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The ine ciency of Reuters foreign exchange

quotes

Martin Martens a,* , Paul Kofman b

a Department of Accounting and Finance, Lancaster University, Lancaster, LA1 4YX, UK

b School of Banking and Finance, The University of New South Wales, Sydney, NSW 2052, Australia

Abstract

Received 18 April 1997; accepted 27 December 1997

Reuters foreign exchange (FXFX) page is the world wide predominant information

source to foreign exchange traders. In this study we compare the indicative spot exchange

rate quotes from Reuters with their matching futures exchange rates from the

Chicago Mercantile Exchange. We ®nd that the indicative quotes on Reuters FXFX

page are ine cient and could be improved by incorporating information from the futures

market. This casts doubt on the way banks determine these quotes, as well as

on the informational content of these quotes as an indicator of the current exchange

rate. Ó 1998 Elsevier Science B.V.

JEL classi®cation: G14; G15

Keywords: Exchange rates; Futures; E ciency

1. Introduction

Journal of Banking & Finance 22 (1998) 347±366

The spot foreign exchange market is a 24 hours electronic market with brokers

and traders around the world. Brokers display quotes to their customers.

*

Corresponding author. Tel.: +44 1524 593623; fax: +44 1524 847321; e-mail: m.martens@lancaster.ac.uk.

0378-4266/98/$19.00 Ó 1998 Elsevier Science B.V. All rights reserved.

PII S 0 3 7 8 - 4 2 6 6 ( 9 8 ) 0 0 0 0 4 - 1


348 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

These quotes are the best bid and ask price provided by a limited number of

banks regularly contacted by the broker. Since there are many brokers each

contacting their own circle of banks to obtain quotes and having their own customers,

the natural question arises whether this market is informationally e -

cient.

To date, no data sets have been available allowing for a direct test of the ef-

®ciency of the spot market in foreign exchange. Goodhart et al. (1996, 1997)

study 7 hours of the Reuters-2000 electronic trading system, which at the time

of sampling was still a relatively small broker in the spot market. Lyons (1995)

studies one week of all transactions of a New York broker.

Obviously, any data set on quotes from brokers will only re¯ect a part of the

spot market. In fact, the only information source available to all traders around

the world consists of indicative quotes, as provided by Reuters foreign exchange

(FXFX) page, and those provided by its competitors Knight Ridder and Telerate.

As such these quotes play an important role in the spot market, indicating

the current foreign exchange rate. Though the quotes are only `indicative', studies

using the quotes claim banks have reputation considerations and will most

likely trade against their quotes if called within a short time after appearance

on the Reuters FXFX page. This assumption is crucial for studies like De Jong

et al. (1995) who study triangular arbitrage, Bollerslev and Domowitz (1993),

and Dacorogna et al. (1993) who study the trading intensity and volatility patterns

in the spot market, and Bollerslev and Melvin (1994) who study the relationship

between the spreads and volatility. Similarly, Olsen and Associates

who use these quotes to forecast the foreign exchange rate, started a boom in empirical

research by releasing 1 year of Reuters quotes in 1994.

In this study we further investigate the assumption that one can actually

trade against the Reuters quotes. We compare these spot exchange rate quotes

with their matching futures exchange rates traded at the Chicago Mercantile

Exchange (CME). The futures market is a highly liquid market, but in value

terms relatively small as compared to the spot market. Nevertheless, we ®nd

that the futures market is leading the `quoted' spot market for up to 3 minutes.

The results of a simple trading strategy show that pro®ts can be made from the

futures lead, unless trading against the Reuters quotes is not (always) possible.

This could (partly) explain our results, which we therefore interpret conditional

on the possibility of trading against the Reuters quotes:

(i) If one can trade against the Reuters quotes, then our results show that

gains can be made and hence the spot market is ine cient.

(ii) If one cannot (always) trade against the Reuters quotes, our results can

be (partly) explained by that fact. However, this implies that studies which

made the assumption that one can trade against Reuters quotes were incorrect

to do so. Their results will then have to be taken with care.

In both cases banks displaying quotes on Reuters FXFX page can improve

upon these quotes by paying more attention to the futures market. The futures


M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 349

price provides a more adequate re¯ection of the (true) current spot exchange

rate than the Reuters quotes.

The remaining part of this study is organised as follows. Section 2 discusses

in further detail the functioning of the spot and futures markets. In Section 3

the data set is discussed. Section 4 describes the methodology, and Section 5

elaborates upon the results. Section 6 focuses on the e€ect of prescheduled

news announcements and high volatility periods. In Section 7 the pro®tability

of a simple trading strategy is tested. Finally, Section 8 will conclude.

2. Microstructure of foreign exchange markets

The major di€erence between the futures and the spot market in foreign exchange

is the trading system. While the futures contracts at the CME are traded

in an open outcry (OOC) market, the spot market is an electronic market

with brokers and traders around the world. In addition to brokers setting

quotes, market participants can also o€er or obtain quotes via Reuters, Telerate

or Knight Ridder.

We will use Reuters FXFX data for the spot rates. These data consist of,

mainly indicative, bid and ask quotes. Transaction prices are not available.

Each trader can immediately submit quotes to Reuters FXFX page. In addition

to Reuters FXFX page, traders have a screen displaying a very small

spread often of a magnitude of only 1 tick (i.e., one hundredth of percentage

point). This spread re¯ects the current best bid and ask provided by a broker.

This broker just compares the available bids and asks of a number of banks

(usually four or ®ve) by calling them at regular time intervals. In a normal market

situation the spreads on Reuters screen will include the best bid and ask

price. 1 Often banks are willing to trade only on one side of the bid±ask spread

and they try to do so by setting a bid±ask hitting the currently best bid or ask

and moving the other side away. 2

Results in Goodhart et al. (1996) suggest that the nature of the `indicative'

spreads on Reuters FXFX page is di€erent from the `®rm' spreads provided by

brokers (derived from Reuters electronic broking system, D2000-2). However,

1 Most banks put quotes on the Reuters screen themselves. In addition, there are several brokers,

each with their own limited circle of banks from which they obtain their quotes. As a result Reuters

contains more updated information than the broker uses. The minimum spread, however, is 5 ticks

on Reuters screen, while it can reduce to 1 tick for a broker using the best bid and ask.

2 For example, if for the DM/$ spot rate the current spread set by the broker is 1.6022±1.6023, a

bank willing to sell US dollars could either do so by hitting the current best bid of 1.6022 or by

setting a quote of, e.g., 1.6018±1.6023. In the latter case the quote on Reuters screen (1.6018±

1.6023) will be skewed to the left. Similarly a bank willing to buy US Dollars could set a quote of

1.6022±1.6032, skewing the spread to the right.


350 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

an unresolved issue is the informational role of the `indicative' quotes as a re-

¯ection of the entire spot market which is available to all traders. Our results

show that not only the nature of these indicative spreads is di€erent, but also

that the quoted prices are ine cient. This does not necessarily imply pro®table

trading opportunities. In a high-volatility situation, for example around news

announcements, Reuters FXFX page might lag the current developments.

Traders ®rst trade and only then update Reuters screen by setting new quotes.

In that case the best bid and ask of the broker might be outside the spreads

given on Reuters FXFX page. It will then also be impossible to trade against

the bids and asks given on Reuters screen, simply because they are outdated

and therefore no longer valid. In all other circumstances studies using Reuters

FXFX quotes claim that reputation considerations will ensure that banks will

trade at their quotes if requested within reasonable time after the appearance

on Reuters.

In this study we compare the Reuters quotes for the DeutscheMark/US Dollar

(DM/$) exchange rate with the DM/$ futures contract traded on the CME. During

our sample period most trading was still conducted by telephone and with

many di€erent brokers having a reasonable market share. Reuters FXFX page

was therefore still the main source of publicly available information, while the individual

broker's quotes were only known to the limited circle of his or her clients.

3. Data

The data set consists of intraday Reuters quotations of the DM/$ exchange

rate from the Olsen and Associates data set, and of futures prices of the DM/$

exchange rate for the September 1993 contract from the CME. The sample period

covers June, July and August 1993. We choose to analyse the DM/$ exchange

rate since it is the most liquid exchange rate in terms of number of

contracts (futures market) and number of bid±ask quotes (spot market).

The futures data contain the transaction price, the date and the time truncated

to the minute (e.g. 8:37 0 45 00 will be shown as 8:37). The Reuters quotations

contain the date, a time stamp to the nearest second, the bid and ask price,

the code for the bank, and the code for the country. The data set is an almost

complete record of spot DM/$ quotations shown on Reuters FXFX page. Suspect

quotations were ®ltered out using the methods of Dacorogna et al. (1993).

Whereas Reuters FXFX data cover the entire day (24 hours), the futures data

only cover the 7:20 a.m.±14:00 p.m. Chicago Standard Time (CST) period.

In total we use 65 common trading intervals (i.e. days), during the trading

hours of the CME. In this period we have about 3.2 quotations per minute

for the futures market, and about 4.0 observations per minute for the spot market.

The distribution of the number of observations over the trading hours of

the CME is given in Fig. 1. The decrease in the number of spot quotes that


M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 351

Fig. 1. Total number of Reuters spot quotes and futures transaction prices for the DM/$ exchange

rate for each minute during the trading hours of the Chicago Mercantile Exchange, 1 June±31

August 1993.

starts around 10:00 a.m. (CST) can be ascribed to the withdrawal of the European

traders from the market. To allow for a fair comparison between the futures

and the spot market, we con®ne the main part of our analysis to the 7:20±

10:00 a.m. CST window.

We construct 1-min prices and returns to compare the futures exchange

rates with the quotes on Reuters FXFX page. For the futures transaction prices

the last available price in each minute is used, 3 and whenever there was no

trade in a certain minute, the price in the previous minute is used. For the spot

market the bid±asks are often skewed to one side, and this may alternate between

the bid- and ask-side. This `skewing' results in negative autocorrelation

(Goodhart and Figliuoli, 1991; Bollerslev and Domowitz, 1993) when using the

bid±ask midpoints to generate spot returns. We propose, therefore, to extract

spot prices from the Reuters data in two di€erent ways. First, we will use the

last available quote in each minute. From this bid±ask quote the midpoint is

used as the price (from now on we call this panel A):

3 As noted before, the time stamp for the futures prices is truncated to the minute. As a result,

e.g. the last observation with time label 0731 will be used to re¯ect the price at 0731 0 59 00 .


352 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

Table 1

Autocorrelation in the DM/$ spot and futures exchange rate returns

Spot Futures

Panel A Panel B

c )0.000347 )0.000292 )0.000246

Rt 1 )0.187 a )0.0516 a )0.0540 a

Rt 2 0.00244 0.0448 a 0.0442 a

Rt 3 0.0324 a 0.0339 a 0.0220 b

Rt 4 0.0359 a 0.0249 b 0.0275 a

Rt 5 0.0250 b 0.0215 b 0.0409 a

Rt 6 )0.0259 a

Rt 7 )0.0243 b

The results are obtained from an AR(p) process for the DM/$ spot and futures exchange rate

returns. The sample period is June±August 1993, with for every day 1-min observations from 7:20±

10:00 a.m. CST. This results in 10,335 returns. Panel A uses the midpoint of the last spot quote each

minute, Panel B uses also the previous two spot quotes (if within 15 seconds) to ®rst determine the

best bid and ask and then calculate the midpoint.

a Denotes signi®cance at 1% level.

b Denotes signi®cance at 5% level.

st ˆ …st;bid ‡ st;ask†=2 …ln st;bid ‡ ln st;ask†=2; …1†

where the log spot rate st is expressed in US dollars per DeutscheMark, like the

futures price. Second, we will also use the two preceeding quotes, if within

15 seconds of the last available quote of the minute, and calculate the best

bid and ask (from now on we call this panel B). Thus, we try to imitate what

happens in practice, i.e., the broker searching for the currently best available

bid and ask. 4 This latter approach will (partly) correct for the spread being

usually skewed to one side. This can be observed in Table 1. The AR(1) coef-

®cient is )0.0516 for Panel B as opposed to )0.187 for Panel A. The negative

®rst order autocorrelation in the futures transaction prices can be attributed to

the bid±ask bounce (Roll, 1984).

4. Methodology

Having discussed the univariate data series, we can now proceed with their

joint analysis based on the covered interest rate parity (CIRP). For the necessary

US and German interest rates we obtained daily Eurocurrency rates from

Datastream. We refrain from using intraday interest rates. First, they are not

readily available. Second, foreign exchange transactions are not settled within

4 Obviously we cannot imitate exactly the broker(s), since the Reuters screen contains actually

more spreads than the ones used by a broker. Furthermore, we cannot observe from our data

whether a quote is still valid.


M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 353

the trading day but at the end of the trading day when the ®nal position in foreign

currency is deposited at a bank o€ering the best rate. 5 Thus, the daily

rates turn out to be the realised rates which had to be estimated by the traders

during the day. For both the Eurodollar and the Euromark we have the midquotes

(the bid- and ask-quotes are symmetric around the mid-quotes) for the

1-week, 1-month, 3-months and 6-months interest rate. From these data we

construct midpoint series for the time-to-maturity of the futures contract using

a standard polynomial (of order 6) interpolation. 6 The resulting series are rt;mid

for the Eurodollar rate, and r f

t;mid for the Euromark rate.

To evaluate the CIRP, Brenner and Kroner (1995) relate the cost-of-carry

asset pricing model to the existence of cointegration between the spot and forward

(futures) prices. They illustrate that cointegration depends on the time-series

properties of the cost-of-carry. Since the interest rate di€erential is likely to

be stationary, the forward price and spot price in the FX markets should be

cointegrated with vector (1 )1). Under certain assumptions given in Brenner

and Kroner, a marking-to-market adjustment term, re¯ecting the di€erence between

futures and forward contracts, while being non-stochastic will have no

e€ect on the cointegration relation. 7 For an extensive summary of the literature

on cointegrating vectors for foreign exchange markets we refer to Table 2

in Brenner and Kroner.

In our approach we de®ne the theoretical (log) futures price by

ft ln Ft ˆ …rd;mid r f

d;mid †…T d† ‡ st ; …2†

where T is the maturity date of the futures contract, and t is the current time on

day d. Hence for all prices within day d we make the same interest rate adjustment

because the actual transactions in the spot market take place at the end of the

trading day. This expression is based upon the CIRP, and it should therefore include

an error term equal to the di€erence between the forward and futures rate

(see footnote 7). When using returns from minute to minute, this error term can

be neglected. Furthermore, omitting overnight returns, a return in the theoretical

futures price as de®ned by Eq. (2) will be equal to the spot return.

The mispricing error is then de®ned as

5

Banks keep a record of all transactions during the day of their currency traders. Only at the end

of the day all transactions are actually settled. The ®nal position in each currency has then to be

deposited at the best available rates.

6

See for example Chambers et al. (1984), p. 236. It is assumed that the term structure of interest

rates may be expressed as a simple polynomial of time.

7

More speci®cally, Brenner and Kroner derive for the (log) futures price

ft ˆ …rt r f

t †…T t† ‡ st ‡ Qt;T ;

where Qt;T is a non-stochastic marking-to-market adjustment term. This term will depend on the

interest rate expectations of traders, which we cannot measure.


354 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

Zt ˆ ft ft ; …3†

where ft is the market observed futures price. If this mispricing variable is stationary

(while the observed and theoretical futures prices are non-stationary),

the prices will be cointegrated with a vector close to (1 )1). To facilitate the

interpretation we will use this exact vector (1 )1). In Section 5 we formally test

for cointegration.

Engle and Granger (1987) show that if prices are cointegrated, the returns

follow an error correction model. In this model the current price changes depend

on how far the system was out of long-run equilibrium in the previous

period. The traditional solution of ®rst di€erencing the data imposes too many

unit roots in the system, biasing parameter estimates and inference. Instead, we

will estimate the following Vector Error Correction Model (VECM):

DXt ˆ l ‡ XK

kˆ1

CkDXt k ‡ ab 0 Xt 1 ‡ et …4†

with Xt ˆ (ft f t ) 0 , b is restricted to (1 )1) 0 (thus b 0 > Xt 1 equals zt 1), l and a

are (2 ´ 1) vectors of parameters, Ck are (2 ´ 2) matrices of parameters, et is

a (2 ´ 1) error vector with mean zero and variance±covariance matrix X, and

K is the lag-length which will be determined using the Schwarz (1978) criterion.

Estimation of the model in Eq. (4) allows us to calculate impulse-response

functions, and to determine the information share of both the spot and futures

market by using the measure de®ned in Hasbrouck (1995), p. 1183. The VECM

has a common trends representation (e.g. Johansen, 1991)

Xt ˆ X0 ‡ C Xt

iˆ1

ei ‡ C…L†et ; …5†

where X0 is a constant (2 ´ 1) vector, and C(L) a matrix polynomial in the lag

operator. C is the impact matrix which represents the long-run impact of a disturbance

on each of the two prices. The impact matrix is related to Eq. (4) by

the expression

C ˆ b ?…a 0

? Wb ?† 1 a 0

? ; …6†

where a? (b?) is a vector orthogonal to the vector a (b), and W is given by

W ˆ I XK

kˆ1

Ck ‡ Kab 0 ; …7†

where I is the (2 ´ 2) identity matrix. By construction, C will have two identical

rows, say c. If the price innovations between the spot and the futures market

are correlated, X will not be diagonal. Let F be the Cholesky factorisation of

X (F the lower triangular matrix such that X ˆ FF 0 ), then the market share

of the innovation variance attributable to market j (j ˆ 1,2 for the futures

and spot market, respectively) is equal to


Sj ˆ …‰cF Šj †2

cXc0 : …8†

The outcome depends on the stacking order of the prices in the vector Xt. The

information share is maximised on the ®rst price in the vector. Therefore the

information share will alternatively be calculated by putting the spot price (theoretical

futures price) ®rst in the vector Xt. Then Eq. (8) will provide both a

lower and upper bound for the information share of each market.

One disadvantage of the VECM model is the multicollinearity problem between

the explanatory variables, the lagged futures and spot returns, obscuring

the length of the lead±lag relation, despite penalising the speci®cation of too

many lags according to the Schwarz criterion. This multicollinearity e€ect is

quite strong here due to negative autocorrelation in both return series and

the positive impact of one market on the other market. This results in opposite

signs of the parameters of the futures returns on the one hand and the parameters

of the spot returns on the other hand. For this reason we will also calculate

cross-correlations between the observed and theoretical futures returns.

Finally, to address the impact of high volatility on the intertemporal relations

set out above, we split our data set into two parts: high and low volatility.

Since the futures market will turn out to be the most frequently updated market,

we will employ the following ad hoc rule using the absolute futures returns

as a proxy for the volatility:

1 X

N

n‡N

jDf1j > 1 X

T

T

1 X

jDftj ‡ P

t 1

T

1 X

…jDftj

T

T

jDftj† 2

v

u

t : …9†

tˆn‡1

tˆ1

Thus, we attribute each N-minute interval to the high volatility panel if the

mean volatility during these N minutes is above the mean volatility of the total

sample plus P percent of the standard deviation of the volatility of the total

sample. For the majority of cross-correlations both the spot and the futures return

will lie within the same N-minute interval. Volatility clustering is then taken

into account as well by assigning intervals to the high or low volatility

panel instead of single minutes.

5. Results

M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 355

In this section we will ®rst test for cointegration between the market observed

futures prices and theoretical (spot-induced) futures prices based on

the CIRP. Next, we will estimate the VECM speci®ed in Eq. (4). From this

we will calculate impulse±response functions and the information share of both

the futures and spot market. In Section 5.2 we investigate simple cross-correlations

between the spot and futures returns to determine the lead±lag structure

between the two markets.

tˆ1

iˆ1


356 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

Table 2

Augmented Dickey Fuller tests and cointegration

Panel A Panel B Critical

Futures

price

Theoretical

futures price

5.1. Cointegration and error correction

Futures

price

Theoretical

futures price

value

Levels )2.39 )2.37 )2.39 )2.36 )2.86

Di€erences )37.9 )38.2 )37.9 )37.6 )2.86

Cointegration a )22.7 )22.7 )22.7 )22.7 )3.30

Vector 0.997 1.00 0.997 1.00

Mispricing error b )22.1 )22.0 )2.86

Stationarity and cointegration tests for the DM/$ spot and futures prices. The sample period is

June±August 1993, with for every day 1-min observations from 7:20±10:00 a.m. CST. Panel A uses

the midpoint of the last spot quote each minute, Panel B uses also the previous two spot quotes (if

within 15 seconds) to ®rst determine the best bid and ask and then calculate the midpoint. Theoretical

futures prices are calculated using Eq. (2). For the stationarity tests the following equation

is estimated using OLS:

Pt Pt 1 ˆ h0 ‡ h1 Pt 1 ‡ XL

/ i …Pt i Pt i 1† ‡ et: …i†

iˆ1

For both the levels and the ®rst di€erences, the t-values of h1 are reported. Critical values are

provided in the last column. The null hypothesis of non-stationarity is rejected if the t-value of h1

exceeds the critical value.

a

P1t ˆ c ‡ pP2t ‡ zt is estimated, ®rst with the futures price as the dependent variable (column `futures

price'), second with the theoretical futures price as the dependent variable (column `theoretical

futures price'). For the resulting error term, Eq. (i) is estimated. The t-value of h1 is reported here.

b For the mispricing error (di€erence between futures and theoretical futures price) Eq. (i) is esti-

mated. The t-value of h1 is reported here.

Table 2 provides the results of the test for cointegration. For both panel A

and panel B the (theoretical) futures prices are non-stationary (row 1, labelled

`levels'), while the returns are stationary (row 2, labelled `di€erences'). Hence

both the theoretical futures price and the market observed futures price have

one unit root, which is the ®rst condition for cointegration.

The estimated cointegration vector (row 4) is close to the expected vector

(1)1), and the resulting residual is stationary as can be seen from row 3.

Finally, the mispricing error as de®ned in Eq. (2) is stationary. To facilitate interpretation

we will use this mispricing error.

The results of the VECM model for both panel A and B are provided in

Table 3. The mispricing error of the previous period, zt 1, only has a signi®cant

impact on spot price changes. Given the estimated coe cients (e.g. in Panel A it

is 0.215 for the spot equation and )0.0287 for the futures equation), the e€ect of

the previous mispricing error is clearly stronger on the current spot price change.

Since the results for Panel A and B are similar, we only report the impulse

response functions resulting from the estimated VECM for Panel B. The im-


M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 357

Table 3

Vector error correction model for DM/$ spot and futures exchange rate returns

Panel A Panel B

Dst Dft Dst Dft

Dft 1 0.456 a )0.0636 a 0.403 a )0.0669 a

Dft 2 0.501 a 0.0498 a 0.507 a 0.0437 a

Dft 3 0.420 a 0.0119 0.401 a )0.000281

Dft 4 0.343 a 0.0151 0.322 a 0.00653

Dft 5 0.292 a 0.0342 0.283 a 0.0294

Dft 6 0.229 a )0.0334 0.224 a )0.0312

Dft 7 0.185 a )0.0356 b 0.189 a )0.0272

Dft 8 0.154 a )0.0151 0.151 a )0.00460

Dft 9 0.136 a 0.00320 0.134 a 0.00990

Dft 10 0.0955 a )0.0229 0.0932 a )0.0130

Dft 11 0.0451 a )0.0398 a 0.0454 a )0.0344 a

Dst 1 )0.708 a 0.0574 a )0.611 a 0.0812 a

Dst 2 )0.404 a 0.0242 )0.374 a 0.0280

Dst 3 )0.345 a 0.00359 )0.321 a 0.0180

Dst 4 )0.285 a 0.00576 )0.274 a 0.00830

Dst 5 )0.228 a 0.0100 )0.222 a 0.00172

Dst 6 )0.193 a 0.00976 )0.181 a 0.00215

Dst 7 )0.168 a 0.00903 )0.175 a )0.00897

Dst 8 )0.116 a 0.00842 )0.115 a )0.00366

Dst 9 )0.0894 a 0.00619 )0.0820 a )0.00936

Dst 10 )0.0435 a )0.00864 )0.0354 a )0.00268

Dst 11 )0.0146 )0.00263 )0.00273 )0.00588

zt 1 0.215 a )0.0287 0.170 a )0.0293

l )0.00308 a )0.000026 )0.00243 a )0.000022

Adj R2 0.449 0.0137 0.467 0.0143

VECM for the DM/$ spot and futures returns, given in Eq. (4). The sample period is June±August

1993, with for every day 1-min observations from 7:20±10:00 a.m. CST. Panel A uses the midpoint

of the last spot quote each minute, Panel B uses also the previous two spot quotes (if within

15 seconds) to ®rst determine the best bid and ask and then calculate the midpoint. Standard errors

are usually around 0.01, while White errors are similar to the standard errors.

a Denotes signi®cance at 1% level.

b Denotes signi®cance at 5% level.

pulse response functions illustrate what happens to the system if there is a one

standard deviation shock in either the spot or the futures market. The relevant

impulse±response functions are given in Fig. 2. A shock in the futures market

has clearly a much larger e€ect on the subsequent returns in the spot market

than vice versa, i.e. the e€ect of a shock in the spot market on the futures returns.

The upper and lower bounds of the information share of each market are

calculated according to expression (8). For Panel A this implies an information

share of 89.8±98.7% (Panel B: 88.1±98.6%) for the futures market, and 1.27±

10.2% for the spot market. Once again, the futures market is leading the quotes


358 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

Fig. 2. Impulse response functions derived from the VECM results in Table 3. The graph shows the

response of the futures returns to a shock in the spot returns (labelled `futures returns') and the response

of the spot returns to a shock in the futures returns (labelled `spot returns').

Table 4

Cross-correlations between the DM/$ spot and futures exchange rate returns

Full sample June July August

corr(Dfe t 4 ,Dse t ) 0.00364 [0.016] 0.0228 [0.033] 0.00425 [0.021] )0.0271 [0.022]

corr(Dfe t 3 ,Dse t ) 0.0764 a [0.016] 0.0631 b [0.031] 0.0905 a [0.025] 0.0809 a [0.022]

corr(Dfe t 2 ,Dse t ) 0.396 a [0.022] 0.398 a [0.028] 0.598 a [0.049] 0.159 a [0.025]

corr(Dfe t 1 ,Dse t ) 0.311 a [0.032] 0.515 a [0.036] 0.0800 [0.046] 0.263 a [0.027]

corr(Dfe t ,Dse t ) 0.159 a [0.019] 0.101 a [0.036] )0.0144 [0.017] 0.451 a [0.026]

corr(Dfe t ,Dse t 1 ) 0.0424 a [0.016] 0.0709 b [0.028] 0.00293 [0.021] 0.0438 [0.025]

corr(Dfe t ,Dse t 2 ) 0.00833 [0.014] 0.0213 [0.026] )0.00969 [0.019] 0.00894 [0.021]

Dse t and Dfe t are the prewhitened DM/$ spot and futures returns, respectively, using an AR(p) ®lter.

The sample period is June±August 1993, with for every day 1-min observations from 7:20±

10:00 a.m. CST. For the spot returns we employed Panel B. Panel B uses, in addition to the last

quote in each minute, also the previous two quotes (if within 15 seconds) to ®rst determine the best

bid and ask and then calculate the midpoint. Heteroscedasticity and autocorrelation consistent

(HAC) errors inside brackets.

a Denotes signi®cance at 1% level.

b Denotes signi®cance at 5% level.


M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 359

on Reuters FXFX page and new information is incorporated much faster into

the futures prices than into the spot quotes.

5.2. Cross-correlations

Before calculating the cross-correlations, we ®rst prewhiten the time series

using an AR(p) process following Pierce and Haugh (1977). 8 The results for

Panel B are given in the ®rst column of Table 4 (the results for Panel A are similar;

apparently, the direction of the skewing of the spot spreads is close to random

and, therefore, does not a€ect the results, and the ®ltering procedures

remove the di€erent autocorrelation patterns).

These results show that the futures market leads the spot quotes on Reuters

FXFX page signi®cantly up to 3 min, while there is only a small signi®cant 1min

lead the other way around. One possible explanation is that updating

Reuters screen takes some time, another explanation is that one cannot actually

trade against these quotes and as a result insu cient e€ort is taken to make

them e cient. Previous studies using Reuters quotes, however, claim that

banks have reputation considerations, and for our sample period Reuters

was the main information source available to all traders.

The results for the subsamples June, July and August in columns 2±4 in

Table 4 show that the general conclusions hold. The futures market is more ef-

®cient than the Reuters spot quotes.

6. The e€ect of high volatility periods

In this section we split the sample into two parts: one where volatility is relatively

high and one where volatility is at its normal level. In cases of high volatility

or prescheduled news announcements, traders prefer ®rst to trade and

only then update their Reuters quotes. However, traders will then also need

to closely follow the current developments in the market.

6.1. Prescheduled news announcements

Ederington and Lee (1993, 1995) investigate the volatility pattern surrounding

news announcements for several futures markets. One of them is the DM/$

futures contract traded at the CME. In the period 7:30±7:31 a.m. CST there is a

peak in the futures volatility, coinciding exactly with US macro-economic an-

8 For example for the ®rst column in Table 4 we use the estimated AR(p) processes from Table 1.

We also experimented with time-varying AR(p) ®lters. This gives similar results as the ones

provided here.


360 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

Fig. 3. Average volatility spot (based on Reuters quotes) and futures exchange rates for the ®rst

40 min of trading in Chicago, 1 June±31 August 1993.

nouncements like GNP and employment rates. Using the absolute values of the

futures and spot returns, the average taken over all trading days for each trading

minute results in Fig. 3 (only the ®rst 40 min of CME trading are included).

As expected, we observe in the period 7:30±7:31 a peak in the futures returns

volatility. The peak in the spot returns volatility based on Reuters quotes occurs 1

min later. So, on average (the volatility in) the spot returns are lagging by 1 min in

the case of prescheduled news announcements. The fact that there is only a 1 min

lag is somewhat surprising given the 3 min lead we found earlier. It seems that the

Reuters FXFX page is actually updated quite fast considering the fact that traders

®rst trade and only then update their quotes. Some traders may even temporarily

withdraw from the market to wait until the volatile period has passed.

To study the e€ect of prescheduled news announcements in greater detail,

we analysed two occasions where the news announcement resulted in a major

shock in the DM/$ exchange rate. On Friday, 4 June 1993, the announcement

included an increase in US jobs for the month May with 209,000 (while the expectation

was 155,000), and the unemployment rate had decreased slightly

(while predicted to be stable). According to the news reports this apparently

resulted in a fear for increased US in¯ation and an increase in the US interest

rate, strengthened by the strong economic quarter compared to the ®rst quarter

of 1993. In just a couple of minutes after the announcement the US dollar ap-


M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 361

preciated by 1 cent versus the Deutsche Mark. Our second news event took

place on Friday, 2 July 1993, when the US dollar depreciated after an increase

in non-agricultural employment of 13,000 instead of the expected 130,000.

Since the futures prices are labelled up to the minute only, we divide the futures

prices uniformly over each minute at equal time intervals (e.g. two futures

observations in minute 7:33 a.m. will become 7:33:20 and 7:33:40 a.m.). The

spot prices are multiplied by the cost-of-carry. This results in Fig. 4 where

we also include the spot spread.

On 4 June the futures market immediately reacted to the positive news in the

same minute as the news was released, 1 min later followed by the spot quotes.

Yet, quotes were still appearing on the Reuters FXFX page between 7:30 and

7:31 a.m. CST. Hence, the time-lag did not occur due to traders ®rst trading

and only then posting new quotes. Also, it is surprising to see that the bid±

ask spread only increases 1 min after the news release. One would expect

spreads to increase before the news release. Second July gives a similar picture.

The futures market is again the ®rst to react. In this case, however, it is obvious

that the futures market overreacted, since even before the spot rate starts to decrease,

the futures price moves up again. Once again there were spot quotes immediately

after the release and the spread only increased 2 min after the release.

6.2. High volatility periods

To investigate the e€ect of high volatility more formally, we divide our data

according to the rule given in Eq. (9). We experimented with several values for

the length of the intervals, N (5, 10 and 20 min), and the percentage of the standard

deviation, P (20% and 40%). The results are similar. We therefore only

give results for intervals of 5 min (N ˆ 5) and P equal to 20%, in Table 5.

The spill-overs between the futures and the spot markets are signi®cantly

stronger when volatility is high. This applies to both spill-over directions.

The signi®cant 1 min lead of the Reuters spot quotes actually disappears in

the low volatility panel, while it is larger in magnitude in the high volatility

panel. One explanation is that in the case of high volatility related to news,

one market might react faster, immediately followed by the other market.

When there is no directly related news, the markets might follow each other,

but this e€ect will be noticeably smaller. 9

9 Similar results are found when we split up the sample according to the time of the day, i.e. 7:20±

10:00 a.m. and 11:00 a.m.±14:00 p.m. CST. For the morning period we ®nd a 1-min signi®cant lead

of the spot quotes in Table 4, while there is no spot lead left in the afternoon. The futures lead

extends to 5 min for the afternoon period. From Fig. 1 it can be seen that trading activity decreases

once the European markets are closed. The fact that there is no signi®cant spot lead in the

afternoon period, can be attributed to the growing relative importance of the futures market once

the European traders retreat from the spot market.


362 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

Fig. 4. Prescheduled news announcements.


M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 363

Table 5

Cross-correlations between the DM/$ spot and futures exchange rate returns in high and low volatility

periods

Panel I: Low volatility Panel II: High volatility

corr(Dfe t 4 ,Dse t ) )0.0361 [0.014] 0.0349 [0.027]

corr(Dfe t 3 ,Dse t ) 0.0218 [0.013] 0.117 a [0.026]

corr(Dfe t 2 ,Dse t ) 0.296 a [0.016] 0.469 a [0.034]

corr(Dfe t 1 ,Dse t ) 0.244 a [0.015] 0.359 a [0.052]

corr(Dfe t ,Dse t ) 0.107 a [0.014] 0.193 a [0.030]

corr(Dfe t ,Dse t 1 ) 0.00117 [0.013] 0.0688 a [0.024]

corr(Dfe t ,Dse t 2 ) )0.0213 [0.012] 0.0274 [0.020]

Dse t and Dfe t are the prewhitened DM/$ spot and futures returns, respectively, using an AR(p) ®lter.

The sample period is June±August 1993, with for every day 1-min observations from 7:20±

10:00 a.m. CST. The data are split up according to the rule in Eq. (9), with P ˆ 0.20 and N ˆ 5. This

results in 2935 observations in the high volatility panel and 7400 observations in the low volatility

panel. For the spot returns we employed Panel B. Panel B uses, in addition to the last quote in each

minute, also the previous two quotes (if within 15 seconds) to ®rst determine the best bid and ask

and then calculate the midpoint. Heteroscedasticity and autocorrelation consistent (HAC) errors

inside brackets.

a Denotes signi®cance at 1% level.

b Denotes signi®cance at 5% level.

7. A simple trading strategy

Having established a statistically signi®cant lead of the futures market over

the spot quotes, it is interesting to investigate its economic signi®cance. Of

course the underlying test would be more reliable if we would have quotes from

brokers at which we could certainly trade. On the other hand, Reuters FXFX

page quotes usually include the broker's quotes, and thus the underlying strategy

overvalues the trading costs. The spread of the broker can reduce to 1 tick,

while the minimal spread on Reuters FXFX page is 5 ticks. For our sample period

in many cases the spread is 10 ticks or even 15 ticks. Especially when the

spread is skewed to one side, the test will overestimate the costs as compared to

reality.

In our test we employ the following simple trading strategy:

Minute t Observe whether jDFtj P f

Minute t ‡ 1 Buy at St‡1;ask if DFt P f and St‡1;ask St‡1;bid 6 s

…case 1†

Sell at St‡1;bid if DFt 6 f and St‡1;ask St‡1;bid 6 s

…case 2†

Minute t ‡ 3 Sell at St‡3;bid …case 1† Profit: St‡3;bid St‡1;ask

Buy at St‡3;ask …case 2† Profit: St‡1;bid St‡3;ask:

…10†


364 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

Thus, if it is observed at t that the absolute futures return exceeds a certain

threshold f, then we buy/sell in the spot market at the ®rst observation in

minute t + 1 appearing on Reuters screen if the spread is not too high,

i.e., less than a threshold s. The position is liquidated by selling/buying in

the spot market at the ®rst observation in minute t + 3, regardless of the

spread.

Applying this strategy to the total sample of three months for the 7:20±

10:00 a.m. CST window results in Table 6. The ®rst number in columns 2±4

indicates the total pro®t in DeutscheMarks per US dollar. Each time the strategy

is applied, it is either ``winning'' or ``losing''. The ®rst number inside brackets

denotes the number of times the strategy was winning, the second number

denotes the number of times the strategy was losing.

The results show that, despite the huge losses induced by the overestimated

spot spreads, substantial gains could have been made (assuming one can

always trade against the Reuters quotes immediately after appearance). For

example, in the case of s equal to 0.0010 (10 ticks) and buying (selling)

when the futures return is at least 5 ticks (is below )5 ticks), the above strategy

would have earned 591 ticks in the spot market (winning 70 times, losing 34

times of the 104 times we apply the strategy). If the transaction size is 5 million

US dollar each time, then 1 tick is worth 500 Deutsche Marks. Thus,

591 ticks amounts to 295,500 Deutsche Marks. The standard error inside parentheses

show that this result is signi®cantly di€erent from zero. This also applies

to the majority of other entries in Table 6.

Table 6

Pro®ts from a simple strategy in the DM/$ exchange rate

|DFt| P f Spread 6 5 ticks Spread 6 10 ticks Spread 6 15 ticks

0.0001 )0.0915 [53;209] (0.0127) )0.7897 [647;1920] (0.0404) )1.6003 [845;3019] (0.0571)

0.0002 )0.0084 [26;58] (0.0080) )0.0831 [399;686] (0.0283) )0.3422 [612;1249] (0.0396)

0.0003 0.0099 [12;11] (0.0058) 0.0905 [195;162] (0.0214) 0.0660 [321;346] (0.0293)

0.0004 0.0067 [5;4] (0.0050) 0.0658 [85;42] (0.0174) 0.1084 [152;96] (0.0232)

0.0005 0.0055 [3;4] (0.0048) 0.0591 [70;34] (0.0168) 0.1010 [131;77] (0.0222)

0.0006 0.0059 [2;2] (0.0039) 0.0420 [31;11] (0.0116) 0.0910 [68;26] (0.0187)

0.0007 )0.0003 [0;2] (0.0002) 0.0285 [21;5] (0.0092) 0.0657 [40;15] (0.0166)

0.0008 0.0000 [0;0] ()) 0.0260 [10;0] (0.0089) 0.0484 [20;8] (0.0148)

0.0009 0.0000 [0;0] ()) 0.0251 [9;0] (0.0088) 0.0489 [19;7] (0.0147)

0.0010 0.0000 [0;0] ()) 0.0199 [7;0] (0.0079) 0.0422 [15;6] (0.0140)

Cumulative returns from the trading strategy in Eq. (10). A spot position in DM/$ is started based

on the ®rst observation in the minute after a futures increase (decrease) and held for 2 min.

Transaction costs are incurred by using the spot bid and ask quotes. Inside brackets the number of

times the strategy won and the number of times the strategy lost, respectively. Inside parentheses

the standard errors of the returns.


8. Conclusion

M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366 365

This study compares the DM/$ futures prices in Chicago with Reuters foreign

exchange page displaying spot exchange rate quotes of many di€erent

banks. Even though Reuters' FXFX page is mainly indicative, reputation considerations

might induce banks to trade at their quotes when asked to within

reasonable time after the appearance on the screen. Also, broker's quotes are

usually within the Reuter's quotes. Interestingly, we ®nd that the futures market

is leading the quotes on Reuters FXFX page up to 3 min. There is only a

small lead the other way around. This informational lead is supported by the

information share as proposed by Hasbrouck (1995) showing that the information

share of the futures market exceeds 89%.

In the case of prescheduled news announcements the futures lead is reduced

to approximately 1 min. Spot traders might then ®rst trade and only later update

quotes on Reuters FXFX page. However, a few examples show that Reuters

FXFX page still contains new quotes with spreads of at most 10 ticks

immediately after the news release.

Not only does this question the assumption that one can trade against the

quotes on Reuters screen, it also suggests an alarming ine ciency as an informational

tool. Our results suggest that it deserves further attention to investigate

whether spot traders should more closely watch the futures market. 10

Especially banks putting quotes on the Reuters FXFX page should take current

developments in the futures market (if open) into consideration.

Acknowledgements

The authors would like to thank Yuan-chen Chang, Michel Dacorogna,

Theo Nijman, Antoon Pelsser, Piet Sercu, Ton Vorst, Siegfried Trautmann,

Casper de Vries, two anonymous referees, participants of the 13th International

Conference of the French Finance Association in Geneva (1996), and participants

of the 23rd European Finance Association meeting in Oslo (1996) for

useful comments. We are also grateful to the ABN-AMRO bank for allowing

us to visit their dealing-room in Amsterdam and speak with some of their trad-

10 Nowadays most trading occurs through the brokers, and Reuters claims that its electronic

broking system D2000 has a still growing market share. In the Financial Times (30 June 1997) it

was recently reported that over the ®rst quarter of 1997 Reuters had a daily average of 21,000

currency deals. Its main rival, EBS, had an average of 24,000 currency deals. Therefore, for future

research it is interesting to obtain data from both these brokerage systems (up to now they seem

hard to get for a reasonable period of time) and compare them with each other and with the futures

market in Chicago.


366 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366

ers. The Erasmus Center for Financial Research is gratefully acknowledged for

®nancial support. All remaining errors are our own responsibility.

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