informed trade in spot foreign exchange markets

8/6/2019 Informed Trade in Spot Foreign Exchange Markets

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Journal of International Economics 61 (2003) 307329

www.elsevier.com/locate/econbase

Informed trade in spot foreign exchange markets: anempirical investigation

*Richard Payne

Department of Accounting and Finance and Financial Markets Group,

London School of Economics, Houghton Street, London WC2A 2AE, UK

Received 31 August 2002; received in revised form 18 September 2002; accepted 5 December 2002

Abstract

This paper presents new evidence on information asymmetries in inter-dealer FX

markets. We employ a new USD/DEM data set covering the activities of multiple dealers

over one trading week. We utilise and extend the VAR structure introduced in Hasbrouck [J.

Finance 46(1) (1991) 179] to quantify the permanent effects of trades on quotes and show

that asymmetric information accounts for around 60% of average bid-ask spreads. Further,

40% of all permanent price variation is shown to be due to transaction-related information.

Finally, we uncover strong time-of-day effects in the information carried by trades that are

related to the supply of liquidity to D2000-2; at times when liquidity supply is high,

individual trades have small permanent effects on quotes but the proportion of permanent

quote variation explained by overall trading activity is relatively high. In periods of low

liquidity supply the converse is trueindividual trades have large permanent price effects

but aggregate trading activity contributes little to permanent quote evolution.

2003 Elsevier B.V. All rights reserved.

Keywords: Exchange rates; Market microstructure; Asymmetric information

JEL classification: C22; F31; G15

1. Introduction

Prior to the 1990s, analysis of the causes of exchange rate movements was a

*Tel.: 144-20-7955-7893; fax: 144-20-7242-1006.

E-mail address: [email protected](R. Payne).

0022-1996/03/ $ see front matter 2003 Elsevier B.V. All rights reserved.

doi:10.1016/S0022-1996(03)00003-5
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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308 R. Payne / Journal of International Economics 61 (2003) 307329

field that was firmly in the hands of macroeconomists. Exchange rate models,

based on the goods and asset market approaches were set out and tested using low

frequency (e.g. monthly or quarterly) data on exchange rates and macroeconomic

fundamentals. However, these tests most often revealed that the fundamentals wereless important for exchange rate determination than models predicted. The

explanatory power of macroeconomic data for exchange rates was poor and the

forecasting power of regressions based on fundamentals was less good than that of

a simple random walk. A classic reference along these lines is Meese and Rogoff

(1983).

This failure has led, in the last decade or so, to increasing attention being paid to

models of FX market activity and exchange rate determination based on market

microstructure analysis. This large and growing literature places the process by

which currencies are actually exchanged in centre stage and focusses on the impact

of heterogeneities in the trading population for prices and traded quantities.A key source of heterogeneity in standard microstructure finance is information-

alsome agents are assumed to be better informed about future asset prices than

others (Glosten and Milgrom, 1985). On an empirical level, such informational

asymmetries have several important implications. First, faced with the possibility

of trading with a better-informed individual, uninformed liquidity suppliers widen

the bid-ask spreads that they charge. This allows them to recoup the losses

inflicted upon them by insiders from uninformed individuals. Second, and more

importantly in the current context, transaction activity carries information and thus

trades permanently alter prices. Episodes in which aggressive traders tend to be

buying a given currency will lead to its price rising while the converse is true

during episodes of aggressive sales. This second prediction is vitalit opens a

channel through which transaction activity in FX markets might play a role in

exchange rate determination, a feature that is entirely absent from standard

macroeconomic exchange rate models. The current study seeks to assess the

importance of this channel.

Recent empirical papers that also focus on the explanatory power of currency

trading activity for exchange rate changes include Lyons (1995) and Yao (1998).

Both of these studies use data from single FX dealers to demonstrate that spreads

contain an asymmetric information component. Lyons (1996) extends his priorwork by examining the role of time in the relationship between trades and quotes.

He finds that trades occurring in periods when the market is active convey less

information than those consummated when the market is quiet. This is interpreted

as consistent with his hot potato hypothesis by which high interdealer volumes

are generated more by inventory rebalancing than exploitation of private in-

formation. Most recently, studies by Evans and Lyons (2001) and Evans (2001)

provide strong evidence for an information content to inter-dealer FX order flow

using 4 months of data on direct (i.e. non-brokered) FX trading activity.

Theoretical models that focus on the information contained in inter-dealer spot

FX trading activity can be found in Lyons (1995) and Perraudin and Vitale (1996).


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R. Payne / Journal of International Economics 61 (2003) 307329 309

In these models dealers receive private signals of future exchange rate evolution1, 2

from their customer (i.e. non-dealer) order flow. On an institutional level, this is

possible as customerdealer trade is entirely opaque (i.e. dealer B cannot observe

the customer order flow arriving at dealer A and vice versa). The customer orderflow arriving at dealers may be informative for a number of reasons. First, and

most blatantly, a given dealer might have an intervening central bank as a

customer and thus may learn about future interest rates from the central banks

order. However, such occurrences are likely to be rare and hence this channel is3

not ideal for arguing that customer orders contain information in general. A

second rationale for the information content of customer order flow comes from

arguing that information regarding future exchange rate fundamentals, for example

trade balances, is dispersed among individual customers. A given dealer observes

the trading behaviour of a group of individual customers and, thus, from their

aggregate trading activity receives a signal regarding future fundamentals whichcan be exploited in inter-dealer trade.

Note that in both of the prior examples, customer order flow is informative

about exchange rate fundamentalsi.e. interest rates or trade balances. Another

class of models, see Evans and Lyons (2001), generates customer trading activity

that forecasts future prices because it is informative about risk premia. These

portfolio shifts or portfolio balance models generate permanent price shifts due

to risk-aversion. Assume that the non-dealer segment of the market experiences a

portfolio shift that requires currency trade. Further assume that there is no

expectation that this trade will be reversed such that the aggregate inventory

imbalance (and risk) foisted upon the market is permanent and undiversifiable.

Assuming risk averse agents operating in the FX markets then delivers a

permanent change in prices due to a permanent change in risk premia. Dealers are

informed in this setting as they see a signal of the aggregate portfolio shift through

1Throughout this paper we will repeatedly refer to trades, volumes and order flow. Volume has its

usual definition as the sum of all Dollar quantities traded in a given interval. However, each individual

trade may be given a direction according to whether the aggressor (the agent demanding liquidity) is a

buyer or a seller. Order flow is the difference between buyer-initiated and seller-initiated tradingactivity. It can be measured in Dollar terms (i.e. as the volume of buyer-initiated activity less the

volume of seller-initiated) or simply in terms of the number of trades (i.e. as the number of buyer- less

seller-initiated trades).2Another interesting recent paper by Chakrabarti (2000) explores the possibility that dealers also

learn from the quotes of other dealers. Whilst this is interesting, it is not possible to empirically

evaluate this proposition with our data and thus we leave it to one side.3Peiers (1997) examines USD/DEM dealer quoting around Bundesbank intervention events, finding

that Deutsche Bank is a price leader at these times. Given that Bundesbank intervention operations are

carried out via Deutsche Bank, these results lend credence to the notion that observing central bank

trades conveys price-relevant information. Corroborating evidence is provided by Naranjo and

Nimalendran (2000), who demonstrate that the adverse selection component of FX spreads increases

around central bank intervention events.


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their customer order flow, before the size of the shift becomes public. Again,

dealers can exploit their signals in trade in the inter-dealer market.

As stated above, for the current paper the key empirical implication of the

preceding models is that trading activity in the inter-dealer market will have apermanent affect on interdealer quotes. This permanent effect is generated by the

information carried by inter-dealer trades (regardless of whether this information is

about future fundamentals or future risk premia). We test for this permanent price

effect using the bivariate VAR model for quote revisions and signed trades

introduced in Hasbrouck (1991a). Further, whilst earlier studies have demonstrated

that at least some FX trades carry information, none has computed the aggregate

impact of such information. Use of the variance decomposition presented in

Hasbrouck (1991b) allows us to calculate the proportion of all information

entering the quotation process via order flow and hence address this issue. Finally,

we examine variations in the information content of trades with the pace of themarket. Theoretical contributions such as Admati and Pfleiderer (1988) and Foster

and Viswanathan (1993) predict correlations in the intra-day variation of transac-

tions costs, volume and the intensity of informed trade which we evaluate via a

time-of-day subsample analysis of the D2000-2 data. We refine the time-of-day

analysis by modifying the basic VAR structure to allow for dependence of the

parameters on D2000-2 liquidity measures.

One of the main innovations of this work is the use of a new data set on

inter-dealer USD/DEM trades, drawn from an electronic brokerage called D2000-

2. Whilst earlier work in this area has employed data based on the operations of

single dealers, the D2000-2 data reflect the interactions of multiple traders. As

such, these data provide broader coverage of inter-dealer activity. Further, the

D2000-2 data can be used to construct proxies for the liquidity of the FX market

as a whole. D2000-2 operates as a closed, electronic order book and every limit

and market order entered onto the system are available from the data. Liquidity

and depth measures can be constructed from the limit order data and used as

conditioning variables in the analysis of the effects of private information.

Our main results are as follows. First, our estimates imply that over 60% of the

D2000-2 spread can be thought of as compensation for informed trade. An

unexpected market buy, for example, leads to an upwards equilibrium quoterevision of 1 pip (i.e. DM 0.0001) on average. The 60% figure derives from

comparing this 1 pip permanent price impact to one half of the average bid-ask

spread on D2000-2. Further, we estimate that around 40% of all information

entering the quotation process does so through order flow, a figure which is

comparable in magnitude to equivalent measures from equity market studies.

The 40% number quoted above is important in that it quantifies the relative

impact of trade-related versus non-trade-related information on exchange rate

evolution. However, given that the data we study here cover only one of several

venues for inter-dealer FX trade we should clarify its interpretation. Our result

does not imply that 40% of the information relevant to long-run USD/ DEM


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determination is revealed on D2000-2. The number is derived from the correlation

between D2000-2 order flow and permanent USD/DEM movements. If we were to

assume, quite reasonably, that all inter-dealer FX venues shared the same

permanent price component, and further that all order flows were perfectlycorrelated, then we would get the same value for the size of the trade correlated

component from each venue. Thus in this case 40% would be a market-wide

figure. We would suggest that, in the current context, the size of the trade

correlated component should be interpreted along similar lines. However, there is

the possibility that informed FX trade is more prevalent on other (less transparent)

trading venues. In this case, our 40% number might be interpreted as a lower

bound on the impact of private information in FX markets.

Finally, impact of informed trade on D2000-2 quotes is shown to depend

strongly on the level of market activity. When the D2000-2 order book is relatively

thin and volume low (i.e. from the late GMT afternoon to the early GMTmorning,) an unexpected trade has a much larger permanent effect on quotes than

in peak trading periods. This result is consistent with the empirical analysis of

Lyons (1996) and the theoretical predictions of Admati and Pfleiderer (1988). A

more direct examination of the relationship between informed trade and market

conditions is obtained from some extended VAR results. These demonstrate that

the permanent impact of informed trade on quotes and the supply of limit orders to

D2000-2 are negatively and non-linearly related. However, it should be noted that

while we show that individual trades have larger permanent price effects in periods

of lower trading activity or liquidity supply, the variance decompositions tell us

that the aggregate contribution of transaction activity to permanent exchange rate

determination is low at these times. Thus these periods should not be considered

those in which price discovery is particularly intensive.

The rest of the paper is set out as follows. Section 2 introduces the basic

features of the D2000-2 data set and Section 3 details the empirical methodology

employed in the current study. Section 4 presents the empirical findings from the

VAR estimations. Finally, Section 5 concludes and presents ideas for further work.

2. The dealing system and the data

Around 80% of all spot FX trade is inter-dealer, with the remainder consisting

of trade between dealers and non-financial customers or dealers and non-dealer

financial customers. Until fairly recently, all inter-dealer trade, both direct and

brokered, was carried out over the telephone. Dealers would call one another to

request price information and consummate trades or, alternatively, might call

human brokers, also known as voice brokers, to express their trading interests. One

implication of this was that, aside from the triennial BIS surveys of FX market

activity, no consolidated source of FX trade information existed. Further, the order

flow information available to dealers themselves was limited. Indicative quote


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information was available via a number of screen based systems and reports of

voice brokered trades were broadcast over intercom systems, but there were no

indications of market wide order flow.

In the early 1990s, however, a shift away from telephone based trade occurredwith the introduction of several electronic trading systems. Reuters opened a

network called D2000-1, facilitating direct, bilateral inter-dealer trade. Evans

(2001) and Evans and Lyons (2001) analyse transactions data from this network.

A further two electronic broking systems came into beingthat run by the EBS

consortium and the Reuters D2000-2 system. These systems have grown quickly,

driving the voice brokered portion of trade down considerably. In June 1997 EBS

claimed to handle 37% of all brokered trade in London, with the D2000-2 market4

share commonly assumed to be similar. Based on this figure and on BIS survey

information, one might estimate that the share of spot inter-dealer USD/DEM

volume passing through D2000-2 in 1997 was around 10%. The data used in thisstudy cover all USD/DEM trade on D2000-2 over the week 6th10th October

1997. Around 30,000 transactions occurred during this time with total volume

approaching $60bn.

2.1. The D2000-2 dealing system

D2000-2 is an electronic order driven system. Liquidity is supplied to the

system via limit buy and sell orders and is drained from the system in two ways;

first, through market buy and sell orders and direct crossing of limit orders and,5second, through voluntary cancellation of limit orders. Transaction consummation

is governed by rules of price and time priority subject to one proviso. Participants

in the system must bilaterally negotiate credit lines if they wish to trade. Thus

trades sometimes execute outside the inside spread due to lack of credit lines

between limit and market order submitters.

A subscriber to D2000-2 sees the following items on the trading display. First

there is an indication of the most competitive limit buy and sell prices in the

system along with the quantities available at those prices. No information on

subsidiary limit orders (i.e. buy/sell orders with prices below/above the extant

best price) is displayed. The screen also indicates the last transactions whichoccurred in a given currency pair, detailing price and volume. The above

information is simultaneously available for multiple exchange rates. From our

4At the current time, that is mid-2002, the market shares of D2000-2 and EBS are certainly not

similar. Reuters has the greater market share in Sterling-related exchange rates but EBS appears to have

achieved market dominance in most others, including the key EuroDollar rate.5By direct crossing of limit orders we mean the situation in which the D2000-2 order book contains

a limit buy with price greater than or equal to that of a limit sell. In this case the system automatically

transacts the overlapping quantity. Thus, we can treat the entry of limit orders that result in crosses as

similar to market order entries.


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USD/DEM data set, one can reconstruct all of the information available to

subscribers. Additionally, the data set contains information on every limit order

entered on D2000-2.

2.2. The data set

The raw D2000-2 data feed consists of just over 130,000 data lines. Each line

contains 10 fields detailing the type of event to which it refers, timestamps with a

one hundredth of a second accuracy, price and quantities. Just over 100,000 of

these data lines are limit order entries (with timestamps for entry and exit times, a

buy/sell indicator, quantity available, quantity traded and price.) The rest of the

data consists of market order entries. The market order lines give the quantity

transacted and price, a timestamp and whether buyer or seller initiated. From the

raw data we construct an event time data set including the following variables; themidquote (i.e. the average of best limit buy and limit sell prices); a signed

transaction indicator variable; signed transaction quantity (in $m); the inside

spread; aggregate buy and sell order book size and the number of buy and sell6

limit orders outstanding. The first panel of Table 1 presents descriptive statistics

for the market activity variables for seven non-overlapping subsamples of all

trading days. The first six of these subsamples consist of observations from 2 hour

segments of each day, covering the period from 6 to 18 GMT. The seventh

subsample represents data from GMT overnight periods, 18 to 6 GMT.

The main feature of these statistics is that all series have strong repetitive

intra-day patterns. The number of limit orders outstanding and aggregate size on

the book broadly follow an inverted U-shape across the GMT trading day.

Transaction frequency and volume data show a similar pattern, aside from a lull in

activity in the period from 10 to 12 GMT. D2000-2 liquidity, as measured by the

percentage spread, follows the inverse pattern to transaction activity. It is

particularly noticeable that D2000-2 is extremely illiquid between the hours of 18

and 6 GMT. Spreads are very high, order book size limited and in our data this

interval accounts for less than 5% of transaction activity. During the complemen-

tary portion of the trading day the spread is very small (with a modal value of one

pip) and the D2000-2 order book is very deep.Panel (b) of Table 1 contains summary statistics for the percentage change in

the midquote for our seven subsamples. Again, the effects of the intra-day in

6We define an event as a revision in the best limit buy or sell price or the occurrence of a transaction.

The transaction indicator is signed positive when the aggressor (i.e. the market order trader) is buying

and negative when the aggressor is selling. The transaction indicator is zero when there is a revision in

either of the best limit prices without an accompanying trade. Transaction indicators were constructed

both including and excluding crosses. When included, crosses are signed by treating the latest entering

limit order as the aggressor. Limit buy (sell) order book size is defined as the aggregate quantity, in $m,

outstanding across all buy (sell) limit orders.


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Table 1

Summary statistics for D2000-2 order book and return data

Panel (a): Order book statisticsSample Obs. Bids Offers Q Q s

b o

6 to 8 GMT 10816 31.14 31.25 55.24 58.68 0.0

8 to 10 GMT 11587 46.63 47.02 91.39 89.82 0.0

10 to 12 GMT 10446 47.74 42.15 99.84 79.29 0.0

12 to 14 GMT 17339 50.22 46.77 95.31 80.79 0.0

14 to 16 GMT 12088 41.65 33.31 86.59 57.37 0.0

16 to 18 GMT 3333 20.04 14.46 54.89 22.04 0.0

18 to 6 GMT 4938 9.33 7.43 25.45 15.06 0.0

Panel (b): Return statistics

Sample Mean Var. Skew Kurtosis r Q(5)1

6 to 8 GMT 0.00002 0.00006 0.40 92.5 20.38 1619.3

8 to 10 GMT 20.00000 0.00003 0.10 34.2 20.35 1459.7

10 to 12 GMT 20.00009 0.00013 20.38 127.2 20.39 1632.8

12 to 14 GMT 0.00001 0.00005 0.04 55.9 20.34 2086.0

14 to 16 GMT 0.00004 0.00007 20.05 180.9 20.35 1492.8

16 to 18 GMT 20.00013 0.00036 0.11 18.9 20.42 601.1

18 to 6 GMT 0.00001 0.00230 0.05 34.5 20.50 1267.8

Note: panel (a) of the table gives basic statistics for order book data. Columns headed Bids and Offers give the average

sample. The next two columns give average aggregate quantity outstanding on both sides of the order book in $m. s i

subsample. The final three columns give the total number of transactions, total volume traded in $m and mean transacti

gives summary statistics for midquote returns for the time-of-day-based subsamples. The first four columns of the tablreturn series. The fifth column gives the first order return autocorrelation. Column six presents fifth order BoxLjung stat

fifth order BoxLjung statistics for squared residual returns, where residual returns are created by filtering an MA(1) from

final two columns, the asymptotic 5% and 1% critical values are 11.07 and 15.09 respectively.


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D2000-2 activity are apparent. Both return dependence and volatility are inversely

related to traded volume. An information flow model of trading would suggest the

opposite relationship. Our explanation for the observed correlation is that it is due

to D2000-2 liquidity variation: while in peak trading periods more information isimpounded into midquotes and midquotes change more frequently, in less active

periods the thinness of the D2000-2 order book causes measured return volatility

to be high around trades or limit order cancellations.

The figures in Table 1 imply that D2000-2 only operates effectively during

European and North American trading hours. Hence, in the empirical analysis

presented in Section 4, observations from the 18 to 6 GMT period are omitted.

3. Empirical methodology

As discussed in the previous section, D2000-2 is a multi-lateral order driven

system. This implies that the empirical models used in Lyons (1995) and Yao

(1998) are inapplicable here as they are based on an underlying quote-driven,

single-dealer structure. Instead, we employ the reduced form VAR in trades and

quote revisions developed in Hasbrouck (1991a) and Hasbrouck (1991b). This

framework is not predicated on any particular underlying microstructure model

and has been used in the analysis of entirely order driven markets in de Jong et al.

(1995) and Hamao and Hasbrouck (1995). Information-based trade is identified

via a positive and significant long-run response of quotations to transaction

activity, in line with the theoretical argument presented in Section 1. Further, the

variance decomposition presented in Hasbrouck (1991b) allows one to evaluate the

amount of information entering the FX quotation process that is trade-related and

hence the contribution of information-based trade to price discovery.

There are two main assumptions which underlie the application of this

framework to the current data set. The first of these is that informed agents exploit

their advantage through the use of market orders rather than limit orders.

Non-informed agents, on the other hand, submit either market or limit orders to

D2000-2 depending on their desire for execution speed. This implies that private

information can only influence prices through unexpected trading activity. Theassumption may be justified by noting that an informed agent submitting a limit

order is noisily advertising his beliefs and hence possibly eroding his advantage.

Moreover, using limit orders to exploit (short-lived) information advantages is

risky due to the possibility of non-execution. Finally, the analysis of informed

order placement strategy presented in Harris (1998) suggests that informed agents

should prefer to trade via market order in fast paced, liquid markets. The spot FX

markets certainly exhibit these features lending credence to our assumption.

The fact that everyone trading on D2000-2 is a dealer makes the distinction

between informed and uninformed agents less clear than in other examples. In the

Introduction we argued that customer order flows were the source of information


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asymmetries between dealers. Thus one way to discriminate between informed and

non-informed dealers is to appeal to variation in the size and quality and size of7

dealer customer bases. It should also be borne in mind that there is nothing here

that requires the identity of informed traders to be the same over time. At certainpoints in time, dealers with unusually good customer order flows may act as

informed agents in the inter-dealer market (and, via the preceding arguments, trade

by market order) whilst at other times those same dealers are essentially

uninformed (and supply liquidity to D2000-2).

The second required assumption is that public information is immediately

reflected in quotes. If this was not the case, traders observing information releases

could form profitable trading strategies which would generate correlation between

trades and subsequent quote revisions in the absence of private information.

However, given the levels of liquidity and competition in FX markets this

assumption would seem to be reasonable.A final point that should be discussed here is the attractiveness of D2000-2 as a

venue for informed trade. As D2000-2 competes for order flow with several other

trading venues we should ask where informed dealers are most likely to trade.

Given the microstructural similarities between D2000-2 and EBS, this discussion

should focus on the implications of differences between electronically brokered

trading and direct trading (whether electronic or over the telephone). The former

offers trading opportunities that are pre-trade anonymous but which are broadcast

to the rest of the market. The latter offers trading opportunities that are non-

anonymous but which remain private to the two counterparties. In our eyes, then,

both avenues for trade have an advantagethe pre-trade anonymity of electronic

brokers versus the post-trade opacity of direct trading. Thus, on a theoretical level,

the venue of choice for informed trade is not clear cut and we would argue that

there is no reason to believe that informed traders would always avoid trading on

D2000-2.

3.1. The VAR model

Denote the percentage change in the midquote by r and let x represent a vectort t

of transaction characteristics, where t is an eventtime observation counter. Thebasic VAR formulation used in our empirical work is as follows:

P P

r 5O ar 1O bx 1 (1)t i t2i i t2i 1t

i 51 i 50

7Citibank, as perhaps the biggest player in customerdealer FX trade, might be thought of as an

informed participant in the inter-dealer market whilst a smaller bank with a much smaller customer

base might be considered uninformed. Along the same lines, an interesting recent paper that links

effects of a dealers trades on the market to that dealers reputation is Massa and Simonov (2001).


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P P

x 5O gr 1O dx 1 (2)t i t2i i t2 i 2t

i 51 i 51

where the errors are to be zero mean and mutually and serially uncorrelated at all2 2 2

leads and lags. Further, we define Var( ) 5E( ) 5 s and Var( ) 5E( ) 51t 1t 2t 2t

V.

Eqs. (1) and (2) form a general model of the dynamics of trades and quotes and

the interactions between these variables. Note that the VAR is not entirely standard

as the contemporaneous realisation ofx enters the return equation. Hence tradest

logically precede quote revisions. This ensures that the innovations to the two

equations are uncorrelated and identifies the VAR.

Given the two assumptions detailed above, the innovations in the VAR can be

interpreted as follows. The innovation to the return equation reflects transitory

quote variation and the effects of public information on quotes. The innovation to

the trade equation represents unpredictable transaction activity and hence the

possibility of information-based trade. Using this classification, the effects of

private information on quotations are easily retrieved. First, we invert the VAR to

retrieve the VMA representation:

r a(L) b(L) t 1t

5 (3)S D S DS Dx c(L) d(L) t 2t

2 kwhere, for example, a(L) 5 a 1 a L 1 a L ? ? ? 1 a L . Given the lack of

0 1 2 k

correlation between the innovations, the coefficients in the VMA lag polynomialsare precisely the impulse responses implied by the VAR. The coefficient a , for

k

example, is the effect of a unit return shock on the midquote return at a k period

horizon. The effects of private information are revealed in the b(L) polynomial.

The possibility of informed trade implies that quotes respond permanently to trade`

innovations and hence b(1) (i.e. o b ) should be positive and significant.i 50 i

Estimation of the VAR and calculation of the implied impulse response functions

will hence allow us to evaluate the existence of information-related trade on

D2000-2. In the empirical analysis reported in Section 4, the order of the VAR was

chosen via application of the Schwarz information criterion. The VAR equations

were estimated by OLS and are reported with heteroskedasticity robust standard

errors. The VMA representation was calculated by simulation.

3.2. The variance decomposition

While the VAR model allows us to quantify the information content of a single

trade, it does not permit one to assess the overall importance of informed trade in

determining the evolution of the exchange rate. Hasbrouck (1991b) presents a

variance decomposition for returns, based on the VAR structure above, which

permits retrieval of the variance of the permanent component of midquotes, plus


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the proportion of permanent variation related to order flow. The overall influence

of informed trade can be estimated with the latter measure. Denoting the (log) of

the midquote as q , the following structure is assumed:t

q 5 m 1 s (4)t t t

Eq. (4) decomposes the midquote into a random walk component (m ) and at

mean-zero stationary term (s ). Hence,t

2m 1 m 1 v, v |N(0, s ), E(v v ) 5 0 for t s

t t21 t t w t s

and lim E s 5 lim Es 5 0. From an economic perspective, m can bek` t t1 k k` t1k t

thought of as the fundamental or full-information price process. The transitory

component, s , represents that portion of the current midquote generated by anyt

non-information based microstructure effect, e.g. price discreteness, digestioneffects or inventory control.

2The key parameter in the preceding formulation is s which measures variation

w

in the permanent component. This can be estimated by equating the return

representation implied by Eq. (4) (using the fact that r 5 Dq ) and the returnt t

equation from the VMA. Furthermore, variation in the permanent component due

to order flow alone can also be retrieved. These measures are calculated as:

` ` ` 2

2 29s 5 O b V O b 1 1 1O a s (5)S D S D S Dw i i i

i 50 i 50 i 51

` `

9s 5 O b V O b (6)S D S Dwx i ii 50 i 50

Standard errors for these two variance estimates can be computed via a residual

based bootstrap of the estimated VAR system. An economic interpretation of Eqs.

(5) and (6) is as follows. Public information events are incorporated into the

exchange rate via the return innovation, . The permanent effect on midquotes of1t

`

a unit return shock is given by unity (the contemporaneous impact) plus o ai 51 i

and hence the variation in the permanent component implied by public information

events is given by the second term on the right hand side of Eq. (5). Privateinformation is impounded into the exchange rate via trade innovations with the`

permanent impact of an unexpected unit trade given by o b in the case wherei 50 i

x is scalar. For vector x , the variation in the permanent component driven byt t

trade innovations is thus the first term on the right hand side of (5).

3.3. Non-linear effects in the trade quote relationship

While the VAR structure presented in Section 3.1 provides a fairly robust

characterisation of the determination of trading activity and quote revisions, it

restricts the relationship between these two variables to be invariant to underlying


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market activity. Theoretical considerations, however, suggest that the pace of the

market will impact upon the response of quotes to trades. Admati and Pfleiderer

(1988) suggest that there should be a negative correlation between the information

content of trades and overall volume, for example, and the empirical results ofLyons (1996) might be considered consistent with this.

We allow for the possibility that the dynamic relationship between quotes and

trades varies with market conditions by using an arranged VAR estimation.

Specifically, consider a weakly exogenous candidate measure of the pace of the

market, z , and denote by D the matrix of time-series data on z , returns, tradest t t

and their respective lags i.e. D 5 hz , r , r , . . . , r , x , x , . . . , x j. Now,t t t t 21 t2P t t21 t2 P

consider a sort ofD according to an increasing order sort ofz . Denote the sortedt t

data set by D . One way to investigate variation in the VAR parameters withs

market activity would be to estimate Eqs. (1) and (2) using successively larger

numbers of sorted observations via recursive least squares. Evidence of parameterchange could then be evaluated via plots of the recursive coefficient estimates or

through formal tests for parameter instability.

We follow a similar route to the above but we run a series of regressions where

the number of observations in each regression is fixed and this fixed window is

moved through the sorted data sample. We use a window size of 2000 observations

such that our first regression uses sorted observations labelled s 512000 (i.e. the

first 2000 rows of the sorted data set D ), our second regression uses rowss

1012100 of D , the next regression uses rows 2012200 etc. This techniques

provides similar information to that outlined in the previous paragraph but the

series of estimated parameters is not smoothed by the use of an increasing

sample size. Plots of the parameter estimates are used to gain insight into the

existence of non-linearities in the VAR structure. The first lag of the number and

aggregate size of outstanding limit orders are employed as z in our analysis.t

4. Results

This section presents the results from estimating the VAR models in Section 3.1

and Section 3.3 and the variance decomposition in Section 3.2. We begin withresults for the entire D2000-2 trading day data set. Results for time-of-day based

subsamples are discussed next. Finally we analyse the effects of market activity on

the information content of trades via the arranged VAR models. The basic

variables included in our VARs were percentage midquote returns and a signed

transaction indicator (which takes the values 21, 0 and 11). In the construction

of the transaction indicator only market orders were used, limit order crosses were

ignored. Inclusion of crosses in the definition of the transaction indicator changes

the results only marginally. A signed volume variable was similarly constructed.

The first few columns in the top row of row of Table 2 give a summary of the

relevant VAR parameters estimated using all trading day observations. Some


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Table 2

Summary of VAR results for trading day and subsamples

Subsample VAR results Varia

2 Lag SIC o b x (b) QIR Q(10) s /s

i i i wx

6 to 18 GMT 8 89877 0.00668 4178.7 0.00518 36.1 0.41

6 to 8 GMT 5 28425 0.00565 837.8 0.00502 42.4 0.43

8 to 10 GMT 5 216964 0.00406 1060.1 0.00404 19.9 0.47

10 to 12 GMT 5 2106 0.00758 561.5 0.00583 70.0 0.32

12 to 14 GMT 4 27213 0.00548 1344.1 0.00490 55.6 0.3614 to 16 GMT 9 24220 0.00750 804.6 0.00564 11.2 0.41

16 to 18 GMT 5 2406 0.01853 247.1 0.01192 56.7 0.35

Note: the table summarises the VAR results from the entire trading day and for the 6 time-of-day subsamples. The colu

in the VAR, chosen using the Schwarz information criterion, which is reported in the second column. o b is the sum of thi i

the VAR. The following column gives a Wald test statistic for the null that all are zero. The asymptotic 5% and 1% cr

respectively. QIR denotes the long-run quote impulse response implied by the VAR and the column headed Q(10) gives 12

residuals. The asymptotic 5% and 1% critical values for this test are 18.31 and 23.21 respectively. s /s is the proportiowx v

2 2 2trade-correlated. s /s is the size of the permanent component as a proportion of total return variance. s /s is the ratio

v r wx r

return variation. These figures are calculated using the expressions in Eqs. (5) and (6). Numbers in parentheses in the fi

errors for the ratios. They are calculated from a standard, residual-based bootstrap of the model using 500 bootstrap re


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general comments on those parameters not presented are as follows. First, quote

returns demonstrate significant negative autocorrelation at all included lags. The

transaction indicator displays strong positive autocorrelation. This indicates runs in

buying and selling activity and may be due to dealers splitting large market orders8

or possibly order flow imbalances induced by informed trade. Finally, the effects

of lagged quote returns on transaction direction are mixed. Although marginally

significant as a group, these coefficients were generally individually insignificant.2 2

The R in the return equation was 0.22 whilst the trade equation R was

approximately 0.075.

From the current perspective, the key parameters in the VAR formulation are

those labelled b in Eq. (1) i.e. the effects of trades on current and subsequenti

midquote returns. As shown in row one of Table 2 the sum of these coefficients is

positive. Moreover, each individual coefficient is positive and all are statistically

significant. Hence, a market buy tends to increase quotes. Computing the VMArepresentation and calculating the equilibrium midquote impulse response shows

that an unexpected market buy leads to an upward quote revision of around9

0.005% on average. The average percentage bid-ask spread on D2000-2 is around

0.016%. Expressing the equilibrium price impact of a trade as a fraction of

one-half of the bid-ask spread gives an estimate of the contribution of asymmetric

information to spreads. In this case, the numbers tell us that around 60% of the

spread is compensation for asymmetric information.

This 60% figure is very high relative to recent estimates of similar quantities

from equities markets. For example, Huang and Stoll (1997) report average

asymmetric information components between 10 and 20% for the 20 stocks of the

US Major Market Index. At least some of the discrepancy might be explained by

fundamental microstructural differences between the NYSE and the electronically

brokered segment of the inter-dealer FX market. One would conjecture that both

order processing costs and inventory control costs would be larger for an

intermediated equity market, such as the NYSE, where trade frequency is low

relative to that in FX, for example. The fact that spreads in the Huang and Stoll

(1997) data are around 25 basis points on average, while in our inter-dealer FX

data the average percentage spread is less than 1 basis point, lends credence to this

conjecture. Nonetheless, the fact that the asymmetric information component is solarge in our data is noteworthy.

It should be noted that we also experimented with the use of trade size variables

in the trade description vector (x ). One such experiment involved the use oft

signed volume and signed squared volume in addition to trade direction. In a

8In many other data sets a similar result would be found due to the data recording one transaction in

which a market order executes against a number of limit orders as a series of trades, one for each limit

order affected. Note that in the D2000-2 data this is not the case: one market order appears as one trade

in the data set, regardless of how many limit orders it executes against.9

This translates to a 1 pip (i.e. DM 0.0001) quote increase approximately.


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second set of experiments we examined whether the largest k% of trades (where

we varied k from 5 to 25) had return impacts that differed from those of all other

trades. In contrast to the results of Yao (1998), the size variables were generally

insignificant. This is likely to be due to the fact that there is very little variation intrade size on D2000-2. Over 90% of D2000-2 trades are for less than $5m such

that the data may not contain the power needed to identify a size effect.

The result presented in the preceding paragraphs is keymarket orders

permanently alter D2000-2 quotes and we take this as evidence that they are

information-motivated. Of course, given that our study is based on 5 days of data,

it might be argued that the effects we uncover are not permanent in that they die

out over a horizon longer than 1 week. This is a possibility. However, a number of

theoretical and empirical arguments support our viewpoint. First, on a theoretical

level, effects of order flow on exchange rates that mean-revert over weeks or

months might indicate profit opportunities, violating market efficiency. Second,corroborating empirical evidence on the long-run/permanent effect of market

order activity on quotes can be drawn from regressions of returns on order flows

aggregated over fixed calendar time intervals as in Evans and Lyons (2001). We

have run a set of such regressions having midquote returns for sampling

frequencies ranging from 20 seconds to 1 hour on the left-hand side and aggregate

order flow (i.e. the excess of the number/volume of market buys over market sells

in an interval) on the right-hand side. We choose 1 hour as the lowest sampling

frequency due to the fact that our entire span of data only covers 5 days. Results

demonstrate that across all sampling frequencies, the effect of order flow on2returns is highly significant. Moreover, the R of the regression increases as the

sampling frequency decreases such that at a 1 second level, order flow explains

approximately 70% of all return variation. Thus, we believe that the results

presented here, coupled with those in Evans and Lyons (2001), provide clear

evidence for the information content of order flow and its longer run effects on the

USD/DEM.

The results above confirm the findings of previous research in that a portion of

trade on D2000-2 can be characterised as information-related. A question which

has not been directly addressed in the literature, however, is the extent to which

such asymmetries alter over the trading day. Microstructure theory relevant tointra-day variations in the intensity of informed trade and liquidity can be found in

Admati and Pfleiderer (1988) and Foster and Viswanathan (1990). The endogen-

ous information acquisition model of Admati and Pfleiderer (1988), for example,

predicts that high volume periods should be characterised by relatively small price

impacts from trade. This is due to the clustering of discretionary liquidity trade and

increased competition between informed traders in equilibrium. The model of

Foster and Viswanathan (1990) has a similar setup except that the information

advantage of insiders is assumed to decline over time.

We examine these issues by estimating the VAR separately for the six previously

defined non-overlapping subsamples of the trading day. The results, are given in


17/23


rows 2 to 7 of Table 2, correspond with the predictions made by Admati and

Pfleiderer (1988). First note that Table 1 shows that the segments of the trading

day with greatest volume are those with lowest percentage spreads. Admati and

Pfleiderer (1988) construct a batch trading model and, hence, it does not contain aspread. However, given that their framework predicts more liquid markets in high

volume intervals this result is consistent with their analysis. Further, Table 2

indicates that the information content of trading activity follows an intra-day

pattern which is the inverse of that followed by volume. Thus, in high volume/

liquidity periods, the price response to a trade is relatively low. This is consistent

Fig. 1. The response of the midquote to a unit trade innovation. Notes: these plots show the response of

the D2000-2 midquote (i.e. the average of the best limit buy and sell prices) to a unit trade innovation.

The impulse response functions are calculated via simulation from the VAR estimates summarised in

Table 2. Panel (a) presents the impulse response function estimated from all trading day data (i.e. all

data falling in the 618 GMT interval. Panel (b) shows the function calculated using data from the

810 GMT subsample only and, similarly, panel (c) shows the impulse responses implied by the VAR

estimated using data from the 1618 GMT interval. The solid line in each panel gives the actual

impulse response function and the dotted lines are a 95% confidence band, estimated using a residual

based bootstrap of the VAR model with 500 bootstrap replications. The x-axis values give the number

of observations since the trade shock was first felt.


18/23


with the hot-potato hypothesis of Lyons (1996), although it runs counter to the

results presented in Dufour and Engle (2000). A further point to note is that, while

the size of the asymmetric information effect is inversely related to volume, the

significance of the trade variables is greater in high-volume periods. These resultscontrast with those of Foster and Viswanathan (1993) who demonstrate a positive

relationship between adverse selection costs and trading volume for a sample of

NYSE equities and reject the models of Admati and Pfleiderer (1988) and Foster

and Viswanathan (1990).

Hence, our results suggest that high volume periods are characterised by a

concentration of liquidity trades and increased competition between informed

agents, with these effects reducing the price impact of a trade. Fig. 1 graphically

illustrates the VAR results, plotting the quote impulse response functions (along

with 95% confidence intervals) for the entire trading day data set plus the 8 to 10

GMT and 16 to 18 GMT subsamples in panels (a) through (c) respectively. Thesetwo subsamples were chosen as they are those with the lowest and highest average

spread respectively. The individual panels of Fig. 1 also demonstrate variation in

D2000-2 depth across the trading day through variation in the immediate response

of the midquote to a trade. In panel (c) this is about five times as large as the

corresponding value from panel (b)and thus peak trading periods are characterised

by greater clustering of limit orders around the inside spread.

Based on the VAR results, the return variance decompositions are presented in

the final three columns of Table 2. Across the entire trading day, 41% of the

permanent return variance is attributable to order flow (with a bootstrapped

standard error for this estimate of 0.015). Comparable figures are contained in

Hasbrouck (1991b) who reports an average value of 33% for a sample of U.S.

equities and de Jong et al. (1995) who analyse a sample of French stocks traded on

the Paris Bourse and report an average trade correlated component of 40%. Hence

our results imply that the information content of USD/DEM order flow on

D2000-2 is of the same magnitude as that on equities markets. Examination of the

last two columns of Table 2 shows that the permanent component accounts for

only one quarter of all return variation such that the information contained in order10

flow contributes one tenth of total return variance. Of course, the distinction

between the size of trade correlated component, at 40%, and the 10% number fromthe previous sentence is due to high-frequency, transitory return variation reducing

the latter.

The variance decompositions for the six trading day subsamples are also

presented. While the VAR results showed that the information content of a single

trade was inversely related to the level of overall traded volume, the variance

10Note that if one performs the permanent/transitory decomposition on data from the 186 GMT

period only, the ratio of permanent to total return variation is less than 0.05. Thus, during this interval

returns are almost entirely noise rather than signal. It is for this reason that we omit these data from

our econometric analysis.


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decompositions show that the information contained in order flow as a whole is

positively related to volume. The ratio of the trade-correlated component to the2

variance of total permanent quote variation (s /s ) is greatest during episodes ofwx v

high traded volume. Furthermore, the proportion of all return variation explainedby order flow information and the size of the permanent component have a

positive correlation with volume.

Again, these results can be reconciled with the predictions from theoretical

models of intraday variation in transaction activity and price formation. Admati

and Pfleiderer (1988) imply that, in high volume intervals, return variance should

be high and prices more informative. In Section 2 we discussed the negative

correlation between total traded volume and volatility arguing that the lack of

order book depth in low trading periods was behind this result. However, the

variance decompositions show that both permanent return variation and the

information entering prices through order flow increase with volume. Aggregatetrading activity hence contributes a greater proportion of all information in peak

trading periods in line with Admati and Pfleiderer (1988).

The prior results provide evidence that the information content of a trade and

that of aggregate order flow are strongly linked to market activity via time of day.

Our final set of estimations refine this analysis. The relevant theoretical models

predict such patterns due to clustering of liquidity trading in equilibrium. Hence,

empirical identification of times of concentration in uninformed activity should

corroborate the prior results. Given the assumptions on order placement strategy

made in Section 3, a concentration of noninformation based trade should be

associated with a high level of limit order placement (the informed preferring to

trade by market order due to the execution certainty it yields.)

Data from such episodes should hence display small price impacts from trades.

This is examined using the arranged VAR technique discussed in Section 3.3. Both

the first lag of the number and aggregate size of all limit orders outstanding are

used as the variable governing the non-linearities in the VAR (z ).t

Fig. 2 presents the moving window OLS estimates of the asymmetric in-P

formation coefficients from the VAR (i.e. o b) after arranging the data by thei 50 i

aggregate limit order size (panel (a)) and numbers of orders outstanding (panel (b))

respectively. These estimates are from an 8 lag VAR using the 618 GMTD2000-2 sample. The x-axis labels in the panels give the sorted observation

number of the final included data point.

Both panels of Fig. 2 strongly suggest that the asymmetric information problem

is lower when z is higher i.e. in periods of high limit order placement. Conversely,t

when either the number or size of outstanding limits is low, the impact of an

unexpected trade on the midquote is large. The relationship is clearly non-linear,

however, with the sum of asymmetric information coefficients declining sharply

over the first 15,000 sorted observations and less rapidly thereafter. Nonetheless,

the graphs provide evidence consistent with Admati and Pfleiderer (1988) and the

hot-potato hypothesis of Lyons (1996).


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Fig. 2. Sum of asymmetric information coefficients from the arranged VAR specifications. Notes: theseP

plots show the sum of the asymmetric information coefficients (i.e. o b) from arranged VARi 51 i

estimations, as discussed in Section 3.3. In panel (a) the data are arranged by aggregate order book size

(i.e. the sum across limit buy and sell sides of order quantity outstanding in $m) and in panel (b) the

data have been arranged by aggregate number of order outstanding. The values on the x-axis give the

number of the last observation included in the subsample upon which the VAR was estimated.

We have tested the stability of the VAR coefficients in a number of ways. For

example, we have estimated extended versions of Eqs. (1) and (2) where

right-hand side variables are interacted with dummies for low, medium and high

levels of liquidity supply. Formal tests for parameter stability confirm the visual


21/23


evidence in Fig. 2, yielding strong evidence of structural change in the VAR

equations related to the level of liquidity supply.

Finally, it is clear that these arranged VAR results tell essentially the same story

as do our results from time-of-day subsamples of the data. When D2000-2 is busy,price impacts of trading tend to be smaller. We have included them here as we feel

that time-of-day effects, in themselves, are uninterestingthe interesting issue is

what economic phenomenon generates them. We feel that our result relating the

size of information effects to the rate of liquidity supply is a step towards

understanding the underlying economics.

5. Conclusion

This paper analyses trades and quotes from the electronically brokered segmentof the spot USD/DEM market. Specifically, we examine whether this market can

be characterised as subject to information asymmetries. Using the technology

introduced by Hasbrouck (1991a) and Hasbrouck (1991b) we find that trades do

carry information. Roughly 60% of the bid-offer spread in our data can be related

to the asymmetric information problem. Further, around 40% of the variation in

the fundamental price is shown to be order flow driven, a proportion which is

comparable to those found in studies of equity markets.

We uncover clear intra-day variation in the information content of a trade and

the total information content of order flow. In line with the theoretical predictions,

in high volume/liquidity intervals the information content of a single trade is low

while the share of information entering the midquote through order flow is high. A

more refined analysis of the relationship between the price impact of trades and

market activity is also presented. Similar to the time-of-day analysis, results from

an arranged VAR estimation indicate that the asymmetric information coefficients

vary systematically with market liquidity.

There are several possible extensions to the current study. First, a more careful

analysis of the non-linearities in the VAR structure may prove to be fruitful.

Parameterising the nonlinearities would likely yield insights into the determination

of price impacts from trade. Another interesting area of research would be toexamine how transaction activity affects subsequent liquidity supply through

analysis of the excess demand and supply schedules for currency. Furthermore,

econometric analysis of individual limit order entry and execution would provide

stronger evidence on the order placement strategy of dealers and how it reacts to

trading activity and quote volatility. Such issues are currently under investigation.

Finally, as we have mentioned above, D2000-2 is one of several venues for

inter-dealer foreign exchange trade. It would be interesting to evaluate how these

venues interact (in terms of trading activity and quoting behaviour). This would

give a clearer picture of when and where informed trade was trading price.


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Acknowledgements

I would like to acknowledge the financial support of the Financial Markets

Group at LSE and the ESRC (UK). I am grateful to Jon Danielsson, CharlesGoodhart, Roger Huang, Rich Lyons, Carol Osler, Paolo Vitale, seminar particip-

ants at LSE and the City University Business School, three anonymous referees

and the Editor, Andrew Rose, for helpful comments. Thanks also to Reuters plc for

providing the data employed in this study. All remaining errors are my own.

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