditions. In FX, commercial customers typically know far less about market conditions

than financial customers so they might be expected to pay wider spreads, as they do.

The second channel through which asymmetric information might affect customer spreads in FX

involves strategic dealing. Building on abundant evidence that customer order flow carries information

(e.g., Evans and Lyons (2004), Daníelsson et al. (2002)), we argue that rational FX dealers might strategi-

cally vary spreads across customers, subsidizing spreads to informed customers in order to gain informa-

tion which they can then exploit in upcoming interbank trades. In standard adverse-selection models, by

contrast, dealers passively accept the information content of order flow. The idea that dealers strategically

vary spreads to gather information was originally explored in Leach and Madhavan (1992, 1993). When

applied to two-tier markets in Naik et al. (1999) it implies that customer spreads will be narrower for

trades with information, consistent with the pattern in FX.2

The paper's third contribution is to outline a process through which information may become em-

bedded in exchange rates, and more generally to outline a process for price discovery in liquid two-tier

markets. In contrast to adverse selection, which focuses on spreads in the customer market, our suggested

process focuses on dealers' inventory management practices in the interdealer market. The mechanism is

this: After trading with an informed customer, a dealer's information and inventories provide strong in-

centives to place a market order in the interdealer market. An informed-customer buy would thus tend to

trigger market buys in the interdealer market and thus higher interdealer exchange rates. In this way the

information brought to the market by informed customers will generate information-consistent changes in

interdealer prices. By contrast, after trading with an uninformed customer a dealer has only weak incen-

3

tives to place market orders. Thus dealer transactions with uninformed customers may be more likely to

generate liquidity in the interdealer market than to move exchange rates.3

This view of dealer behavior differs critically from that of the "portfolio shifts" model of the FX

market (Evans and Lyons (2002)). In that model, there are three rounds of trading. In the first, dealers

absorb inventory from end-users; in the second round dealers trade with each other; in the third round

dealers sell their inventory to end-users and prices adjust to reflect information. We suggest, by contrast,

that prices begin to reflect information earlier, during interbank trading.

Our view of dealer behavior predicts a number of the key stylized facts in FX microstructure. First,

it predicts the positive relation between interdealer order flow and exchange-rate returns documented in

Lyons (1995), Payne (2003), Evans (2002), Evans and Lyons (2002), and Daníelsson et al. (2002), inter

alia. If the dealer is responding to fundamental information it also predicts that the relation should be sub-

stantially permanent, consistent with evidence presented in Killeen et al. (2006) and Bjønnes et al.

(2005). In addition, our view of dealer behavior predicts the positive relation between exchange rates and

financial order flow documented in Evans and Lyons (2004), Bjønnes et al. (2005), and Marsh and

O'Rourke (2005). Finally, our view predicts that the response of exchange rates to financial order flow

will be substantially permanent, consistent with evidence in Lyons (2001) and Bjønnes and Rime (2005).

We test the two most direct implications of our proposed price discovery mechanism. First, dealers

should be more likely to make outgoing trades after financial-customer trades than after commercial-

customer trades. Second, dealers should be more likely to make outgoing trades after large incoming

trades than after small ones. The evidence provides encouraging support for both implications.

Our analysis, though it concerns the microstructure of trading in currency markets, is also relevant

to the exchange-rate dynamics literature within macroeconomics – which is analogous to the "asset pric-

ing" literature within finance. A critical recent contribution to exchange-rate economics is been the rec-

ognition that order flow is a key determinant of returns (Evans and Lyons 2002). Since this insight is in-

consistent with the traditional "efficient markets" view that has dominated exchange-rate theory since the

1980s, it is particularly important for the evidence to have a solid conceptual foundation. So far, theory

4

has shown that order flow matters in part because it contains information (e.g., Evans and Lyons 2002,

2004), but has not articulated the mechanism through which the information in order flow gets into to the

exchange rate. Our work is designed to fill that gap.

Our data comprise the entire USD/EUR transaction record of a single dealer at a bank in Germany

during four months in 2001. These data have two advantages relative to most other tick-by-tick transac-

tions datasets in FX: (i) they distinguish between financial and commercial transactions, and (ii) they

cover a longer time period.

The rest of the paper has four sections and a conclusion. Section I describes our data. Section II

shows that customer spreads in FX are narrowest for the trades most likely to carry information. Section

III discusses how operating costs, market power, and strategic dealing can explain this pattern. Section IV

presents our interpretation of the price discovery process in currency markets, along with supporting evi-

dence. Section V concludes.

I. FX MARKET STRUCTURE AND DATA

The currency markets make all other markets look tiny. FX trading averages almost $2 trillion per

day (B.I.S. (2004)) – over twenty times daily trading on all NYSE stocks. An active currency trades as

much in a half hour as a high-volume stock on the Paris Bourse trades in a day. About half of this trading

takes place in the interdealer market (B.I.S. (2004)), trading in which is now largely carried out on order-

driven electronic exchanges. The customer market, by contrast, is quote-driven.4 Hundreds of dealers

compete in the euro-dollar market, which accounts for almost a fifth of all transactions (B.I.S. (2004)).

There is no significant retail component to FX trading; virtually all trading is carried out by institutions.

Since currencies are important in commerce as well as finance, however, the institutional customer base

for FX includes non-financial as well as financial firms. (Bjønnes and Rime (2005) provides an excellent

description of the market.) In the foreign exchange market it is accurate to treat the dealers as the only

intraday suppliers of liquidity. Trading in euro-dollar and other major currency pairs is never constrained

by a shortage of a particular bond or stock. Furthermore, during our sample period only dealing banks had

5

access to the interbank market. In consequence, there is no need to consider "latent liquidity" (Chacko et

al. 2006).

Our data comprise the complete USD/EUR transaction record of a bank in Germany over the 87

trading days from 11 July 2001 to 9 November 2001. Though the data technically refer to the overall

bank, they are an accurate reflection of a single dealer's behavior because only one dealer was responsible

for the bank's USD/EUR trading. For each transaction we have the following information: (1) the date and

time;5 (2) the direction (customer buys or sells); (3) the quantity; (4) the transaction price; (5) the type of

counterparty − dealing bank, financial customer, commercial customer, preferred customer; (6) the initia-

tor; and (7) the forward points if applicable.

Though our sample period includes September 11, 2001, that day's events will not distort the re-

sults. The foreign exchange customer market functioned quite smoothly that day, though trading volume

was low. This no doubt stems in part from the wide geographical dispersion of dealers around the world –

indeed, dealers are dispersed even within New York City. The interdealer market also functioned

smoothly that day, due in part to wise planning by the two major electronic exchanges. Both Reuters and

EBS, though based in the United Kingdom, have servers in multiple geographic locations around the

world performing real-time replication of all functions.

We include outright forward trades, adjusted to a spot-comparable basis by the forward points, as

recommended by Lyons (2001). The bank's inventory position is inferred by cumulating successive trans-

actions.6 Following Lyons (1995), we set the daily starting position at zero. This should not introduce

significant distortions since our dealer keeps his inventory quite close to zero, as shown Figure 1. The

dealer's average inventory position is EUR 3.4 million during the trading day and only EUR 1.0 million

at the end of the day. Table I provides basic descriptive statistics.7

A preliminary comparison of our dealer with the large dealers described in the literature is provided

in Table II. Table III provides information on the size distribution of our dealer's transactions. (We

would, of course, prefer to present statistics on spreads themselves; however, such figures cannot be cal-

culated from transactions data.) The small size of our dealer is reflected in his total daily trading value,

6

average transactions per day, average inventory position, and mean absolute price change between trans-

actions.8 Our dealer is comparable in size to a NOK/DEM dealer at the large dealing bank examined in

Bjønnes and Rime (2004). Our bank is probably a reasonably good representative of the average currency

dealing bank because small dealing banks are far more common than large ones (B.I.S. (2002)). Nonethe-

less, big banks dominate currency dealing.

Despite the small size of our bank, our main qualitative conclusions should generalize to the entire

foreign exchange market for at least four reasons. First, the FX market is extremely competitive. Hun-

dreds of banks deal in the major currency pairs and even the largest dealer's market share is only on the

order of 10 percent. In such a market, the behavior of any (successful) dealer should accurately represent

the behavior of all (successful) dealers. Second, surveys of currency dealers reveal that the primary de-

terminant of currency spreads is the conventional level of such spreads (Cheung and Chinn (2001)).

Third, market participants consistently confirm that the pattern we identify is correct.

Finally, our small bank's behavior should be representative because it is broadly consistent with

that of large banks in many well-studied dimensions. The Appendix provides a detailed comparison of our

bank's pricing and inventory management practices with those of large banks analyzed in earlier studies.

This analysis suggests that the following statements about larger dealers are equally true for our dealer:

• The baseline spread for interbank trades is on the order of two pips (or equivalently two ticks) 9

• The baseline spread for customer trades is a few times larger than the spread on interbank trades

• Existing inventories are not statistically related to quoted prices

• The dealer typically brings his inventory back to zero by the end of the trading day

• The dealer tends to bring inventory back to zero in a matter of minutes, a speed that is comparable

with that of futures traders and lightning fast relative to traders in equity and bond markets.

These parallels support the reasonableness of generalizing from this bank to the market.

II. THE CROSS -SECTIONAL PATTERN OF CURRENCY SPREADS

This section evaluates whether adverse selection is likely to drive price discovery in the FX cur-

rency markets by testing its implications for the cross-section of FX customer spreads. We analyze two

7

different models of spreads, the Huang and Stoll model (1997) and the Madhavan and Smidt model

(1991). The behavior of spreads fails to conform to the predictions of adverse selection for both models.

Among the violations of adverse selection, we especially note that FX customer spreads are wider for

small trades than for large trades and that they are wider for commercial customers than for financial cus-

tomers.

A. Preliminary Analysis

Our transactions data cannot be used to calculate direct measures of spreads, since they only indi-

cate one side of each quote. Nonetheless, we can extract indirect measures of spreads from a statistical

analysis of successive price changes. Consider a simple market where everyone pays the same spread and

the spread never changes. If the market price is stable then prices only change if trading moves from the

bid to the ask or vice versa, so the spread equals the price change. Even if the market is volatile, any asso-

ciated distortions should ultimately average to zero if there is no dominant trend.10

We begin our analysis with crude estimates of how price changes are related to trade size and coun-

terparty type. These preliminary estimates are based on the following equation:

tttti DDP

+−+=Δ − )( 1 . (1)

Pt is the price and Δ P t is the price change from period t -1 to t : Δ Pt = P t - P t , measured in pips. Dt is the

direction of trade [Dt = 1 (-1) if the counterparty is a buyer (seller)]. The coefficient

β

should represent

half of the spread. To understand this, note that if Dt is at the ask and Dt-1 is at the bid then Dt - Dt-1 is two.

The price change in this case, however, should just equal the spread. Likewise, if Dt is at the ask and Dt-1

is at the bid then Dt - Dt-1 is negative two and the price change should equal the negative of the spread. If

both transactions are at either the bid or the ask, the price change and Dt - Dt-1 are both zero.

Trade size: Market participants tell us that they in formally divide normal-sized customer transac-

tions into three categories: regular trades, which vary from €1 million to about €25 million; modest

trades; and tiny trades. Though the line between the latter two categories is ambiguous, their treatment

8

can vary substantially: tiny trades are often spread by formula rather than by dealers' discretion, and on

such trades a one percent spread is not considered unreasonable. For estimation purposes we distinguish

the following size ranges: Large trades: {|Qt | ∈ [€1 million, € 25 million)}; medium trades: {|Qt| ∈ [€0.5

million, €1 million)}; and small trades: {|Qt | ∈ (€0, € 0.5 million)}. To capture the influence of trade size

we interact the change-in-direction with dummies for large (Lg ), medium (Md ), and small trades (Sm ).

We follow standard practice and use generalized method of moments (GMM) with Newey-West

correction for heteroskedasticity and autocorrelation (e.g., Yao (1998); Bjønnes and Rime (2005)). Since

our data operates on transaction time and involves trades in which the dealer sets the price, our dependent

variable is the sequence of prices on transactions initiated by customers. We exclude the few transactions

over $25 million because such trades essentially represent a distinct market: customers hire dealers to

manage such trades by breaking them up into smaller interbank transactions.11 Interbank and customer

trades may not strictly be comparable, given the structural differences between quote- and order-driven

markets, so we exclude interbank transactions.

The results of this analysis on 1,640 customer trades, shown below, show a negative relation be-

tween trade size and spreads, inconsistent with adverse selection:

ΔPt = 0.979 + 8.403 ( Dt - Dt-1 ) x Sm + 6.859 ( D t - Dt-1 ) x Md + 3.181 (Dt - Dt-1 ) x Lg Adj R2 = 0.266.

(0.296) (0.503) (1.188) (0.724)

The half-spread is, on average, 8.4 pips for small customer trades, 6.9 pips for medium-sized trades, and

only 3.2 pips for large trades. All coefficients are strongly statistically significant. The Wald test statistic

for the difference between the coefficients on large and medium-sized trades is a highly-significant 7.04,

indicating that spreads are indeed narrower for large trades than for medium-sized trades, on average. The

Wald statistic for the large-small distinction is a similarly-significant 34.87. The Wald statistic for small

versus medium-sized trades is only 1.46, which is not statistically significant. This may reflect a lack of

power due to the paucity of medium-sized trades in our sample (see Table 3). We note in passing that the

constant term is positive and statistically significant. This reflects the incompleteness of our sample of

9

trades: when interdealer trades are included the constant becomes economically small and statistically

insignificant.

Customer Type: To examine the bilateral relationship between spreads and customer type we inter-

act the change-in-direction variable with dummies for trades with financial customers (FC ) and commer-

cial customers (CC ). The results, presented below, indicate that the half-spread is 4.0 pips, on average, for

financial customers and roughly twice as large, 7.9 pips, for commercial customers.

ΔPt = 1.069 + 4.010 ( Dt - Dt-1 ) x FC + 7.878 ( Dt - Dt-1 ) x CC Adj R2 = 0.329.

(0.298) (1.005) (0.454)

As before, the coefficients are strongly statistically significant. The Wald statistic of 12.38 for the differ-

ence between coefficients on financial and commercial trades is highly significant, indicating that finan-

cial customers pay narrower spreads, on average, than commercial customers. Adverse selection predicts

the opposite.

According to our correspondents at large dealing banks, the correct customer disaggregation is be-

tween small commercial customers, on the one hand, and financial customers and large multinational

(commercial) corporations, on the other. Though we cannot technically distinguish large multinationals

from other commercial customers, large multinationals are unlikely to do much business with a small

bank. Thus the counterparty-based tiering identified here should be roughly accurate for our bank.

Trade Size and Counterparty Type: Since commercial trades tend to be smaller than financial

trades, it is possible that the commercial trades' smaller spreads simply reflect their small size. We test

whether counterparty type has an independent influence by interacting the change-in-direction variable

with dummies for both trade size and counterparty type. The results of this analysis, reported in Table IV,

indicate that currency spreads are influenced by counterparty type as well as trade size, at least for small

and medium-sized trades. Commercial spreads are wider larger than financial spreads for both small and

medium-sized trades, though the spreads are roughly the same for large trades. Wald tests confirm the

broad outlines of this pattern though they do not indicate statistical significance for all the pairwise differ-

10

ences across coefficients. This could reflect a lack of power due to the low number of observations for

large commercial-customer trades and for medium-sized trades for both customer types (see Table 3).

B. Structural Analysis

In reality, of course, spreads vary for a number of reasons, so the univariate regression above need

not provide reliable estimates of the influence of adverse selection. For this reason, we estimate the de-

terminants of spreads using two standard structural models, those of Huang and Stoll (1997) and Madha-

van and Smidt (1991). Though each model parameterizes the influence of adverse selection differently,

we find no evidence for that influence.

1. The Huang and Stoll Model

Huang and Stoll (1997) observes that trade size is relatively unimportant for pricing in markets ─

like foreign exchange ─ where large trades are routinely broken up into multiple smaller transactions.

Even in such markets, however, the risk of trading with a better informed counterparty remains. Huang

and Stoll's model analyzes the pricing decision of a representative dealer in a competitive market whose

counterparties have private information that is revealed by their trade direction of (buy or sell). Agents are

fully rational. The model assumes that dealer i's quote is determined by the dealer's expected true value

of the asset,

μ

it, a trade's direction, and the dealer's existing inventory, as follows:

()

ttittitit II

S

D

S

P

υθμ

+−−+= *

22 . (2)

The baseline half-spread ─ meaning the spread that would apply before adjustment for existing

inventories ─ is S /2. Iit is dealer i 's inventory at the beginning of period t ; I*i is his desired inventory,

which we assume to be zero since this is generally the case for FX dealers (Bjønnes and Rime 2005). The

model permits dealers to manage existing inventories by shading prices to customers (e.g., quote lower

prices when his inventory is high), which implies

θ

> 0.

Dealer i updates his expectation of the asset's fundamental value in light of the private information

revealed by the direction of the previous trade as well as public news:

μ

it –

μ

it-1 = (

λ

S/2) Dt-1 +

ε

t. The term

11

λ

S/2 captures the information effect of trade direction and is thus a direct manifestation of adverse selec-

tion. The shock

ε

t is a serially uncorrelated and is assumed to reflect public information. Combining the

pricing and updating rules gives the following expression for price changes between customer transac-

tions:

tittttti eI

S

D

S

DD

S

P+Δ−+−=Δ −− 22

)(

211

θλ

, (3)

where et ≡

ε

t + Δ

υ

t. We follow Huang and Stoll (1997) in estimating separate coefficients for trades in our

various size and customer-type categories, which we achieve by interacting the key right-hand-side vari-

ables with dummies for both transaction size {Lg , Md, Sm } and counterparty type {FC , CC }. We again

use GMM with Newey-West standard errors.

The results, shown in the first two columns of Table V, broadly confirm our two preliminary find-

ings. First, baseline spreads are wider for small trades than for large trades. Second, baseline spreads are

wider for the small and medium-sized trades of commercial customers than for the equivalently-sized

trades of financial customers. As noted above, these results are not implied by adverse selection.

The estimated adverse-selection coefficients,

λ

, also provide no support for the influence of adverse

selection in this market. Four of these are insignificant, and the patterns implied by the two significant

coefficients do not conform to adverse selection. While the theory predicts that

λ

should be larger for

financial customers than commercial customers, the two significant coefficients both apply to commercial

customers. Furthermore, the theory predicts that

λ

rise monotonically with trade size, but the point esti-

mates of

λ

vary non-monotonically with trade size for both commercial and financial customers. If we

instead take statistical significance as our guide, the estimates of

λ

imply that small and medium-sized

commercial trades have wider spreads than large commercial trades, the opposite of what we would ex-

pect under adverse selection.

We note in passing that the results suggest that inventory levels are not relevant to FX customer

spreads: None of the six inventory coefficients is significant at standard significance levels. This pre-

12

sumably reflects the general preference among FX dealers for managing inventory via interbank trades

(Bjønnes and Rime (2005)), rather than shading prices to customers.

As shown in Table V, we test the robustness of these results in three ways. First, we rerun the re-

gressions excluding inventories, which appear to have no influence. Second, we rerun the regressions

using only spot transactions. Forward transactions account for 20 percent of all trades, so their inclusion

could impede direct comparisons with earlier papers, which focus exclusively on spot trades. Finally, we

rerun the regressions including interdealer as well as customer trades. This provides comparability with

Bjønnes and Rime (2001), where customer transactions (as a single category) and interbank transactions

are included in the main regressions. Our results consistently prove robust.

2. Madhavan and Smidt Model

FX dealers consistently report that they consider large customer trades to be more informa-

tive than small ones, so the Huang and Stoll (1997) model's assumption that trade size is unin-

formative may not be valid.12 For this reason, we also estimate the Madhavan and Smidt model

(1991), in which trade size is informative. This is perhaps used more commonly in FX micro-

structure than any other model of spreads (see, for example, Lyons (1995) and Bjønnes and Rime

(2005)).

The Madhavan and Smidt model (1991) model assumes that agent j calls dealer i request-

ing a quote on amount Qjt which is determined as follows: jtitjtjt XPQ +

)(

. The term

μ

jt

represents agent j 's expectation of the asset's true value, conditional on a noisy private signal of

the asset's true value and on a noisy public signal. Xjt represents agent j 's liquidity demand. The

coefficient

ξ

is positive, so demand increases with the gap between the true value and the quoted

price; it is because of this relation that trade quantity conveys information.

Dealer i's regret-free price, Pit , is determined as , where variables are

defined as above. If dealers shade prices to manage existing inventories, ζ < 0. After solving for condi-

tiititit DIIP

χζμ

+−+= )( *

13

tional expectations and taking first differences, one arrives at the following expression for the price

change between incoming transactions, ΔPit = Pit - Pit-1 :

tjtititttit QIIDDP

++= −− 121121 (4)

The intercept,

α

, should be zero if the dealer's desired inventory is zero. If the dealer shades prices in

response to inventories then

γ2 = |γ 1 |/

φ

> |γ 1 | > 0 > γ 1 . Our estimates of the Huang and Stoll model sug-

gest that both γ 1 and γ 2 should be zero.

Adverse selection, if operative, could influence three of the parameters in this model. First, it could

influence the values of

β

2, the coefficient on lagged direction, which according to the model is the nega-

tive of the baseline half-spread. Under adverse selection these would be bigger (in absolute value) for

large trades and for financial trades. This same effect could also be reflected in

δ,

the coefficient on trade

size, Qt : under adverse selection this should be positive. Large trades can reflect a big gap between the

asset's true value and the dealer's quote, so a rational dealer in the model increases the spread with trade

size. Unfortunately, the interpretation of a positive coefficient on trade size is inherently ambiguous, since

it could also reflect an inventory effect noted in Ho and Stoll (1981). Larger trades leave market makers

with higher inventory and thus greater inventory risk, so larger trades should carry wider spreads. Ad-

verse selection and this inventory effect, which we will refer to as a "prospective" inventory effect, are

observationally equivalent here.

Finally, adverse selection should influence the relation between

β

1 and

β

2. The model implies that

β

1 = |

β

2|/

φ

> 0 >

β

2, where 0 <

φ

< 1 is a model-derived parameter that captures the extent to which deal-

ers rely on their priors rather than the current trade in updating their estimate of the currency's true value.

Under adverse selection, estimates of

φ

should be smaller (farther below unity) for large trades and for

financial trades, since dealers consider such trades to be relatively informative.

As before, we estimate the model using generalized method of moments (GMM) with Newey-West

correction for heteroskedasticity and autocorrelation, and interact the key variables with dummies corre-

sponding to trade size and counterparty type. We include the same robustness tests used when estimating

14

the Huang-Stoll model plus one more. The new test excludes the quantity variables but not the inventory

variables. As before, our results consistently prove robust.13

The results of this analysis, presented in Table VI, do provide no more support for adverse selection

in the FX customer market than our earlier results. We begin with the estimated baseline half-spreads. For

financial customers, estimated baseline half-spreads are a statistically significant 6.6 pips on small trades

and roughly half that size – and insignificantly different from zero – for medium and large trades. Wald

tests are not able to detect statistically significant differences within the financial-customer spreads, which

could be due to a lack of power since our bank primarily serves commercial customers. However, Wald

tests indicate that financial-customer spreads are indeed smaller than commercial-customer spreads for

small trades (marginal significance 0.0002) and medium trades (marginal significance 0.03). Wald tests

detect no significant difference between spreads on large trades for financial and commercial customers,

which is not surprising since the spreads themselves were insignificant in this size category for both types

of customers.

The other two potential sources of evidence for adverse selection are equally unsupportive. The

point estimates of the coefficient on trade size, Qt , are positive for both commercial and financial custom-

ers but neither is significant at standard significance levels. Finally, we turn to the ratio between the coef-

ficients on lagged and current direction,

φ

= |

β

2|/

β

1. Though adverse selection predicts these will vary

inversely with trade size, the estimates rise with deal size for the financial customers ─ the customers to

which the theory most likely applies. Similarly, though adverse selection predicts that

φ

is smaller for

financial than commercial customers, the reverse is true for medium-sized and large trades.

The other results from this model are generally unsurprising. The constant term is insignificant, im-

plying that our dealer's preferred inventory level is indeed zero. The coefficients on inventory are signifi-

cant for commercial customers but have signs opposite those predicted by the model. This can be traced

to one particular trade; when that trade is excluded the coefficients become statistically insignificant. The

15

other inventory coefficients are also insignificant, implying that our dealer does not manage inventories

by shading prices to customers consistent, with the results from the Huang and Stoll model.

C. Discussion

This section has provided evidence inconsistent with the hypothesis that price discovery in FX fol-

lows the standard adverse selection model. That is, dealers do not appear to adjust customer spreads to

protect themselves against the trades' likely information content. Indeed, our analysis has shown that the

cross-sectional pattern of FX customer spreads is the opposite of that predicted by adverse selection.

Spreads are widest for the smallest trades and they are wider for the least informed customers, the com-

mercial customers. Market participants at large banks, to whom we sent the paper for a reality check, all

confirmed both this qualitative pattern as well as the specific magnitudes we find for spreads during our

sample period. Indeed, they assert that the pattern just identified approximates common knowledge within

the FX market: the pattern is known by virtually everyone who trades, and virtually everyone who trades

knows that virtually everyone else who trades knows it, etc. Only rank beginners might find the pattern

unfamiliar, they claim.14

We stress that these results apply only to the customer FX market. Existing evidence suggests that

interdealer spreads have a different relation to both counterparty type and trade size. Interdealer spreads

are likely invariant to counterparty type, since the interdealer market is anonymous. The true qualitative

relation between interdealer spreads and trade size is unclear, but it does not seem to be negative. The

earliest study using interbank transaction data, Lyons (1995), finds a positive relationship between trade

size and spreads, and subsequent studies find little or no relationship (Yao (1998), Bjønnes and Rime

(2005)). The absence of any such relationship in recent years presumably reflects the fact that interbank

trades are consistently small, in part because large trades are split into many small trades, so the size of an

interdealer trade is unlikely to carry much information.15

Our results indicate that caution should be applied when interpreting correlation results at lower

frequencies. For example, Lyons (2001) and Marsh and O'Rourke (2005), which both analyze daily data,

16

suggest that the observed negative correlation between commercial order flow and exchange rates might

reflect a negative price impact of commercial trades. If so, our analysis suggests that the price impact is

certainly not instantaneous, since we find that spreads are positive for both commercial and financial cus-

tomers. Indeed, negative spreads do not appear to happen in this market even in the extreme: the authors

have had extensive conversations with many FX dealers, most of whom work at large banks and none of

whom were aware of negative spreads.16 Marsh and O'Rourke (2005) also suggest that the negative rela-

tionship may instead reflect feedback trading, and provide evidence that commercial-customer trades are

indeed influenced by lagged returns. Our evidence provides a further indication that the feedback-trading

interpretation is more likely to be correct.

How do our results compare with results from other markets? Adverse selection has a mixed record

of success in explaining spreads elsewhere. It successfully explains the relation between spreads and trade

size on the NYSE (see, for example, Harris and Hasbrouck (1996); Bernhardt and Hughson (2002); Peter-

son and Sirri (2003)). Not only are NYSE spreads wider for larger trades, but some stock brokers pay for

order flow from retail (uninformed) customers (Easley et al. 1996). The theory also works well in ex-

plaining the pattern of price discrimination among specialists on the non-anonymous Frankfurt Stock Ex-

change. As shown by Theissen (2003), those specialists provide price improvement when adverse selec-

tion costs are lowest and adjust quotes the most after trades that did not get price improvement.

Despite this evidence and the general acceptance of the adverse selection hypothesis in the FX mi-

crostructure literature, there are a number of markets where adverse selection cannot explain the behavior

of spreads. A negative relationship between trade size and spreads has been observed in the U.S. munici-

pal bond market, where spreads average 2.23 percent for small trades and 0.10 percent for large trades

(Harris and Piwowar (2004)). We argue below that this may reflect the lack of anonymity in this market

coupled with asymmetries in bargaining power among different customers, properties that are also shared

with FX. A negative relationship between spreads and trade size has also been documented in the U.S.

corporate bond market (Goldstein et al. (2006)) and the London Stock Exchange (Hansch et al. (1999)).17

17

We argue below that the negative relationship between trade size and spreads in these markets may reflect

their two-tier structure coupled with their high liquidity, properties they share with FX.

III. OPERATING C OSTS , M ARKET P OWER , AND S TRATEGIC D EALING

This section examines possible alternative explanations for the pattern of FX customer spreads

documented in the previous section. We begin by considering the components of the standard paradigm

beyond adverse selection, which are: monopoly power, inventory risk, and operating costs. Pure monop-

oly power is unlikely to be important in FX, where hundreds of dealers compete intensely. Inventory risk

can also be ruled out as a determinant of our pattern, since the prospective inventory effect implies a posi-

tive relationship between spreads and trade size (Ho and Stoll (1981)), rather than the negative relation

we observe. In addition, inventory risk is invariant across customers, so this element cannot explain the

relationship between spreads and customer type.

The remaining component of the standard paradigm is operating costs. In discussing the negative

relationship between spreads and trade size on the London Stock Exchange, Angel (1996) and Hansch et

al. (1999) note that such a relationship could arise if per-unit processing costs are smaller for large trades.

This occurs with fixed costs, which certainly exist in FX and, in conversation, foreign exchange dealers

themselves suggest that they are relevant. However, the relationship between costs and customer type

seems unable to explain the smaller spreads paid by financial customers: fixed costs do not vary strongly

by customer type, and marginal costs are, if anything, higher for asset managers, who often require the

proceeds of a large trade to be "split" among numerous individual funds. In short, the standard paradigm

can explain the relationship between FX customer spreads and trade size but not the relationship between

spreads and customer type.

One might wonder whether the customer-based differences in spreads could result merely from dif-

ferences in the intraday pattern of trading. If commercial customers trade more intensely during hours

when spreads are widest, they will naturally pay larger spreads on average. Interdealer spreads (the only

spreads for which intraday patterns are available) are widest during the London morning and during the

18

FX market's "overnight" period, which lasts for a few hours after about 5 pm London time (Payne

(2003)). Financial-customer trades tend to be concentrated during the London morning hours (Figure

2A,B), while commercial-customer trades are more evenly distributed across the trading day. In conse-

quence, intraday trading patterns predict variation in customer spreads opposite to that just documented.

The rest of this section highlights two mutually consistent theories of dealing under asymmetric in-

formation that might have more success in explaining why FX spreads vary across counterparty types.

One theory suggests that information about current market conditions provides market power which, in

turn, affects spreads. The other theory suggests that dealers strategically vary spreads across customers in

an attempt to gather private information about near-term exchange-rate returns.18 It is our view that both

of these information-based forces operate simultaneously with operating costs.

The additional theories we highlight in this section do not exhaust the long list of factors dealers

consider in setting spreads – though a longer list of theories would doubtless exhaust the patience of our

readers. For example, dealers acknowledge that conforming with standard market practice, presumably in

order to maintain a strong reputation, is the single most important determinant of spreads (Cheung and

Chinn 2001). This does not, however, help us understand the issue on which we focus: why market prac-

tice itself involves wider spreads for smaller trades and for commercial customers. We choose to focus on

the market power and strategic dealing theories based on the view that these theories reveal the factors

most influential in determining this pattern. Our readers in the market have found no reason to complain

about this choice.

A. Market Power

Green et al. (2004) shows that variations in market power between dealers and their customers may

explain why spreads are inversely related to trade size in the U.S. municipal bond market. That paper

points out that dealership markets are opaque due to the dispersion of trading, so current market condi-

tions – meaning real-time mid-quotes, spreads, volatility and the like – are hard to ascertain. The custom-

19

ers who make smaller municipal bond trades tend to know the least about current market conditions, so

they have the least market power relative to the dealers and are charged the widest spreads.

The market-power hypothesis can be applied directly to explain why commercial FX customers pay

wider spreads than financial customers. Currency markets are also dealership markets with dispersed in-

formation. What little market information is available to customers is expensive. Financial customers

typically purchase real-time information and hire professional traders who know how to interpret it. By

contrast, most commercial customers do not purchase that information and do not hire sophisticated trad-

ers, so their traders are usually considered relatively uninformed about market conditions.

Information about market conditions is not the only potential source of financial customers' market

power. In discussing the NYSE, Angel (1996) notes that

a dealer knows that an unsophisticated individual who places a small order may have higher search

costs per share and is not in a good position to monitor the quality of a broker's execution. The

broker has little incentive to spend time negotiating or shopping around for a better deal for a small

order. Thus, a dealer may take advantage of this by quoting a wider market for small orders (p. 4).

Duffie et al. (2004) develops this insight into a formal model and shows that bargaining power in

OTC markets partly reflects the alternatives to trading immediately, alternatives that are determined by

the relative costs and benefits of further search. In currency markets, the benefits to search are smaller for

commercial customers than for financial customers. 19 FX traders at commercial firms are not always re-

warded for finding better prices; for them, trading is typically just one of many administrative responsi-

bilities. By contrast, FX traders at financial customers are often explicitly evaluated on execution quality.

Since FX traders at financial firms perceive greater benefits to search, they are more likely to keep at it

until they find a narrow spread. Knowing this, dealers may not even try to quote them a wide spread. Fi-

nancial customers' market power may also come from their tendency to undertake large trades (see Table

III). As shown in Bernhardt et al. (2004), customers who regularly provide a dealer with substantial

amounts of business may receive better spreads as dealers compete for their business.

20

B. Strategic Dealing

The counterparty-based tiering of currency spreads may also reflect "strategic dealing," in which

the dealers adjust their pricing so as to extract private information from their customers. Order flow at

large banks includes information about upcoming high-frequency currency returns, as documented by

Evans and Lyons (2004) and Daníelsson et al . (2002). Evidence from equity markets confirms that access

to real-time order flow information can provide an informational advantage (Anand and Subramanyam

(2005)). Thus it seems logical that FX dealers might try to capture a larger share of the most informative

order flow, since the information could help increase returns and/or lower risk through better inventory

management, better pricing on upcoming trades, and better speculative positioning.20

Our own small bank's order flow need not be hugely informative for strategic dealing considera-

tions to influence its customer spreads. As noted earlier, dealers' dominant concern when setting FX

spreads is conforming to standard practice (Cheung and Chinn (2001)), so strategic dealing will (at least)

indirectly influence spreads at small banks so long as it directly influences spreads set at large banks.21 It

is also noteworthy that dealers need not know exactly which customers are informed for strategic dealing

to be influential. Strategic dealing can arise even if, as is true in FX, dealers discriminate only according

to a customer's likelihood of being informed.

The insight that market makers might strategically manipulate spreads to increase the information

value of order flow is not new. Leach and Madhavan (1992, 1993) use equity-market inspired models to

demonstrate that market makers may adjust prices early in a trading session to enhance later profitability.

This general insight motivates the empirical tests of Hansch and Neuberger (1997), which "provide[s]

evidence that dealers [on the London Stock Exchange] do act strategically, and that they deliberately ac-

cept losses on some trades in order to make superior revenues on others" (p. 1). Evidence for this type of

strategic dealing in an experimental market which shares many properties with the FX interdealer market

is presented in Flood et al. (1999).

Our evidence, however, concerns cross-sectional variation in spreads rather than variation across

time. An equity-inspired strategic dealing hypothesis that overlaps more substantially with our own is

21

presented in Naik et al. (1999), whose analysis of a two-tier market indicates that customer spreads will

be narrower for more informed customers, consistent with the pattern we document for FX. The motiva-

tion for this conclusion is similar to the first two outlined above: after gleaning the information included

in the current customer trade, dealers can profit more in subsequent trading. However, the Naik et al.

model also concludes that customer spreads vary positively with trade size, while our data fits the oppo-

site pattern.

Consistent with the Naik et al. (1999) model, Reiss and Werner (2004) report that "[d]uring the

period of our sample, London [Stock Exchange] dealers were known to solicit large customer orders,

even if the terms were unfavorable. The explanation most often given for this behavior was that dealers

were 'purchasing' information …" (p. 625). In the context of the FX market we hypothesize that informa-

tion content is not only inversely related to trade size, as on the London Stock Exchange, but it is typi-

cally higher for financial customers than commercial customers. Evidence that financial transactions in

FX carry information for high-frequency exchange-rate returns is provided in Froot and Ramadorai

(2005). Evidence that the information in financial transactions tends to exceed the information in com-

mercial transactions, at least information about high-frequency dynamics, is provided in Fan and Lyons

(2003) and Carpenter and Wang (2003). Related evidence is presented in Ramadorai (2005), whose buy-

side view of the phenomenon supports our sell-side view. His analysis of the FX transactions of a large

set of asset managers finds that spreads are narrower for the managers that produce higher (risk-adjusted)

FX returns.

There is a sense in which the strategic dealing hypothesis mirrors the market power hypothesis. In

the game between dealers and their commercial customers, dealers gain market power from their knowl-

edge of market conditions, on the basis of which they extract wider spreads. In the game between dealers

and their financial customers, both sides are well informed about market conditions but financial custom-

ers also have private information relevant to near-term exchange-rate dynamics. Financial customers view

themselves as exploiting the market power associated with their private information to extract smaller

22

spreads. Dealers simultaneously view themselves as strategically setting small spreads to increase their

business with privately informed customers and learn their information. Both sides are right.

IV. PRICE DISCOVERY IN FOREIGN EXCHANGE

The evidence presented so far shows that spreads in the FX customer market are inversely related

to a deal's information content, the opposite of the pattern predicted by adverse selection. But, if adverse

selection is not the basis for price discovery in currency markets, what is? This section proposes an alter-

native price discovery mechanism, relevant to FX and other liquid two-tier markets, and provides evi-

dence in support of that proposal. Asymmetric information is the centerpiece of our story, as it must be,

but we suggest that information influences inventory management and order choice in the interdealer

market rather than spreads in the customer market. Our proposed m echanism thus reflects institutional

features of the FX market, such as its two-tiered structure and the importance of the interdealer market for

inventory management, that distinguish FX from the simpler market structures assumed in adverse-

selection models.

Our proposed price discovery mechanism differs in a key way from the familiar "portfolio shifts"

model of the FX market articulated in Evans and Lyons (2002). In that model, dealers first absorb inven-

tory from end users, then trade that inventory among themselves, and finally sell the inventory to other

end users. The exchange rate moves to reflect information only during the customer trading of round

three. If one were to graft our price discovery framework to the Evans and Lyons model, however, one

would conclude that the exchange rate moves to reflect information during the interbank trading of round

two. Nonetheless, our proposal creates a coherent picture from disparate stylized facts from FX micro-

structure.

A. The Mechanism

Our proposed price discovery mechanism involves dealers' interbank trading in response to cus-

tomer trades. We focus on the interbank market because the evidence presented above implies that a

given trade's potential information content is not embedded in customer prices. We infer that price dis-

23

covery does not happen in the customer market and must therefore happen in the interdealer market.22

Interdealer markets are crucially important for inventory management in FX (Lyons 1996) as in other

two-tier markets (Manaster and Mann (1996), Reiss and Werner (1998), Lyons (1997)).

Consider a dealer whose inventory rises abruptly in response to an incoming customer call. Since

FX dealers prefer to have zero inventory (this is documented for our dealer in the Appendix and for large

dealers in Bjønnes and Rime (2005)), our dealer will most likely try to offload the new inventory to an-

other dealer. In FX the dealer must choose between "indirect" trading in the order-driven broker market or

"direct" trading in the regular quote-driven market.

Assume for now that our dealer chooses to trade through an interdealer broker, in which case he

must decide whether to submit a market sell or a limit sell. Harris (1998) and Foucault (1999) highlight a

central trade-off: market orders provide immediate execution with certainty while limit orders provide

better prices with uncertain execution. Since FX dealers can identify their customers, this order choice

could depend on the customer providing the inventory (Reiss and Werner (2004)).

Suppose the customer is informed. In this case the dealer has three incentives to exploit the imme-

diacy offered by market orders: He has information, he has inventory with its inherent risk, and his infor-

mation indicates that his inventory could soon bring a loss. Our dealer therefore seems likely to place a

market sell order and earn the lower bid price. Suppose instead the customer is uninformed. In this case

the dealer has only one incentive to place a market order: the inherent riskiness of his inventory. Thus our

dealer might be more likely to place a limit order which, if executed, would earn him the higher offer

price. In short, we suggest that dealers using the brokers market to manage inventory will have a stronger

tendency to place market orders after informed customer trades than after uninformed customer trades.23

The connection to price discovery is direct: brokered interdealer prices will tend to move in the direction

indicated by informed trades.

If our dealer chooses to trade directly, a modified version of this cost-benefit analysis still applies.

Calling another dealer produces a quick, certain trade at a relatively undesirable price, like placing a mar-

ket order; waiting for someone else to call could bring a better price but could instead bring no trade at

24

all, like placing a limit order. Thus, a dealer who chooses the direct interdealer market has strong incen-

tives to call another dealer after trading with an informed customer and may be more likely to wait for

incoming calls after trading with an uninformed customer.

The overall conclusion is consistent regardless of whether a dealer chooses to manage his inventory

via brokered or direct trades. After trades with informed customers, a dealer will be more likely to make a

(parallel) outgoing trade than after trades with uninformed customers. As a result, interdealer prices will

tend to move in the direction required by the information contained in customer trades. (Note that our

discussion of price discovery does not assume a priori that informed dealers place outgoing/market or-

ders, but instead derives that outcome.)24

In equilibrium, trading might cease entirely if (a) customer identity were the only factor determin-

ing whether a dealer makes an outgoing trade and (b) customer identity were a reliable indicator of

whether the customer is informed at a given point in time. Under this combination of circumstances deal-

ers would only place market orders after informed-customer trades, so placing a limit order would be a

recipe for losing money and the market might cease to exist. In reality, however, customer identity is im-

perfectly correlated with a given customer's private information at any point in time. Furthermore, the

decision to make an outgoing trade depends on more than just customer identity, as shown below.25

B. Explaining the Stylized Facts

Our proposed price discovery mechanism predicts a number of the stylized facts in FX microstruc-

ture. For example, it predicts that financial order flow, which dealers assert is relatively informed, will be

positively related to exchange-rate returns. Evidence for this positive relationship is provided in Evans

and Lyons (2004), Bjønnes et al. (2005), and Marsh and O'Rourke (2005). Our analysis also predicts that

this relationship between financial order flow and exchange rates is substantially permanent, evidence for

which is provided in Lyons (2001) and in Bjønnes et al. (2005).

Our proposed price discovery mechanism also predicts a positive and largely permanent relation-

ship between exchange rates and interdealer order flow, which is defined as buy-initiated interdealer

25

transactions minus sell-initiated transactions. (In the order-driven or brokered portion of the interdealer

market, the initiator of a transaction is considered to be the dealer placing the market order; in the quote-

driven or direct dealing portion of that market, the initiator is the dealer that calls out. In both cases the

initiator makes an "outgoing trade.") Consistent with this prediction, substantial evidence indicates a

strong and positive contemporaneous correlation between interdealer order flow and exchange-rate re-

turns at the daily and weekly horizons (see Lyons (1995), Payne (2003), Evans (2002), Evans and Lyons

(2002), Killeen, Lyons, and Moore (2002), and Daníelsson et al. (2003), inter alia ). Furthermore, a sub-

stantial portion of this relationship is permanent (Evans and Lyons (2002), Payne (2003), Killeen et al.

(2005), Bjønnes et al. 2005).

Our proposed price discovery mechanism also answers this natural question regarding the strategic

dealing hypothesis: If dealers subsidize the trades of their informed customers in order to buy information

(in effect), how do the dealers benefit from that information? We answer: they benefit via enhanced inter-

dealer trading. The information permits them to reduce their inventory risk and to profit from anticipated

high-frequency exchange-rate moves.

C. Other Evidence

Our proposed price discovery mechanism has four additional testable implications. First, it predicts

that interdealer prices are the best measure of "the market" at any instant. Abundant institutional evidence

confirms this implication. Most critically, dealers universally base their customer quotes on the inter-

dealer market's current best bid and offer. In a large dealing room, salespeople construct the quote actu-

ally given to a customer from a preliminary quote provided at that moment by the relevant interdealer

trader. Those preliminary quotes are in turn anchored on the best bid and offer in the interdealer market.

In electronic communication networks (e.g., Currenext, FXAll) the connection between interdealer prices

and customer quotes is programmed directly into the pricing algorithm.

Second, our proposed price discovery mechanism predicts that dealers with the most customers

should be best informed and should profit the most from interdealer trading. Concurrent research by Bjøn-

26

nes et al. (2007) supports both implications. Trades by the banks with the most customers are positively

correlated with each other but negatively correlated with trades by small banks, which suggests implicitly

that dealers can be divided into two size categories, big and small. Large-bank (small-bank) trades are

positively (negatively) correlated with returns, indicating that large banks are relatively informed. Like-

wise, when a large dealer is accumulating speculative positions via interdealer market orders, his counter-

parties banks tend to be small, which suggests that other large dealers have information that helps them

avoid picking-off risk.

The most direct testable implications of our proposed price discovery mechanism concern the like-

lihood of outgoing interbank transactions. Under our proposal, dealers should be more likely to place in-

terdealer market orders after trades with financial customers than after trades with commercial customers,

since financial customers are considered more informed. Similarly, dealers will be more likely to place

interdealer market orders after larger trades than after small ones, even after controlling for inventory,

since large trades are considered relatively informative.

We test these last two implications via a probit analysis of the conditional probability that a given

transaction is outgoing in the interbank market:

Prob (Tradet =IBout ) = P(FCt-1 , CCt-1 , 10miot-1 ,|Iit |, Iit2 , |Qjt | ) . (5)

Our hypothesis concerns the first three variables, dummy variables for lagged financial-customer trades,

FC t-1, lagged commercial customer trades, CCt-1 , and a dummy set to one if the previous transaction was

worth €10 million or more, 10miot-1 . Our conjecture suggests that the coefficient on the financial dummy

will be higher than the coefficient on commercial dummy, and the coefficient on 10miot-1 will be positive.

The last three terms in equation (5) capture other factors relevant to the decision to place a market

order. The coefficient on absolute inventory, |Iit |, should be positive since higher inventory brings higher

inventory risk.26 Following Bjønnes and Rime (2005) we include squared inventory, Iit 2 , to capture poten-

tial nonlinearities in this relationship. The absolute size of the current transaction, |Qjt |, is included be-

cause our dealer's customer transactions are often smaller than the $1 million minimum size for brokered

trades. Since our dealer prefers to carry out interbank trades on EBS, a broker, rather than by dealing di-

27

rectly, he seems likely to collect inventory from small customer transactions and then square his position

by submitting one relatively large market order.

The results of estimating Equation (5), shown in Table VII, support our view that the likelihood of

an outgoing interbank transaction is higher when the most recent transaction is considered informed. Out-

going interbank transactions are statistically significantly more likely when the previous transaction in-

volves a financial customer than when it involves a commercial customer. They are also statistically sig-

nificantly more likely after big trades, meaning those over €10 million. The results are economically

meaningful, as well. After a moderate-sized commercial trade the estimated probability of an outgoing

interbank transaction is 9.5 percent; after a similarly-sized financial trade that probability is roughly twice

as large, at 18.5 percent. After commercial trade over €10 million the probability of an outgoing interbank

transaction is 25.4 percent. After a similarly-sized financial trade this probability reaches a lofty 40.2 per-

cent. (In these calculations, all other independent variables are taken at sample means.) As indicated by

the three robustness tests, these results, like our earlier results, are not sensitive to whether inventories are

included as an independent variable or to whether the data include spot trades or interdealer trades.

The rest of the results from estimating Equation (5) also make sense. The likelihood of an outgoing

trade rises with the absolute value of existing inventory and the relationship is concave. As noted above,

the importance of inventory level for dealer order choice helps the market avoid no-trade equilibria and

maintain low interbank spreads by reducing the signal/noise ratio associated with outgoing interbank

trades. Importantly, the significance of inventory levels eliminates one alternative possible explanation for

the influence of trade size on order choice. Specifically, it appears that the influence of large trades in our

regression does not reflect the inventory risk they bring, since the influence of the dealer's inventory level

per se is already accounted for. The positive relationship between absolute trade size and the likelihood

that the trade itself is outgoing indicates that outgoing brokered transactions tend to be larger than the

dealer's average incoming transaction, as expected.27

To summarize: This section proposes a mechanism through which price discovery may occur in

FX. We first note that price discovery must happen in the interdealer market since customer spreads vary

28

inversely with a trade's likely information content. We then show both conceptually and empirically that

dealers are more likely to make outgoing interbank trades after trading with informed customers than after

trading with uninformed customers. This could be the force that drives interdealer prices in the direction

implied by the information customers bring to the market.

V. CONCLUSIONS

This paper's overall message is that the standard adverse selection model of price discovery may

not apply in liquid two-tier markets. Instead, we propose a new price discovery process relevant to such

markets. Our data comprise the complete USD/EUR trading record of a bank in Germany over four

months in 2001. The paper first shows that adverse selection in the customer market cannot be the

mechanism through which price discovery happens in FX. Spreads on normal-sized currency trades vary

inversely with trade size and are wider for commercial customers than for financial customers. Both com-

ponents of this pattern are inconsistent with adverse selection, since FX dealers consider large trades to be

more informative than small trades and financial customers to be more informed than commercial cus-

tomers.

The paper then highlights three hypotheses that help explain the cross-sectional pattern of currency

spreads. We first note that operating costs are largely fixed in FX, which could help explain the negative

relationship between trade size and spreads. The customer-based variation in spreads could be explained

by Green et al. 's (2004) market-power hypothesis. This hypothesis asserts that spreads in quote-driven

markets vary positively with a dealer's market power relative to a given customer, and that such market

power derives in part from knowledge of market conditions. Commercial customers tend to know the

least about current market conditions, so this theory predicts they will pay the widest spreads, as they do.

The customer-based variation in spreads could also reflect dealers' attempts to strategically gather infor-

mation about near-term returns (Leach and Madhavan (1992), (1993), Naik et al. (1999)). Dealers may

subsidize trades with informed customers in order to learn the information embedded in their trades, from

which they hope to profit in subsequent interdealer trades. Dealers consider financial order flow to be

29

relatively informative, so financial customers pay the narrowest spreads. The three hypotheses we high-

light here do not exhaust the long list of factors dealers consider in setting spreads; we focus on these be-

cause we consider them most influential, and leave the others for future research.

The paper finishes by proposing a new price discovery process relevant to liquid two-tier markets

like FX. This proposal creates a coherent picture of the FX price discovery process by fusing existing

empirical evidence on FX microstructure, including our own, with insights from mainstream microstruc-

ture. We first note that, since customers' information is not immediately reflected in the prices they pay,

price discovery must take place entirely in the interdealer market. We focus our analysis, therefore, on

dealer behavior in the interdealer market, a market that is important for inventory management (Lyons

1997). The key mechanism behind our suggested price discovery process involves the dealer's response to

individual customer trades. We suggest that after transactions with informed customers, dealers will tend

to make parallel outgoing interdealer trades − placing a market order in the order-driven component of the

market, for example − motivated by their inventory as well as by their newly-acquired information. In this

way the information from customer trades will be reflected in interdealer prices. After transactions with

uninformed customers, by contrast, dealers will be relatively likely to place parallel limit orders or to wait

for incoming calls.

Our proposed mechanism implies that dealers should be more likely to place outgoing interdealer

trades after informed customer trades, and we provide evidence that this is true for our dealer. Our theory

also predicts some key stylized facts in FX: the positive and substantially permanent relation between

cumulative interdealer order flow and exchange rates, as well as the positive and substantially permanent

relation between financial order flow and exchange rates.

Customer spreads are known to vary inversely with trade size, as in FX, in the U.S. Treasury Mar-

ket, the U.S. corporate bond markets, and the London Stock Exchange. Our proposed price discovery

mechanism may thus apply in these markets as well as FX, since the mechanism relies solely on the exis-

30

tence of two tiers and high liquidity. In future research it would be valuable to test the relevance of our

proposed price discovery process in these other markets.

31

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35

Appendix: Small Banks and Large Banks Behave Similarly

This Appendix documents that our small-bank dealer behaves very similarly to large-bank dealers

in terms of pricing and inventory management. The analysis is based on the Madhavan-Shmidt model

outlined in Section II, with customers aggregated into one category for comparability with earlier studies.

Baseline spreads: As shown in Table AI, our bank's average baseline half-spread for interbank

transactions is about 1.5 pips, which is similar to estimates from other studies. For example, Goodhart et

al. (2002) finds that the average spread for USD/EUR transactions on the Electronic Brokerage Service

(EBS, one of the two major electronic brokerage systems for interbank trading) was 2.8 pips about one

year after the euro was introduced. Our bank's average half-spread for customer trades, 9.2 pips, is much

higher than its average interdealer spread of 1.6 pips. Customers are also quoted sharply higher spreads

than other dealers by Bjønnes and Rime's (2001) NOK/DEM dealer. These figures imply that currency

spreads average less than 0.1 percent; for comparison, average municipal bond spreads were 180 basis

points in 2003 (Harris and Piwowar (2004)) and average spreads on the London Stock Exchange were

110 basis points in 1991 (Reiss and Werner (2004)).

Influence of existing inventories: Our results indicate that existing inventories have no influence on

the prices our dealer quotes to other dealers, consistent with recent studies of large banks (Yao (1998),

Bjønnes and Rime (2005)). Survey-based evidence confirms that inventories are of minimal importance

when dealers set spreads, and that the dominant concern is whether spreads conform to market convention

(Cheung and Chinn (2001)). Lyons (1995) provides evidence that his dealer did engage in inventory-

based price shading towards other dealers in 1992. This may reflect the unusual character of Lyons' dealer

who, as a jobber, dealt exclusively with other dealers at extremely high frequency. Yao (1998) claims that

his dealer avoided such shading because it would reveal information about his inventory position.

Bjønnes and Rime (2005) argue that any shift away from inventory-based price shading in recent

years may reflect the interbank market's rapid shift to a heavy reliance on electronic brokerages after their

introduction in the mid-1990s (Melvin and Wen (2003)). Our dealer reports that for interbank trades he

generally uses EBS because it is less expensive and faster than direct interbank dealing.28 Together, these

36

observations imply that our dealer controls inventories via interbank trading instead of price shading, a

conclusion we support empirically later in this section. Studies from other markets also show that dealers

in two-tier markets with access to brokerage services prefer to manage their inventory through interdealer

transactions (Reiss and Werner (1998)).

The estimates in Table AI seem to provide slight evidence of inventory-based price shading in the

"wrong" direction with respect to transactions with customers. Reassuringly, this can be traced to one

trade carried out in the first month of our sample period. If that trade is excluded, the coefficients on in-

ventory are insignificant.

Trade size and spreads: The coefficient on trade size is statistically insignificant for interbank

trades, suggesting that neither information asymmetries nor prospective inventories cause large interbank

trades to be priced less attractively than small ones. This is consistent with the large dealing bank exam-

ined in Bjønnes and Rime (2004), for which spreads on brokered interbank transactions seem independent

of trade size. That paper also finds that spreads rise with trade size for direct interbank transactions, a

distinction that makes economic sense. Dealers have limited control over the relationship between trade

size and spread for brokered transactions, but they have full control for direct trades. Notably, the earliest

studies of currency dealers (Lyons (1995), Yao (1998)), which did not control for the distinction between

direct and brokered trades, found that interbank spreads do rise with trade size, consistent with standard

models. This could reflect the fact that interbank trading was mostly carried out through direct transac-

tions until the late 1990s.

The coefficient on trade size is also insignificant for customers in our baseline regression. Note that

this coefficient is negative and significant when inventories are excluded: Section II showed that the

overall relationship between spreads and trade size is indeed negative for customer transactions.

2. Inventory Management

Our dealer's tendency to keep inventories close to zero (Figure 1) is itself similar to inventory man-

agement practices at large banks. As Table I shows, currency dealers of all sizes tend to keep minimal

37

inventories. A more rigorous description of our dealer's approach to inventory management comes from

estimating the following regression:

I

t - I t-1 =

ω

+

ρ

It-1 +

ε

t. (A1)

If the dealer instantly eliminates unwanted inventories, then

ρ

≈ -1. If the dealer allows his inventory to

change randomly, then

ρ

= 0. The time subscript corresponds to transaction time, and only incoming

transactions, for which our dealer quotes the price, are included (giving 2,858 observations). Results from

estimating Equation (A1), once again using GMM with Newey-West standard errors, confirm that our

small bank strives to keep inventories close to zero. Our point estimate of

ρ

= -0.20 has a standard error of

0.008 and is thus highly statistically significant. The dealer on average eliminates 20 percent of an inven-

tory shock in the next trade, which implies a median inventory half-life of 19 minutes.

Our estimated inventory half-life is quite close to the 18-minute median inventory half-life for

Bjønnes and Rime's (2004) NOK/DEM dealer. The speed of adjustment is faster in futures markets,

where dealers eliminate almost half of any inventory shock in the next trade (Manaster and Mann 1994).

Adjustment speeds are also faster of the large DEM/USD dealers at the bank studied by Bjønnes and

Rime, for which inventory half-lives range from 0.7 to 3.7 minutes. Nonetheless, our dealer's adjustment

speed is lightning fast, and differs little from the others just reported, when compared with inventory ad-

justment lags in other markets. On the NYSE these lags average over a week (Madhavan and Smidt

(1993)) and can extend beyond a month (Hasbrouck and Sofianos (1993)). Even on the London Stock

Exchange, which is a dealership market like FX, inventory half-lives average 2.5 trading days (Hansch et

al. (1998)).

Overall, this analysis shows that the dealer from which we take our data behaves much like large

dealers despite his small volume.

38

39

Table I. Descriptive statistics, currency dealing at a small bank in Germany

The table shows the complete USD/EUR trading activity of a small bank in Germany, except preferred

customer trades, over the 87 trading days between July 11th , 2001 and November 9th , 2001.

Customer

All

Transactions Interbank

All Financial Commercial

Number of Transactions

(percent)

3,609

(100)

1,919

(44)

1,690

(56)

171

(5)

1,519

(42)

Of Which, Forward 646 114 532 60 472

Value of trades (€ mil.)

(percent)

4,335

(100)

2,726

(61)

1,609

(39)

405

(9)

1,204

(28)

Of Which, Forward 999 87 912 226 686

Mean Size (€ mil.) 1.20 1.42 0.95 2.37 0.79

Mean Size, Forwards (€

mil.)

1.55 0.76 1.71 3.77 1.45

40

The table shows the complete USD/EUR trading activity of a small bank in Germany, except preferred customer trades, over the 87 trading days be-

tween July 11th , 2001 and November 9th , 2001. For comparison purposes we focus on statistics based exclusively on the small bank's spot trades.

Table II. Comparison of small bank studied here with larger banks studied in other papers.

Bjønnes and Rime (2005)

Small Bank in

Germany B.I.S. (2002)

per Bank Lyons

(1995) Yao (1998) Four Dealers,

Range DEM/USD

Dealer NOK/DEM

Dealer

87 Trading

Days in 2001a April 2001

5 Trading

Days in

1992

25 Trading

Days in

1995

5 Trading Days in 1998

Transactions per

Day 40 (51) --- 267 181 58 - 198 198 58

Transaction value

per Day (in $

millions) 39 (52) 50 - 150 1,200 1,529 142 - 443 443 270

Value per

Transaction ($

mil.) 1.0 --- 4.5 8.4 1.6 - 4.6 2.2 4.6

Customer Share of

Transaction

value (in

percent)

23 (39) 33 0 14 0 – 18 3 18

Average Inventory

Level (in € or $

millions) 3.4 11.3 11.0 1.3 – 8.6 4.2 8.6

Average

Transaction Size

(in € or $

millions)

1.2 3.8 9.3 1.5 – 3.7 1.8 3.7

Average Price

Change Btwn.

Transactions (in

pips)

11 3 5 5 - 12 5 12

a Values in parentheses refer to the data set including outright-forward transactions.

Table III. Size distribution of individual trades

The table shows the size distribution of all USD/EUR spot and forward transactions, except those for pre-

ferred customers, at a small bank in Germany over the period July 11, 2001 through November 9, 2001.

Interbank

Trades Financial

Customer Trades Commercial

Customer Trades

Number 1,872 171 1,492

Share (%)

Below € 0.1 million 7% 15% 54%

€ 0.1 – 0.5 million 9 26 32

€ 0.5 – 1.0 million 7 14 5

€ 1.0 – 20 million 77 44 8

€ 20 million and above 0 1 1

41

Table IV: We estimate this equation: tttti DDP

Δ− )( 1 . The dependent variable is the

change in price between two successive customer trades measured in pips. D t is an indicator variable

picking up the direction of the deal: D t is +1 for buy-initiated trades and –1 for sell-initiated trades. The

change-in-direction variable is interacted with dummy variables for two customer types, financial cus-

tomers (FC ) and commercial customers (CC ), and with dummies for three trade size categories, large

trades (Lg ), meaning those worth $1 million or more; medium trades (Md ), meaning those worth

$500,000 to $1 million, and small trades (Sm ), meaning those smaller than $500,000. Data include all

incoming customer USD/EUR spot and forward trades of a small bank in Germany, except those with

preferred customers, during the period July 11, 2001, through November 9, 2001. Estimation uses GMM

and Newey-West correction. Significance at the 1, 5 and 10 percent levels indicated by ‡, † and *, respec-

tively.

Coefficient Standard

Error

Constant 0.894‡ 0.296

FC x Sm x Δ Dt 5.619‡ 2.420

CC x Sm x Δ Dt 8.618‡ 0.512

FC x Md x Δ Dt 2.821† 1.414

CC x Md x Δ Dt 9.967‡ 1.458

FC x Lg x Δ Dt 3.365‡ 1.100

CC x Lg x Δ Dt 3.060‡ 0.929

Adj. R2 0.271

No. Obs. 1,640

42

Table V. (Modified) Huang and Stoll (1997) model

We estimate this model: tittttit eI

S

D

S

DD

S

P+−+−= −−

ΔθλΔ

22

)(

211 .

ΔPit is the change in price between two successive customer trades measured in pips. Dt is +1 for buy-initiated trades

and –1 for sell-initiated trades. Iit is the dealer's inventory, measured in EUR millions. These variables are interacted

with dummy variables for trades with financial customers (FC ) and trades with commercial customers (CC ). They

are also interacted with dummies for trade size: Lg . = {|Qjt | ∈ [1,∞ )}; Md . = {|Qjt | ∈ [0.5,1)}; Sm . = {|Qjt | ∈ (0,0.5)}.

Data include all incoming USD/EUR spot and forward trades of a small bank in Germany, except those with pre-

ferred customers, over the period July 11, 2001, through November 9, 2001. Estimation uses GMM and Newey-

West correction. Significance at 1, 5 and 10 percent levels indicated by ‡, † and *, respectively. Constant term sup-

pressed. Estimates of the baseline half spread are highlighted in bold.

Baseline Regression Robustness 1:

No Inventories Robustness 2:

Spot Trades

Only

Robustness 3:

Interbank Trades

Included

Coefficient Std. Error Coefficient Coefficient Coefficient

Half-Spread, S /2

S/2 x FC x Sm . 10.538‡ 2.55 10.606‡ 7.807‡ 9.304‡

S/2 x FC x Md . 5.354† 2.39 4.125 2.763 4.918†

S/2 x FC x Lg. 4.202† 1.94 4.214† 0.998 1.597

S/2 x CC x Sm . 13.478‡ 0.59 13.436‡ 11.346‡ 12.805‡

S/2 x CC x Md . 11.621‡ 2.74 12.298‡ 13.561‡ 12.963‡

S/2 x CC x Lg. 3.804† 1.65 3.480† 6.505† 4.478‡

S/2 x IB x Sm. + Md. 0.817

S/2 x IB x Lg. 3.934‡

Adverse Selection

λ x FC x Sm . 0.319 0.21 0.333* 0.529* 0.391†

λ x FC x Md . 0.457 0.52 0.330 -0,395 0.802*

λ x FC x Lg. 0.266 0.57 0.346 -3.360 1.965

λ x CC x Sm . 0.056† 0.02 0.048† 0.197‡ 0.101‡

λ x CC x Md . 0.393† 0.18 0.426‡ 0.614‡ 0.348†

λ x CC x Lg. 0.513 0.46 0.534 0.489 0.364

λ x IB x Sm.+ Md . -2.729

λ x IB x Lg. 0.717‡

Inventory

θ x FC x Sm . 0.038 0.18 0.116 0.18

θ x FC x Md . -0.512 0.42 -1.315 0.42

θ x FC x Lg. 0.003 0.05 0.152 0.05

θ x CC x Sm . -0.078* 0.04 -0.002 0.04

θ x CC x Md . 0.081 0.27 -0.003 0.27

θ x CC x Lg. -0.011 0.02 -0.017 0.02

θ x IB x Sm +Md. 4.814

θ x IB x Lg. -0.077

Adjusted R 2 0.33 0.33 0.35 0.23

Observations 1,651 1,651 1,129 2,859

43

Table VI. Spread variation across trade sizes and counterparty types

We estimate this equation: ΔPit =

α

+

β

1D t +

β

2D t -1 +

γ

1I it +

γ

2I it -1 +

δ

Qjt + ε t .

The dependent variable is the change in price between two successive incoming trades, measured in pips. Dt is an

indicator variable picking up the direction of the deal, positive for purchases (at the ask) and negative for sales (at

the bid); Iit is the dealer's inventory at time t, and Qjt is order flow measured in millions of euros. These variables are

interacted with dummy variables for financial customers (FC ) and commercial customers (CC ). They are also inter-

acted with dummies for trade size: Lg . = {Qjt ∈ [1,∞ )}; Md . = {Qjt ∈ [0.5,1)}; Sm . = {Qjt ∈ (0,0.5)}. Data include all

incoming customer USD/EUR spot and forward trades of a small bank in Germany, except those with preferred

customers, over the period July 11, 2001, through November 9, 2001. Estimation uses GMM and Newey-West cor-

rection. Significance at 1, 5 and 10 percent levels indicated by ‡, † and *, respectively. Estimates of the (negative of

the) baseline half spread are highlighted in bold.

Robustness Tests

Baseline Regression No

Inventory No

Quantity Spot

Trades

Only

Interbank

Trades

Included

Coeff. Std. Error Coeff. Coeff. Coeff. Coeff.

Constant 0.031 0.32 0.159 -0.047 0.718* -0.597†

Direction

FC x Sm x Dt .

FC x Sm x Dt-1

10.456‡

-6.615‡ 2.58

2.39 10.419‡

-6.935‡ 7.880‡

-3.230 12.924‡

-13.236‡ 9.034‡

-5.420‡

FC x Md. x Dt

FC x Md. x Dt-1

3.921

-2.972 2.69

2.99 3.905

-2.930 3.161

-2.174 5.574

-4.679 3.364

-0.895

FC x Lg. x Dt

FC x Lg. x Dt-1

2.397

-3.622* 2.93

2.02 2.788

-3.100 5.117 ‡

-1.308 4.013

-0.065 -0.164

0.343

CC x Sm. x Dt

CC x Sm. x Dt-1

13.329‡

-12.681‡ 0.61

0.64 13.327‡

-12.729‡ 11.766‡

-10.138‡ 11.403‡

-11.100‡ 12.934‡

-11.469‡

CC x Md. x Dt

CC x Md. x Dt-1

12.618‡

-7.199‡ 1.56

1.86 12.473‡

-7.161‡ 12.914‡

-7.267‡ 13.945‡

-5.607‡ 14.570‡

-8.492‡

CC x Lg. x Dt

CC x Lg. x Dt-1

4.682†

-2.064 2.31

1.76 4.721†

-1.715 5.759‡

-4.010‡ 1.010

0.001 6.296‡

-3.189†

IB x Md.+Sm.x Dt

IB x Md.+Sm.x Dt-1

2.027

-3.757†

IB x Lg. x Dt

IB x Lg. x Dt-1

3.450‡

-1.122†

Inventory

FC x Iit

FC x Iit-1

-0.464

0.365

0.59

0.60 0.049

-0.135

-0.234

0.169

1.119

-1.180

CC x Iit

CC x Iit-1

1.052†

-1.087‡

0.41

0.42 0.144

-0.143*

0.029

-0.036

1.012†

-1.097‡

IB x Iit

IB x Iit-1

-0.263

0.198

Trade size

FC x Qjt 0.121 0.73 0.435 -0.263 1.597

CC x Qjt 0.773* 0.47 -0.240 0.311 0.522

IB x Qjt -0.347

Adjusted R 2 0.33 0.33 0.34 0.32 0.24

Observations 1,640 1,640 1,640 1,125 2,848

44

Table VII. Probit regression of choice of outgoing interbank trades

We estimate this equation, Prob (Tradet=IBout ) = P(FCt-1 , CC t-1 , |Iit |, Iit2 , |Qjt | ), as a probit regression.

Incoming (outgoing) interbank trades are coded 0 (1). FCt-1 is a dummy coded 1 if the previous counter-

party was a financial customer, CCt-1 and IBt-1 are defined similarly for commercial customers and other

banks. I represents inventories, in millions of euros; |Qjt | represents the absolute size of the current deal,

measured in EUR millions; 10 miot-1 is a dummy set to one if the size of the previous transaction was €10

million or larger. Significance at the 1, 5 and 10 percent levels indicated by ‡, † and *, respectively.

Robustness Tests

Baseline Regression Spot Trades

Only Interbank

Trades In-

cluded

Coefficient Std. Error z-Statistic Coefficient Coefficient

FCt-1 -0.116 0.116 -1.00 -0.091 -0.256*

CCt-1 -0.531‡ 0.055 -9.60 -0.409‡ -0.672‡

IBt-1 -0.214‡

10 miot-1 0.650‡ 0.190 3.43 0.770‡ 0.657‡

|Iit | 0.030‡ 0.011 2.85 0.051‡ 0.028‡

Iit2 -0.001‡ 0.000 -2.64 -0.002‡ -0.001†

|Qjt | 0.029‡ 0.008 3.58 0.070‡ 0.028‡

Constant -0.875‡ 0.044 -19.92 -0.893‡ -0.728‡

McFadden's R 2 0.041 0.044 0.044

Observations 3,534 2,894 3,534

45

Figure 1. Overall inventory position (EUR millions)

Plot shows the evolution of a currency dealer's inventory position in EUR millions over the period July 11, 2001

through November 9, 2001. Data come from a small bank in Germany and include all USD/EUR spot and forward

trades. The horizontal axis is transaction-time. Vertical lines indicate the end of each calendar week.

-60

-40

-20

0

20

40

60

80

Time

EUR

46

Figure 2: Intraday distribution of trades

The charts below show the average number of trades during each five-minute period of the trading day.

Data come from a small bank in Germany and include all USD/EUR spot and forward trades during four

months in 2001.

Figure 2A: Financial-customer trades

0

0.02

0.04

0.06

0.08

0.1

0.12

1

Time of Day

Number of Trades

8 am 9 am 10 am 11 am 12 pm 1 pm 2 pm 3 pm 4 pm 5 pm

Figure 2B: Commercial-customer trades

0

0.1

0.2

0.3

0.4

1Time of Day

Number of Trades

8 am 9 am 10 am 11 am 12 pm 1 pm 2 pm 3 pm 4 pm 5 pm

47

Table AI. Baseline Madhavan-Smidt model

We estimate this equation: ΔPit =

α

+

β

1D t +

β

2D t -1 +

γ

1I it +

γ

2I it -1 +

δ

Qjt + ε t .

The dependent variable is the change in price between two successive incoming trades measured in pips. Qjt is order

flow measured in EUR millions, Iit is the dealer's inventory at time t, and Dt is an indicator variable picking up the

direction of the trade, positive for purchases (at the ask) and negative for sales (at the bid). These variables are inter-

acted with dummy variables for the two counterparty groups, other dealers (IB for "interbank") and all customers

(CU ). Data include all incoming customer USD/EUR spot and forward trades of a small bank in Germany, except

those with preferred customers, over the period July 11, 2001 through November 9, 2001. Estimation uses GMM

and Newey-West correction. Significance at the 1, 5 and 10 percent levels indicated by ‡, † and *, respectively.

Numbers in bold can be interpreted as the (negative of the) baseline half-spread.

Robustness Tests

Baseline Regression

No Invento-

ries Spot Trades

Only

Interbank

Trades

Excluded

Coefficient Std. Error Coefficient Coefficient Coefficient

Constant -0.590† 0.23 -0.426* -0.383 0.070

Direction

CU X Dt

CU X Dt-1

11.467‡

-9.206‡ 0.50

0.45 11.327‡

-9.186‡ 10.988‡

-8.864‡ 11.548‡

-10.025‡

IB X Dt

IB X Dt-1

2.817‡

-1.579‡ 0.69

0.48 2.753‡

-1.555‡ 0.706

-1.025†

Inventory

CU X Iit

CU X Iit-1

1.125‡

-1.264‡

0.38

0.38 -0.064

-0.046

0.855†

-0.974†

IB X Iit

IB X Iit-1

-0.259

0.133

0.35

0.35 -0.191

0.187

Trade size

CU X Qjt 0.126 0.39 -1.001‡ -0.840‡ -0.001

IB X Qjt -0.152 0.40 0.055 0.590

Adjusted R 2 0.23 0.23 0.23 0.32

Observations 2,848 2,848 2,212 1,640

48

NOTES

1 Our definition of a "customer" follows the market definition as any counterparty that is not another dealer.

2 Buy-side evidence for strategic dealing of this sort is provided in concurrent work by Ramadorai (2006).

3 We show in Section IV that a similar analysis applies if the dealer uses direct trades to unwind his inventory.

4 Electronic brokerages were not introduced until the early 1990s, so their dominance dates only from the late 1990s.

5 The time stamp indicates the time of data entry and not the moment of trade execution, which will differ slightly.

Nevertheless, there is no allocation problem because all trades are entered in a strict chronological order.

6 Inventory calculations are based on all trades for all tests, including those in which our statistical analysis is re-

stricted to subsets of the data.

7 We exclude trades with "preferred customers", typically commercial customers with multi-dimensional

relationships with the bank, because these customers' spreads may reflect cross-selling arra ngements and because

their trades are typically very small (average size EUR 0.18 million). We also exclude a few trades with tiny

volumes (less than EUR 1,000) or with apparent typographical errors.

8 The large mean absolute change in transaction price between successive trades, 10.7 pips, presumably reflects the

relative infrequency of transactions at our bank as well as the high proportion of small commercial customer trades,

which tend to have wide spreads (as we document below).

9 A pip is equivalent to a tick: one unit of the smallest significant digit in an exchange rate as conventionally quoted.

In the euro-dollar market, where the exchange rate averaged $1.1128/€ during our sample period; a one-pip change

from that level would bring the rate to $1.1129/€. In this market one pip is approximately one basis point, since the

exchange rate is near unity.

10 It is also not possible to estimate spreads from matched pairs of trades. This technique is commonly used in ana-

lyzing bond markets (e.g., Goldstein et al. (2006), Green et al. (2004)), where trades can be identified by the amount

traded, as in FX, and also by the particular bond.

11 Fewer than ten of the customer trades in our sample exceeded $25 million. These trades were not excluded when

calculating inventory levels.

12 Trade size is more likely to be irrelevant in the interdealer FX market, where trades are almost always either $1 or

$2 million.

13 One might naturally wonder about collinearity among our instruments. We are confident that this is not a problem,

since the only pair of instruments with non-trivial correlation is Fin x Qt and Fin x LG x Dt , and the qualitative con-

clusions from our baseline analysis are sustained when quantity variables are excluded.

14 The market participants that checked our paper cautioned, however, that the magnitude of spreads on commercial

trades has changed since 2001, even though the qualitative pattern identified here survives. In particular, intensified

competition since 2001 associated with FXAll and other electronic communication networks has brought a compres-

sion in spreads to commercial customers.

15 According to market participants, interbank trades on the electronic brokerages that now dominate that market are

almost always $1, $2, $3, or $5 million.

16 Negative spreads, or the equivalent, are sometimes observed, such as the U.S. treasury market during the late

1980s.

17 Spreads for BBB-rated corporate bonds average $2.37 per $100 face value for trades involving ten bonds or less

but only $0.37 per $100 face value for trades involving over 1,000 bonds (Goldstein et al. (2006)). On the London

Stock Exchange, average quoted spreads range from 165 basis points for the smallest stocks to 112 basis points for

the largest stocks (Hansch et al. (1999): similar results are provided in Bernhardt et al. (2004)).

18 Huang and Stoll (1997) propose yet another explanation for the negative relationship between adverse selection

costs and transaction size in their analysis of equity market spreads. We pass over this explanation since it relies on

the special properties of block trades. We exclude all trades over $25 million from our regression analyses, so this

explanation cannot explain our results. Further, the management of large trades is carried out quite differently in FX

than in equity markets.

49

19 As interpreted here, asymmetric information has two roles in the Duffie et al. (2004) model. First, dis-

persed/asymmetric information about current prices generates the need to search in OTC markets. Second, informa-

tion asymmetries determine the agency relationships within customer firms, between management and their traders,

that in turn determine whether execution is rewarded.

20 Strategic dealing may be more relevant in FX than the municipal or corporate bond markets, since most such

bonds trade relatively infrequently so the information value of any trade may be negligible.

21 This pre-occupation with standard practice may bring to mind the issues of collusion on the NASDAQ raised in

Christie and Schultz (1994). However, since there are literally hundreds of dealers in the major currency pairs, and

they are spread across the globe, it seems highly unlikely that collusion could maintain FX spreads for decades.

22 We are not the first to note that some price discovery happens in the interdealer market (Evans and Lyons 2006),

but to our knowledge we are the first to note that price discovery cannot happen in the customer market, and that

therefore all price discovery must happen in the interdealer market.

23 The choice between limit and market orders will also hinge on market conditions, such as the width of the bid-ask

spread and the depth of the book (Biais et al. (1995), Goettler et al. (2005), Lo and Sapp (2005)).

24 Our conclusion that dealers will place outgoing/market orders after trading with "informed" customers is consis-

tent with the finding of Bloomfield et al. (2005) that informed traders "take (provide) liquidity when the value of

their information is high (low)." In their experimental setting information is most valuable when it is new. In FX

markets, information is newest right after a dealer trades with an informed customer, which corresponds to the time

we suggest the dealer will place the outgoing/market order.

25 Though it would be ideal to develop a formal model of this price discovery mechanism, space constraints preclude

presenting a fully articulated model in this paper. Indeed, the influence of information on order choice has only be-

gun to be analyzed theoretically (Kaniel and Liu (2004)), in part because such models are of necessity extremely

complex. These complexities will multiply when information is incorporated into a two-tier market structure.

26 A more general framework would replace |Iit | with | Iit - I* t |, the gap between actual and desired inventory. However,

currency dealers' desired inventory is usually zero.

27 These inventory management practices are consistent with practices at large banks (Bjønnes and Rime (2004)).

Further extensive parallels between our bank's behavior and that of large banks are documented in the Appendix.

28 This preference is supported by the transactions data. Our dealer's mean interbank transaction size was only €1.42

million (Table 1), the maximum interbank trade size was only € 16 million, and the standard deviation of these trade

sizes was only €1.42. These small values are consistent with heavy use of EBS, where the mean USD/EUR transac-

tion size in August 1999 was €1.94 million and the standard deviation of (absolute) transaction sizes was €1.63 mil-

lion. By contrast, interbank trades averaged closer to $4 million prior to the emergence of electronic brokerages

(Lyons (1995)).

50

... Other FX risk factors include macro-variables like global 5 This vast literature on FX order flow includes, for example, Payne (2003), , Evans and Lyons (2008), Breedon and Vitale (2010), Evans (2010), Menkhoff and Schmeling (2010), Rime, Sarno, and Sojli (2010), and Mancini, Ranaldo, and Wrampelmeyer (2013). 6 For instance, some previous papers using a single interdealer trading platform are, for example, Moore and Payne (2011), Chaboud et al. (2014), and Breedon et al. (2018, while studies based on customers' order flow for a specific bank include, for example, Evans and Lyons (2006), Carpenter and Wang (2007), Breedon and Vitale (2010), Cerrato, Sarantis, and Saunders (2011), Osler, Mende, and Menkhoff (2011), Breedon and Ranaldo (2013, and Menkhoff et al. (2016). 7 For instance, customer trading seems to have a greater price impact than interbank trading does (e.g., Rime, 2000, 2005), and depending on their leverage, financial institutions have a different market impact in different currency markets (Lyons, 2006). ...

... As before, we estimate Eq. (4.2) on the full sample and construct a pairwise F-test, where we test whether all the coefficients in Eq. (4.5) for a particular agent category i C tCO, FD, NB, BAu are jointly significantly different in currency pair k compared with currency pair q. 30 The main result that emerges from this analysis is that corporates, funds, nonbank financials, and banks acting as price takers have a permanent price impact α j m , which varies heavily across currencies. Overall, our empirical analysis extends earlier research on customer order flows (e.g., Evans and Lyons, 2006;Osler, Mende, and Menkhoff, 2011;Menkhoff et al., 2016) at a global scale. An avenue for future research would be to understand the effect of regulation on the local nature of FX price discovery. ...

• Angelo Ranaldo
• Fabricius Somogyi

This work studies the information content of trades in the world's largest over-the-counter (OTC) market, the foreign exchange (FX) market. It analyzes a novel, comprehensive order flow data set, distinguishing among different groups of market participants and covering a large cross-section of currency pairs. We find compelling evidence of heterogeneous superior information across agents, time, and currency pairs, consistent with the asymmetric information theory and OTC market fragmentation. A trading strategy based on the permanent price impact, capturing asymmetric information risk, generates high returns even after accounting for risk, transaction cost, and other common risk factors shown in the FX literature.

... The intuition is that banks, by servicing their customers, collect dispersed information from the market which they put to a good use for their own trades. This view is supported also by the empirical evidence that spreads are narrower for financial customers and for larger trades (Osler et al. 2011). This stylized fact is inconsistent with adverse selection models, according to which market makers should charge larger spreads to the most informed traders and on larger trades, which are more likely to be originated from informed counterparts. ...

... The opportunity of profits for dealers arises because FX is a two-tier market and they may use the information gathered with customers in the first tier to profit from interdealer trades in the second tier. For instance, the results of Osler et al. (2011) show that dealers are more likely to trade aggressively on the interdealer market after trades with informed counterparts. ...

• Leonardo Bargigli

I introduce an optimizing monopolistic market maker in an otherwise standard setting à la Brock and Hommes (J Econ Dyn Control 22(8–9):1235–1274, 1998) (BH98). The market maker sets the price of a zero-yielding asset taking advantage of her knowledge of speculators' demand, manages her inventory of the asset and eventually earns profits from trading. The resulting dynamic behavior is qualitatively identical to the one described in BH98, showing that the results of the latter are independent from the institutional framework of the market. At the same time, I show that the market maker has conflicting effects. She acts as a stabilizer when she allows for market imbalances, while she acts as a destabilizer when she manages aggressively her inventories and when she trades, especially if she acts as fundamentalist or if she is a strong extrapolator. Indeed the more stable institutional framework is one in which the market makers are inventory neutral and doesn't trade but, even in this case, the typical complex behavior of BH98 occurs.

... The tick-by-tick change in average bid-ask spread serves as a proxy for volatility as bid-ask spread in the current period reflects dealers' expectations of current volatility (Chari 2007). The use of bid-ask spread as a proxy for volatility is based on economic literature supporting a positive association between spread and volatility (Bollerslev and Melvin 1994;Hartmann 1999;Galati 2000;Bjønnes and Rime 2005;Osler et al. 2011). The seminal work of Bollerslev and Melvin (1994) emphasizes the link between bid-ask spreads and volatility. ...

... To understand this, we need to consider the information access to market participants. Given that select market participants will have access to private information, dealers try to guard themselves against this information asymmetry by raising the spreads, so that spreads are inversely related to information content (Bjønnes and Rime 2005;Osler et al. 2011). Bid-ask spreads reflect reaction of dealers to adverse selection problem, informational risk and inventory risk (Naranjo and Nimalendran 2000;Pasquariello 2007;Chari 2007). ...

• Smita Roy Trivedi

Central banks seek to guide the foreign exchange market through interventions. However, the success of the central bank in guiding the forex markets, much like the biblical Moses, depends on the differing perceptions and resulting bid–ask spreads of market participants following intervention. Using high-frequency data, we study the behaviour of exchange rate volatility (as reflected in change in bid–ask spreads) following intervention by Reserve Bank of India, India's central bank. We find that intervention increases the probability of volatility being in higher ranges. Event-wise analysis shows an increase in volatility in shorter time frames and a decrease in volatility over the longer time frame of the day, following intervention. Full-text, view-only version at https://rdcu.be/btXd0

... King et al. (2013) summarises unique behaviours of interdealer spreads in comparison to other markets. Dealers do not adjust their quotes to reflect changes in inventory (Bjonnes and Rime, 2005;Osler et al., 2011), and do not quote wider spreads to their informed customers (Osler et al., 2011) so that they can profit from their informed trades. Mancini et al. (2013) show FX liquidity has commonality across currencies with equity and bond markets. ...

... King et al. (2013) summarises unique behaviours of interdealer spreads in comparison to other markets. Dealers do not adjust their quotes to reflect changes in inventory (Bjonnes and Rime, 2005;Osler et al., 2011), and do not quote wider spreads to their informed customers (Osler et al., 2011) so that they can profit from their informed trades. Mancini et al. (2013) show FX liquidity has commonality across currencies with equity and bond markets. ...

... There are a few studies on price discovery in foreign exchange markets (Batten and Hogan, 2001;D'Souza, 2007;Osler et al., 2011;Chaboud et al., 2020) however, the majority of studies on price discovery focus on the U.S. equity markets. Hasbrouck (1995) and Harris et al. (1995) study the price discovery of U.S. stocks cross-listed on the NYSE and regional exchanges and find that the NYSE leads the price formation process. ...

We investigate information shares in the price discovery process in the euro-area sovereign bond market across the yield curve, during both calm and crisis periods. We employ a rich high-frequency dataset from the MTS platform. We find that price discovery is enhanced, on average, especially for periphery countries during the European sovereign debt crisis however, increases in information shares are not uniform across the yield curve. We further show that no particular market leads the price formation process across all maturity segments. We find a clear improvement in market quality for core countries (Germany and the Netherlands) but mixed results for periphery countries (Italy and Spain) in the crisis period.

... [28] [29] [30] [31] [32] are some of the major studies that have analysed the foreign exchange markets in mature and emerging economies. [33] has studied the price discovery process in the currency markets by incorporating new information into the exchange rate dynamics. For the European markets, [34] has studied the impact of macroeconomic news and central bank announcements on the exchange rates. ...

... confirm this expectation for U.S. equity market.Osler et al. (2011) also take the constant term of the adverse selection component of the spread to be zero for different types of investors in currency markets. These findings imply that the constant terms z 0 and z 1 in our model should are expected to equal zero. However, one might also expect z 0 and z 1 to be significantly different from zero, as argu ...

• Murat Tiniç

This thesis investigates how information asymmetry affects asset prices in Borsa İstanbul. In the first chapter, we introduce the R package InfoTrad that estimates the probability of informed trading. Next, we examine the relationship between information asymmetry and stock returns in Borsa İstanbul. Firm-level cross-sectional regressions indicate an economically insignificant relationship between PIN and future returns. Moreover, univariate and multivariate portfolio analyses show that portfolios of stocks with high levels of informed trading do not realize significant return premiums. Consequently, our results, suggest that information asymmetry is a firm-specific risk and it can be eliminated with portfolio diversification. Finally, we compare the informational (dis)advantage of foreign investors trading in Borsa İstanbul. We first show that an average foreign trade creates buy pressure whereas an average local trade generates a sell pressure. The permanent impact of foreign investors over and above local investors is significant only for 24 stocks which correspond to 7% of our sample. Importantly, we show that the foreign price impact occurs primarily in a period of political instability which started with the Gezi Park protests in June 2013. In a panel setting, we also show that adverse selection cost due to foreign trading significantly increases even when we control for firm-specific factors along with global and local macroeconomic conditions. Domestic investors with undiversified portfolios may be more risk-averse during periods of increased turmoil. This may enable foreign investors to have a better position to take advantage of potential price misalignments, especially for stocks of commercial banks.

... This analysis builds on several areas of prior research, including: microstructure models of FX trading, empirical exchange-rate models using trading flows, and the large macro-based literature on exchange rate modeling. Earlier FX microstructure models, such as Lyons (1997), Evans and Lyons (2002), Osler et al. (2011) and Evans (2011), consider trading in a two-tier market. The FX dealers working at major banks trade with each other in the inner tier, while trades between dealers and end-users take place in the outer tier. ...

• Martin D.D. Evans

This paper examines how trading in the FX market carries the information that drives movements in currency prices over minutes, days and weeks; and how those movements are connected to interest rates. The paper first presents a model of FX trading in a Limit Order Book (LOB) that identifies how information from outside the market is reflected in FX prices and trading patterns. I then empirically examine this transmission process with the aid of a structural VAR estimated on 13 years of LOB trading data for the EURUSD, the world's most heavily traded currency pair. The VAR estimates reveal several new findings: First, they show that shocks from outside the LOB affect FX prices through both a liquidity and information channel; and that the importance of these channels varies according to the source of the shock. Liquidity effects on FX prices are temporary, lasting between two and ten minutes, while information effects of shocks on prices are permanent. Second, the contemporaneous correlation between price changes and order flows varies across shocks. Some shocks produce a positive correlation (as in standard trading models), while others produce a negative correlation. Third, the model estimates imply that intraday variations in FX prices are overwhelmingly driven by one type of shock, it accounts for 87% of hour-by-hour changes in the FX prices. The second part of the paper examines the connection between the shocks in the trading model and the macroeconomy. For this purpose, I use the VAR estimates to decompose intraday FX price changes and order flows into separate components driven by different shocks. I then aggregate these components into daily and weekly series. I find that one component of daily order flow is strongly correlated with changes in the long-term interest differentials between US and EUR rates. This suggests that the intraday shocks driving this order flow component carry news about future short-term interest rates which is embedded into FX prices. I find that intraday shocks carrying interest-rate information account for on average 56% of the variance in the daily EURUSD depreciation rate between 2003 and 2015, but their variance contributions before 2007 and after 2011 are over 80%. These findings indicate that the EURUSD depreciation rate is relatively well-connected to macro fundamentals via a particular component of order flow. Finally, I show that flows embedding liquidity risk have forecasting power for daily and weekly EURUSD depreciation rates.

• Smita Roy Trivedi

Do market participants look into the past whilst framing the trading decisions today? Microstructure theory shows that participants increase bid‐ask spreads in response to macroeconomic news to account for asymmetric information and inventory costs. However, the role of perceptions or biases of the market participants in influencing the bid‐ask spread has been largely overlooked. This paper incorporates the role of 'Recurrence bias' in influencing trading decisions and therefore market volatility. Empirically, the model is tested using high‐frequency exchange rate data and time‐stamped news data on Indian rupee, which has seen sharp volatility in the period under study. Probit and GARCH analysis shows that volatility is positively linked to 'Recurrence Bias', which arises from the 'availability of mental models' to traders, created by past response of exchange rate to unexpected component of news.

This paper examines the price discovery process in a two‐tier market, specifically the foreign‐exchange market. The goal is to identify the sources of private information and to gain insights into the process through which that information influences the market price. Using a transactions database that includes trading‐party identities, we show that sustained post‐trade returns rise with bank size, implying that larger banks have an information advantage. The larger banks exploit this information advantage in placing limit orders as well as market orders. We also show that the bank's private information does not come from their corporate or government customers or from some asset managers. Instead, the bank's private information appears to come from other asset managers, including hedge funds, and from the bank's own analysis.

• Dagfinn Rime

The foreign exchange market can be divided in two segments: the interbank market and the customer market. Two advances in trading technology, electronic brokers in the interbank market and internet trading for customers, have significantly changed the structure of the foreign exchange market. In this chapter, we explain the functioning of electronic brokers and internet trading and discuss the economic consequences.

• Thomas Gehrig
• Lukas Menkhoff

This paper provides questionnaire evidence on the role of flow analysis for professional traders and fund managers. This evidence suggests that besides fundamental information and technical analysis, the analysis of flows provides an independent third type of information for professionals. The view that flows can be used to learn about fundamentals is not consistent with the data. Instead, evidence indicates that flows more likely provide insight into semi-fundamental private information.

We study dealer behavior in the foreign exchange spot market using detailed observations on all the transactions of four interbank dealers. There is strong support for an information effect in incoming trades. The direction of trade is most important, but we also find that the information effect increases with trade size in direct bilateral trades. All four dealers control their inventory intensively. Inventory control is not, however, manifested through a dealer's own prices in contrast to findings by Lyons (J. Financial Econ. 39(1995) 321). Furthermore, we document differences in trading styles, especially how they actually control their inventories.

• William G Christie
• PH SCHULTZ

The NASDAQ multiple dealer market is designed to produce narrow bid-ask spreads through the competition for order flow among individual dealers. However, we find that odd-eighth quotes are virtually nonexistent for 70 of 100 actively traded NASDAQ securities, including Apple Computer and Lotus Development. The lack of odd-eighth quotes cannot be explained by the negotiation hypothesis of Harris (1991), trading activity, or other variables thought to impact spreads. This result implies that the inside spread for a large number of NASDAQ stocks is at least $0.25 and raises the question of whether NASDAQ dealers implicitly collude to maintain wide spreads.

• David Easley
• Maureen O'Hara

This paper investigates the effect of trade size on security prices. We show that trade size introduces an adverse selection problem into security trading because, given that they wish to trade, informed traders perfer to trade larger amounts at any given price. As a result, market makers' pricing strategies must also depend on trade size, with large trades being made at less favorable prices. Our model provides one explanation for the price effect of block trades and demonstrates that both the size and the sequence of trades matter in determining the price-trade size relationship.

• Larry Harris

This book is about trading, the people who trade securities and contracts, the marketplaces where they trade, and the rules that govern it. Readers will learn about investors, brokers, dealers, arbitrageurs, retail traders, day traders, rogue traders, and gamblers; exchanges, boards of trade, dealer networks, ECNs (electronic communications networks), crossing markets, and pink sheets. Also covered in this text are single price auctions, open outcry auctions, and brokered markets limit orders, market orders, and stop orders. Finally, the author covers the areas of program trades, block trades, and short trades, price priority, time precedence, public order precedence, and display precedence, insider trading, scalping, and bluffing, and investing, speculating, and gambling.

• Dagfinn Rime
• A. Transparency
• Voice Brokers
• Customer Trading

The foreign exchange market can be divided in two segments: the interbank market and the customer market. Two advances in trading technology, electronic brokers in the interbank market and internet trading for customers, have significantly changed the structure of the foreign exchange market. In this chapter, we explain the functioning of electronic brokers and internet trading and discuss the economic consequences. ? 2003, Elsevier Science (USA).

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Source: https://www.researchgate.net/publication/5091126_Price_Discovery_in_Currency_Markets

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