The ine ciency of Reuters foreign exchange
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
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
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: firstname.lastname@example.org.
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 -
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
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.
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
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
Autocorrelation in the DM/$ spot and futures exchange rate returns
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).
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
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.
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.
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
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
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
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
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
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:
jDf1j > 1 X
jDftj ‡ P
t : …9†
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.
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.
356 M. Martens, P. Kofman / Journal of Banking & Finance 22 (1998) 347±366
Augmented Dickey Fuller tests and cointegration
Panel A Panel B Critical
5.1. Cointegration and error correction
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†
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.
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
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').
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.
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
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
Cross-correlations between the DM/$ spot and futures exchange rate returns in high and low volatility
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
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
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
Sell at St‡1;bid if DFt 6 f and St‡1;ask St‡1;bid 6 s
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:
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
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.
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.
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.
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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