Journal of International Economics 75 (2008) 93 – 109
www.elsevier.com/locate/econbase
Order flow and exchange rate dynamics in electronic
brokerage system data ☆
David W. Berger a , Alain P. Chaboud b , Sergey V. Chernenko c ,
Edward Howorka d , Jonathan H. Wright b,⁎
a
b
Department of Economics, Yale University, New Haven, CT 06520, USA
Board of Governors of the Federal Reserve System, Washington, DC, 20551, USA
c
Harvard Business School, Boston, MA, 02163, USA
d
EBS, 535 Madison Avenue, New York, NY 10022, USA
Received 23 August 2007; accepted 19 October 2007
Abstract
We analyze the association between order flow and exchange rates using a new dataset representing a majority of global interdealer
transactions in the two most-traded currency pairs at the one minute frequency over a six-year time period. This long span of highfrequency data allows us to gain new insights about the joint behavior of these series. We first confirm the presence of a substantial
association between interdealer order flow and exchange rate returns at horizons ranging from 1 min to two weeks, but find that the
association is substantially weaker at longer horizons. We study the time-variation of the association between exchange rate returns and
order flow both intradaily and over the long term, and show that the relationship appears to be stronger when market liquidity is lower.
Overall, our study supports the view that liquidity effects play an important role in the relationship between order flow and exchange rate
changes. This by no means rules out a role for order flow as a channel by which fundamental information is transmitted to the market, as
we show that our findings are quite consistent with a recent model by Bacchetta and Van Wincoop (2006: Can information heterogeneity
explain the exchange rate determination puzzle? American Economic Review, 96, pp. 552–576.) that combines both liquidity and
information effects.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Order flow; Foreign exchange; High-frequency data; News announcements; Micro exchange rate economics; Private information
JEL classification: F31; G14
☆
We are grateful to Benjamin Chiquoine for excellent research assistance and to Jon Faust, Mico Loretan, Richard Lyons, Michael Moore, Dagfinn
Rime, Krista Schwarz, Eric van Wincoop and two anonymous referees for very helpful comments on earlier drafts of this manuscript. The views in
this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal
Reserve System or of any person associated with the Federal Reserve System.
⁎ Corresponding author. Tel.: +1 202 452 3605; fax: +1 202 452 2301.
E-mail addresses: david.berger@yale.edu (D.W. Berger), alain.p.chaboud@frb.gov (A.P. Chaboud), schernenko@hbs.edu (S.V. Chernenko),
ehoworka@ebs.com (E. Howorka), jonathan.h.wright@frb.gov (J.H. Wright).
0022-1996/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.jinteco.2007.10.004
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D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
1. Introduction
A strong positive contemporaneous association between exchange rate returns and order flow has been documented
in many recent empirical studies. Evans and Lyons (2002), for instance, in a seminal paper, reported that a regression of
Deutsche mark/dollar daily returns on daily order flow yielded an R2 in excess of 60%. Other authors have since
confirmed the association between order flow and returns at daily or intradaily frequencies using several other foreign
exchange datasets. However the various papers have come to very different conclusions as to the role of order flow in
price discovery in foreign exchange markets and the permanence of its impact.
Froot and Ramadorai (2005) draw a distinction between two possible views of the relationship between exchange
rates and order flow. The first – which they term the “strong flow-centric” view – holds that order flow is correlated
with changes in the fundamental value of a currency as it transmits fundamental macroeconomic information to the
market. The second – which they term the “weak flow-centric” view – holds instead that order flow is correlated with
the deviation of the exchange rate from its fundamental value, but not with the fundamental value itself. A deviation of
the exchange rate from its fundamental value might, for example, come from hedging demand shocks, liquidity effects,
or the overreaction of investors, but is in any case temporary. It is also possible, however, to have elements of both the
strong and weak flow-centric views, in which order flow is informative about fundamentals but is associated with
transitory exchange rate movements as well. This is the case in the model of Bacchetta and Van Wincoop (2006), which
provides an analytical framework with both portfolio-balance and informational effects of order flow on exchange
rates. Their model is a dynamic rational expectations model in which each investor receives a noisy signal about future
fundamentals. The average signal across all investors is the true future fundamental value. Rational investors would
infer the average signal of others from observed exchange rate changes, except that the exchange rate is also affected by
hedging demand that is unrelated to fundamentals. Investors must therefore attempt to infer whether observed
exchange rate movements represent information about future fundamentals or about hedge trades. In the rational
expectations equilibrium, order flow will be correlated with both the future fundamentals (the information channel), but
also with hedging demand (the liquidity channel). The two channels are intertwined in this model: Order flow from
hedging demand shocks can have a substantial short-run effect on the exchange rate, but this is largely because of the
fact that information about future fundamentals can also be revealed through order flow.
These contrasting views about the relationship between order flow and exchange rates have different implications
about the association between these series at different horizons. Under the strong flow-centric view, the effect of order
flow on exchange rates is permanent, as fundamental macroeconomic information is revealed to the market and becomes
embedded in prices via order flow. Under the weak flow-centric view, where order flow does not convey information
about macroeconomic fundamentals, order flow is associated only with transitory exchange rate movements. In the
model of Bacchetta and Van Wincoop (2006) the relationship between order flow and exchange rates can be stronger or
weaker at long horizons than at short horizons depending on the parameter values. The strong flow-centric model of
Killeen et al. (2006) implies that cumulative order flow and exchange rates are nonstationary but cointegrated, so that
there exists a stable long-run equilibrium relationship between these time series. Weak flow-centric models, such as
Froot and Ramadorai (2005) and Breedon and Vitale (2004), do not, however, imply cointegration. And, in the model of
Bacchetta and Van Wincoop (2006), although order flow reflects, in part, fundamental information, cumulative order
flow and exchange rates are not cointegrated.1
Turning to the data, to reach these differing conclusions researchers have used datasets that vary in frequency, span,
period of coverage, and segment of the market in which the order flow is recorded. Papers arguing for the strong-flow
centric view include Evans and Lyons (2002, 2005, 2006), Love and Payne (in press) and Killeen et al. (2006), among
others. Authors generally favoring the weak flow-centric interpretation include Froot and Ramadorai (2005) and Breedon
and Vitale (2004) who conclude that “the strong contemporaneous correlation between order flow and exchange rates is
mostly due to liquidity effects.” The data used in the seminal work of Evans and Lyons (2002), for instance, was 4 months
of high-frequency data from the Reuters direct-dealing interdealer electronic platform in 1996, and the same data were used
in Evans and Lyons (2006) to study the role of order flow at times of news releases. Breedon and Vitale (2004) studied
high-frequency brokered electronic interdealer data, spanning 6 months in 2000 and 2001, to reach conclusions different
1
See Appendix D of Bacchetta and Van Wincoop (2006). The intuition is that hedge trade shocks permanently affect cumulative order flow but
do not permanently affect the level of the exchange rate. Note that cumulative order flow, exchange rates, and cumulative hedging demand shocks
are, however, cointegrated.
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
95
from Evans and Lyons. Long spans of daily customer-to-dealer order flow, each from a single institution, were used by
Evans and Lyons (2005) and Froot and Ramadorai (2005) to reach their opposite conclusions. Overall, for major currency
pairs, the existing literature has analyzed either relatively short spans of high-frequency interdealer data from various
trading platforms or longer samples of daily single-institution customer-to-dealer data.2
The structure of the foreign exchange market has, however, changed considerably over the past few years, yielding new
data that may help resolve some of this uncertainty. Since the late 1990s, two electronic platforms have taken a leading role
in interdealer spot trading, one offered by EBS and the other offered by Reuters, both electronic limit order books.
Importantly, on a global scale, interdealer trading in each major currency pair has become very highly concentrated on one
of the two systems. Of the most-traded currency pairs, the top two, euro–dollar and dollar–yen across the globe have for
several years been traded primarily on EBS while the third, sterling–dollar, is traded primarily on Reuters. As a result, for
instance, the current EBS euro–dollar exchange rate is now universally used to generate customer quotes and to price
derivatives.
In this paper, we analyze a new dataset of minute-by-minute order flow and exchange rate returns on the EBS platform
for the euro–dollar and dollar–yen currency pairs from January 1999 through December 2004, data which have not been
previously available. This dataset represents a clear majority of global trading in the interdealer spot market for the two
major currency pairs over a span of six years at the intradaily frequency, and is unique in having all of these features.3
Using these data, we find a strong contemporaneous association between exchange rate returns and interdealer order
flow at intradaily and daily horizons: A regression of daily exchange rate returns on contemporaneous daily order flow
yields a significant positive coefficient with an R2 of about 45% for euro–dollar and 50% for dollar–yen, in line with the
previous results. However, our long span of data gives us more power to test the strength of the association at longer
horizons. We find that there is an association at two- and three-month horizons, but that it is notably weaker. We test for
cointegration between cumulative order flow and exchange rates, and generally find that these series are not
cointegrated. However, we show that the pattern of the strength of the relationship between order flow and exchange rate
movements over different horizons is consistent with Bacchetta and Van Wincoop's (2006) model for certain parameter
values. We document an intradaily pattern to the association between one-minute order flow and one-minute exchange
rate returns—the relationship is weakest at times when markets are most active. Using rolling regressions, we also
consider lower-frequency time-variation in the relationship between order flow and exchange rate returns, finding in
particular that the association seems to have weakened somewhat since 2001, likely consistent with an improvement in
market liquidity. Finally, we study patterns in order flow around important scheduled U.S. macroeconomic data releases.
Overall, the analysis of this new dataset points to an important role for liquidity effects in the relationship between
order flow and exchange rates. Evidence for this includes the fact that the relationship between order flow and
exchange rates weakens under time aggregation and that the relationship appears to be strongest at times when market
liquidity is lowest. We conclude that the evidence comes down against a purely information-based explanation of the
relationship between order flow and exchange rates, but is entirely consistent with models that combine both liquidity
and information-based explanations.
The plan for the remainder of this paper is as follows. In Section 2, we describe the data that we use in this paper.
Section 3 reports our results on the relationship between order flow and exchange rate movements at different horizons,
cointegration tests between these two cumulated series, and the calibration of the Bacchetta and Van Wincoop (2006)
model. Section 4 considers non-linearity and time-variation in the link between order flow and exchange rates,
including variation over the full sample and intradaily patterns in the association. Section 5 studies the patterns in order
flow around important scheduled U.S. macroeconomic data releases. Section 6 concludes.
2. The data
The EBS data that we use in this study consist of three time series at the one-minute frequency: the volume (in base
currency) of buyer-initiated trades in each minute, the volume of seller-initiated trades in each minute, and the one2
Longer spans of comprehensive interdealer data at the daily frequency have been used for some smaller currency pairs, including Bjonnes et al.
(2005) and Gereben et al. (2006). These papers studied bilateral exchange rates between the euro and the Swedish krona and the euro and the
Hungarian forint, respectively.
3
Killeen et al. (2006) and Hau et al. (2002) have studied EBS order flow data at the daily frequency from 1998 and 1999. Breedon and Vitale
(2004) collected and studied six months of partial EBS order flow data from 2000 and 2001.
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D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
minute exchange rate returns. The data span January 1999 to December 2004 for the dollar–yen and euro–dollar
currency pairs. These EBS data, which are proprietary and confidential, do not contain any information on the identity
of market participants.4
A buyer-initiated trade is a transaction where a quote offering to sell euros for dollars, or dollars for yen, placed in
the EBS system by one dealer is dealt on by another dealer, who is then seen as the aggressor, or the initiator of the
transaction (and buys euros or yen, respectively). A seller-initiated trade is of course defined similarly. The trades in our
dataset are signed as buyer-initiated or seller-initiated by the EBS computer system, so we do not have to rely on an
algorithm to estimate the direction of trade. Order flow is defined as the difference between the volume of buyerinitiated trades and that of seller-initiated trades, measured in base currency. A positive value of order flow therefore
indicates a net excess of buyer-initiated trades.5
An important aside on the definition and measurement of order flow: The assumption underlying the theoretical
information-based explanations of the relationship between order flow and exchange rates is that some agents are better
informed and that those agents take the active side of foreign exchange transactions, i.e. they buy or sell at prices posted
by others. It seems reasonable to assume that informed agents should generally prefer to trade by market orders in fastmoving liquid markets (e.g. Harris, 1998). Still a caveat is in order: It is entirely possible that some informed agents
might choose to place an aggressively-priced limit order instead. In addition, it is possible that the information
asymmetry we seek to capture could be more prevalent in some other parts of the foreign exchange market, such as the
dealer-to-customer market, than in the interdealer market captured here. There is thus a possible slippage between the
theoretical concept of order flow (the net of informed trades) and the way order flow is measured (the net of buyerinitiated trades in our market) which could weaken the relationship between order flow and exchange rates. This is also
true in all other empirical work on the microstructure approach to exchange rates. Indeed, our dataset has important
advantages in terms of data quality in its long span, its very high frequency, the fact that it records the order flow in
value terms rather than number of trades (rather than number of deals as in Evans and Lyons, 2002), and the fact that it
covers a vast majority of global trades in the interdealer market in the two largest currency pairs.
We exclude from our sample all observations collected from Friday 17:00 New York time through Sunday 17:00
New York time, as foreign exchange trading activity during these hours is minimal. We also drop certain holidays and
days of unusually light volume: December 24–December 26, December 31–January 2, Good Friday, Easter Monday,
Memorial Day, Labor Day, Thanksgiving and the following day, and July 4 (or, if this is on a weekend, the day on
which the Independence Day holiday is observed).6 To construct a minute-by-minute price series, we use the midpoint
of the best bid and the best ask price in the system at the end of each minute (the last millisecond) and calculate
continuously compounded one-minute returns. That is, we compute our returns as 10,000 times the one-minute change
in the log exchange rate. Our returns therefore have the interpretation of (approximately) the percentage change in the
exchange rate multiplied by 100, and so the units can be thought of as basis points of exchange rate movements.
Table 1 reports some summary statistics. The table shows the mean, standard deviation, minimum, and maximum of
the order flow at several frequencies ranging from 1 min to one month. The proportion of observations for which the
order flow is positive at each frequency is also shown. For both currency pairs, the mean order flow is positive.
Although it is only very slightly positive at the one-minute frequency, it turns out that if one aggregates to, for instance,
the daily frequency, about 70% of days have positive order flow. This is somewhat surprising and puzzling, but the
same phenomenon has been found by other authors including Dunne, Hau and Moore (2004) and Evans and Lyons
(2002) and, in analysis of the GovPX Treasury market trading platform, by Brandt and Kavajecz (2004).7 Table 2
reports autocorrelation coefficients for order flow at frequencies ranging from 1 min to one month. Order flow tends to
be positively autocorrelated at the highest frequencies. We also note some first-order autocorrelation in daily and
weekly order flow for both currencies. This positive autocorrelation in order flow could owe to “hot potato” trading
4
See Chaboud et al. (2004) for details of the EBS trading system.
In the study of equity markets, much of the microstructure literature has focused on the signed-order count measure. We also analyzed EBS
order flow data based on counts of trades per minute, and the results, which are very similar, are available from the authors. In practice, as the size of
most individual trades on EBS are within a small range (almost always below $10 million), and the average trade size varies little over time, it is not
surprising that the two order flow measures give us very similar results.
6
Similar conventions were adopted by Andersen et al. (2003).
7
The phenomenon has also been noted in equity trading platforms, where it is less surprising, as investors naturally tend to build long equity
positions. Constraints on short sales may also contribute to the recorded imbalance in equity markets (Diether et al., 2005).
5
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
97
Table 1
Order flow summary statistics
Horizon, h
1 min (n = 2,152,800)
5 min (n = 430,560)
1 h (n = 35,880)
1 day (n = 1495)
1 week (n = 299)
1 month (n = 72)
Mean
Standard deviation
Min
Max
Proportion positive
Mean
Standard deviation
Min
Max
Proportion positive
Mean
Standard deviation
Min
Max
Proportion positive
Mean
Standard deviation
Min
Max
Proportion positive
Mean
Standard deviation
Min
Max
Proportion Positive
Mean
Standard deviation
Min
Max
Proportion positive
Euro
Yen
0.4471
19.63
− 457
615
0.52
2.235
52.86
− 1049
1158
0.53
26.82
205.1
− 1795
2150
0.56
643.8
1177
− 3407
4755
0.72
3219
2897
− 6494
13,644
0.87
13,367
6807
− 2639
31,375
0.99
0.4491
13.71
− 639
667
0.53
2.245
38.62
− 1423
1120
0.53
26.95
177.4
− 3319
3324
0.57
646.7
1252
− 3984
7750
0.70
3234
3474
− 10,109
18,596
0.82
13,428
8207
− 1079
33,010
0.96
This table reports some summary statistics for order flow (in millions of base currency) at the one-minute frequency, and aggregated to five-minute,
hourly, daily, weekly and monthly frequencies. Proportion positive means the number of observations at that frequency for which the measured order
flow is positive, divided by the total number of observations for which it is nonzero.
(Lyons, 1997) where foreign exchange traders pass a large order among themselves, or perhaps to order-splitting, as a
large order is split into a number of smaller orders of the same sign that are executed sequentially.
3. Order flow and exchange rate returns: the basic relationship
To study the contemporaneous relationship between order flow and exchange rate movements at various levels of
temporal aggregation, we ran regressions of the form
rt;h ¼ a þ bOt;h þ et;h
ð1Þ
where rt,h refers to the returns over some period of length h ending at time t and Ot,h refers to the order flow over that
same period. We refer to the period h as the horizon of the regression.8 It can be 1 min, or we can temporally aggregate
both the returns and the order flow to get a longer horizon relationship. Note that Eq. (1) includes an intercept, meaning
that our regressions effectively demean the order flow series.
Fig. 1 plots the estimated slope coefficients and R2s from this regression against h, for the euro–dollar and dollar–
yen currency pairs. The plots of the estimated slope coefficients include 95% confidence intervals, constructed using
heteroskedasticity-robust standard errors. At the one-minute horizon, the coefficient is significantly positive and the R2
is 36% for euro–dollar and 30% for dollar–yen. An excess of buyer-initiated trades is associated with a rising price,
8
At horizons longer than one day, we use overlapping daily observations of the h-period returns and cumulative order flow, and control for the
resulting serial correlation in the errors by using Newey–West standard errors.
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D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
Table 2
Order flow autocorrelation coefficients
Horizon, h
1 min
5 min
1h
1 day
1 week
1 month
Lead
Euro
Yen
1
2
3
10
30
1
2
3
10
30
1
2
3
10
30
1
2
3
10
30
1
2
3
10
30
1
2
3
0.1956⁎⁎⁎
0.0779⁎⁎⁎
0.0376⁎⁎⁎
0.0047⁎⁎⁎
−0.0039⁎⁎⁎
0.0873⁎⁎⁎
0.0125⁎⁎⁎
0.0064⁎⁎⁎
0.0047⁎⁎⁎
−0.0011
0.0096⁎
−0.0025
−0.0058
0.0032
−0.0081
0.1067⁎⁎⁎
0.0245
0.0295
0.0265
0.0300
0.1358⁎⁎⁎
0.0803
0.0245
−0.0400
0.0805
0.2365⁎⁎
0.0803
−0.1474
0.2253⁎⁎⁎
0.1137⁎⁎⁎
0.0775⁎⁎⁎
− 0.0012⁎
− 0.0009
0.1839⁎⁎⁎
0.0742⁎⁎⁎
0.0550⁎⁎⁎
0.0244⁎⁎⁎
0.0093⁎⁎⁎
0.1720⁎⁎⁎
0.0756⁎⁎⁎
0.0507⁎⁎⁎
0.0235⁎⁎⁎
0.0118⁎⁎
0.1902⁎⁎⁎
0.1143⁎⁎⁎
0.0329
0.0280
− 0.0223
0.1861⁎⁎⁎
0.0829
0.0189
0.1029⁎
− 0.0673
0.0089
0.0891
0.2431⁎⁎
This table reports autocorrelation coefficients for order flow at one-minute, five-minute, hourly, daily, weekly and monthly frequencies. We report
results for leads of 1, 2, 3, 10, and 30 intervals. ⁎, ⁎⁎ and ⁎⁎⁎ indicate statistical significance of an individual coefficient at the 10%, 5% and 1% level,
respectively.
with an order imbalance of 1 billion (of the base currency) estimated to be associated with a bit more than a half-percent
appreciation (precisely 54 basis points in euro–dollar and 72 basis points in dollar–yen). At the daily horizon, the R2s
are about 50%, and the estimates of β are very significantly positive, about 40 basis points per billion for each currency
pair, only a bit less than the Evans and Lyons (2002) estimates. However, at the monthly horizon, the R2s fall to about
20% and 30% for the euro–dollar and dollar–yen currency pairs respectively, and β is estimated to be about 20 basis
points. At the two- and three-month horizons, the R2s continue to decline. The pattern is clear—the association
between order flow and exchange rate returns is strong at the intradaily, daily, and weekly horizons, but then declines
gradually at longer horizons.9
Fig. 2 shows cross-correlograms of returns and order flow for both currency pairs, both weekly and monthly, with
leads and lags of 30 weeks and 6 months, respectively. Critical values for these cross-correlations to be significantly
different from zero at the 5% level are also shown. The strong positive contemporaneous association between returns
and order flow found in the regression results is obvious. However, we also note the presence of negative crosscorrelations between returns and order flow at a number of leads and lags in both currency pairs, often statistically
significant, particularly at the monthly frequency. The results are also consistent with those displayed in Fig. 1, with the
9
We also ran regression (1) at the same horizons using excess returns instead of simple returns as a dependent variable, i.e. accounting for interest
rate differentials over horizons of one day or more. LIBOR rates in dollar, euro, and yen were used to calculate excess returns. The results at all
horizons, available from the authors on request, were almost identical to the estimates obtained with simple returns. This is not surprising given the
typical relative magnitudes of interest differentials (small) and exchange rate movements (large) over these horizons in these currency pairs, in
addition to the well-known failure of uncovered interest parity.
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
99
Fig. 1. Estimates of excess returns regressed on order flow at different horizons. Dashed line shows implied population R-squareds from the fitted
Bacchetta van Wincoop Model.
negative autocorrelations at some weekly and monthly intervals linked to the lower R2s observed at longer horizons in
the estimation of Eq. (1).
3.1. Cointegration analysis
Fig. 3 shows a daily time series of the euro–dollar and dollar–yen exchange rates and of the corresponding
demeaned cumulative order flows from 1999 through 2004. The relationship between the two time series appears
consistent with the regression results displayed in Fig. 1. An association between high-frequency movements in order
flow and exchange rates is often apparent. Indeed, there are many periods where cumulative order flow and exchange
rates track each other quite closely. However, the low-frequency association between cumulative order flow and
exchange rate returns seems to be much weaker. Evans and Lyons (2002), for instance, showed graphs of cumulative
order flow and exchange rates over a four month period and found a remarkably close relationship. Studying Fig. 3,
there are clearly periods with a similarly tight link between cumulative order flow and exchange rates, some lasting
perhaps a year or more, but there are also obvious periods of similar length with little or no association.
Cointegration analysis allows us to be more precise about the long-run relationship between cumulative order flow
and exchange rates. Two time series are said to be cointegrated if they are both individually nonstationary, but there
exists a linear combination of them that is stationary. This can be interpreted as a stable long-run equilibrium
relationship. The model of Killeen, Lyons and Moore (2006), for instance, implies that cumulative order flow and
exchange rates are cointegrated, whereas weak flow-centric models and the model of Bacchetta and Van Wincoop
(2006) do not imply cointegration.
Table 3 reports the results of Augmented Dickey–Fuller tests for unit roots in the cumulative order flow and
exchange rate series. In all cases, the unit root hypothesis is not rejected, meaning that we conclude that these series are
both nonstationary for both currency pairs. Table 3 also reports the results of two standard tests for cointegration, both
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D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
Fig. 2. Cross-correlograms of returns and order flow. In each panel, the correlation at lag zero denotes the contemporaneous correlation between
returns and order flow. Positive lags denote correlations between order flow and future returns. Negative lags denote correlations between order flow
and past returns. Dashed lines show 5% critical values for null of zero.
with a null hypothesis of no cointegration. The first is the Engle–Granger test (Engle and Granger, 1987), which applies
an Augmented Dickey–Fuller test to the residuals from the putative cointegrating regression.10 The second is the trace
test of Johansen (1988). As can be seen in Table 3, the Augmented Dickey–Fuller test fails to reject the null hypothesis
of no cointegration for either the euro or the yen. The Johansen trace test rejects (i.e. finds cointegration) for the euro,
but not for the yen. Gonzalo and Lee (1998) discuss the relative merits of the Engle–Granger and Johansen approaches
to testing for cointegration, and find that they often give conflicting results with the Engle–Granger approach being
more robust.11 We conclude from this exercise that the balance of evidence is against a finding of cointegration
between exchange rates and cumulative order flow.
10
The cointegrating regression is estimated by the fully modified least squares estimator of Phillips and Hansen (1990), in order to eliminate
second order asymptotic bias.
11
The Monte-Carlo simulations in Gonzalo and Lee (1998) find that the Johansen test can find cointegration where none is in fact present
(spurious cointegration).
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
101
Fig. 3. Exchange rate level and cumulative order flow (Daily: 1999–2004). The dotted line is the order flow series; the solid line is the exchange rate level.
3.2. Interpretation and calibration
Our results so far point to an important role for liquidity effects in the relationship between order flow and exchange
rates and the evidence comes down against a purely information-based interpretation of this relationship. However, the
R2s near 0.2 that we find for our return-order flow regressions at horizons of two or three months would have been
viewed as a great advance in explaining exchange rate movements prior to the work of Evans and Lyons. Therefore, our
evidence appears most consistent with an analytical framework that combines both liquidity and information-based
explanations, such as the model of Bacchetta and Van Wincoop (2006). That model predicts no cointegration between
cumulative order flow and exchange rates, matching our findings, and can, depending on the parameter values,
generate a pattern of R2s that declines under temporal aggregation.
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Table 3
Unit root and cointegration tests
Euro
Log exchange rate
Cumulative order flow
Engel–Granger
Johansen trace test
Unit root test (Augmented Dickey–Fuller)
− 0.53
− 1.34
Cointegration test
− 0.38
36.30⁎⁎⁎
Yen
− 1.77
− 0.58
− 1.75
3.74
All tests allow for an intercept, but not a time trend. The Augmented Dickey–Fuller test includes one lag. Both cointegration tests take no
cointegration as the null hypothesis. The Engel–Granger cointegration test applies an augmented Dickey–Fuller test to the residuals from the
cointegrating regression, allowing for one lag. The cointegrating regression was estimated by the fully modified least-squares estimator of Phillips
and Hansen (1990). The Johansen trace test is based on maximum-likelihood estimation of a cointegrating vector autoregression with one lag. ⁎, ⁎⁎
and ⁎⁎⁎ indicate statistical significance of an individual coefficient at the 10%, 5% and 1% level, respectively.
We calibrated the model of Bacchetta and Van Wincoop (2006) to the observed empirical relationship in the EBS
data between R2 and the horizon of the regression, h. We conducted a grid search over the parameters of their model
and selected the parameter values that minimized the sum over h of the squared discrepancies between the theoretical
model-implied R2s and their estimated counterparts, for the two currency pairs separately.12 The resulting calibrated
parameter values are shown in Table 4 and the implied R2s are plotted in Fig. 1 as dashed lines in the lower panel. With
this parameterization, the relationship between R2 and h fits the data very closely for both currency pairs. The R2s
declines with h, in contrast to the benchmark parameterization of Bacchetta and van Wincoop, where the R2s instead
increased with h. In our calibration, the variance of hedging shocks and the variance of the noise in investors' signals
are both larger relative to the variance of shocks to fundamentals than in their benchmark parameterization. We would
prefer not to draw strong conclusions about the values of specific parameters from this simple calibration exercise.
Rather, the calibration is intended to show how the model can generate a pattern of R2 declining with temporal
aggregation and to underscore the importance of portfolio balance shocks in explaining the observed relationship
between order flow and exchange rate changes.
4. Non-linearity and variation over time of the return/order flow relationship
Our long span of data at very high frequency also allows us to study the linearity and the stability of the relationship
between exchange rate returns and order flow. Fig. 4 shows scatterplots of the returns and order flow in the euro–dollar
and dollar–yen currency pairs at the one-minute and one-day frequencies, along with fitted lines from the OLS
regressions and non-parametric estimates of the relationship derived using the Nadaraya–Watson estimator. The
scatterplots clearly show the systematic positive relation obtained in the linear regressions, and it is obvious that the
one-minute and daily relations are not the result of a small number of outliers. However, at the one-minute frequency,
the scatterplots do suggest some nonlinearity in the association, and this is confirmed by the non-parametric estimation.
At this very high frequency, the nature of the nonlinearity is that large order flow imbalances, both positive and
negative, have a smaller incremental effect on the exchange rate. This concurs with the findings of Jones et al. (1994) in
equity markets. It is, however, not consistent with the idea (and the common wisdom among traders) that large order
imbalances are more likely to convey information and should therefore have a proportionally larger impact on prices.
At the daily frequency, in contrast, the association between returns and order flow is quite linear, and the OLS estimates
and the non-parametric estimates are very close to each other.
The association between order flow and exchange rate returns may vary over time, and any such time-variation may
shed light on the source and interpretation of the relationship between order flow and exchange rates. We first studied
12
We are grateful to Eric van Wincoop for providing us with the code for calculating the population R2 in the regression of exchange rate returns
on order flow for different parameter values in the model of Bacchetta and Van Wincoop (2006). In our calibration, we treated the unit of time as
one day and set the interest rate to 4% per annum, expressed as a daily interest rate. Using the notation of Bacchetta and van Wincoop, we fixed α
(the interest elasticity of money demand) at 10, γ (the coefficient of absolute risk aversion) at 500 and σf (the volatility of fundamental shocks) at
0.01, as in their benchmark parameterization (see their Table 1). Our grid search then varied the remaining parameters: σb (the volatility of hedging
shocks), σv (the private signal variance), ρb (the persistence of hedge trades) and T (the number of days before fundamentals are revealed) so as to
fit the R2 pattern at values of h from one day to two months as closely as possible.
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
103
Table 4
Calibrated parameter values for the Bacchetta van Wincoop model
σb
σν
ρb
T
Euro
Yen
0.015
0.24
0.95
6
0.02
0.16
0.95
7
This table reports the values of the parameters σb, σν, ρb and T that minimize the sum of squared differences between the theoretical R-squared
pattern in a regression of exchange rate returns on order flow for different values of h from the model of Bacchetta and Van Wincoop (2006) and their
empirical counterparts. These were obtained by a grid search. The values of h were from one day to two months.
intradaily variation in the association between order flow and exchange rate returns. To this end, we considered the
regression
rt ¼ a þ R48
j¼1 bj Dj ðt ÞOt þ et
ð2Þ
where rt and Ot denote one-minute returns and order flow, respectively, and Dj(t) is a dummy variable that takes on the
value 1 iff observation t is in the jth half-hour interval of the day (measured in New York time). Estimates of the
coefficients βj, along with 95% confidence intervals, are shown for both currency pairs in Fig. 5. Considerable
variation within the day in the association between order flow and exchange rate returns is evident—similar patterns
were found by Payne (2003). Fig. 5 also shows the average per-minute trading volume in each half-hour window of the
day.13 The slope coefficient βj is lowest at times within the day when trading is most active. For example, for the euro–
dollar currency pair, the slope coefficient is lowest between around 3am and 11am New York time, the hours during
which European and/or North American markets are most active. This pattern of negative correlation between trading
volume and the price impact of trades is consistent with interpreting estimates of the contemporaneous association
between high-frequency returns and high-frequency order flow as a measure of liquidity.14
Fig. 6 focuses on the longer-term variation in the return/order flow relationship. It shows rolling regression estimates
for Eq. (1) with 30-day windows setting the horizon h equal to 1 min and with 250-day windows (corresponding to
about one calendar year of data) with h equal to one day. The resulting slope coefficient estimates, along with 95%
confidence intervals, are shown for both currency pairs. The one-minute estimates over a 30-day window show a large
amount of variation over time, with the slope coefficients ranging from about 40 basis points per billion of order flow to
about 80 basis points in euro–dollar and from 40 to more than 100 basis points in dollar–yen. This highlights the
sensitivity of the estimates to the sample period, and it may explain some of the differences found in past work done on
short samples from different time periods. The one-day estimates over a yearly window show, of course, less variation,
but the same overall trends can be seen at both horizons and for both currency pairs—the estimates of the slope
coefficients rose in the early part of the sample, and peaked for windows ending in 2001, before trending downwards
subsequently.15 Fig. 6 also shows two standard measures of market liquidity—30-day rolling averages of the bid-ask
spread in the EBS data and of the spread between ten-year swap rates and ten-year Treasury yields. Of course, the latter
is a measure of liquidity in the fixed-income market rather than the foreign exchange market, but is still a widely used
liquidity indicator. There's a positive association between these measures of market liquidity and the rolling regression
estimates from Eq. (1): they generally rose in the early part of the sample and around the last recession before trending
downwards subsequently. The numerical values of these correlations, reported in Table 5, lead us to conclude that the
13
Volume refers to the total value in base currency of all trades conducted regardless of whether they are buyer-initiated or seller-initiated. To
preserve data confidentiality, volume is expressed in index form. EBS does not publicly release trading volume data for individual currency pairs.
14
Given this intradaily pattern, one may suspect that the non-linearity observed in the one-minute return/order flow relationship (Fig. 5) could
simply result from the aggregation of observations obtained at different times of the trading day. We find that this is not the case, however, as the
non-linearity is still evident when using only observations obtained between 3 am and 11 am, the busiest time of the trading day.
15
We note that for the daily-horizon rolling regressions, the lowest coefficient estimate in dollar-yen is for a one-year window ending in March
2004, precisely at the end of the 14-month-long episode of massive foreign exchange intervention by Japan’s Ministry of Finance. In contrast,
Giradin and Lyons (2006), using daily customer order flow data from Citibank, do not detect a significant change in the return/order flow slope
coefficients during Japanese intervention.
104
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
Fig. 4. Scatter plot of exchange rate returns and order flow. Dashed line is the OLS estimate; solid line is the Nadaraya–Watson non-parametric estimate.
correlations of order flow and exchange rates, both at one-minute and at daily frequencies, tend to be greater when
market liquidity is lower.
5. Order flow and macroeconomic news announcements
A scheduled data release is the canonical public news event and one might theorize that the role of asymmetric
information would be minimal at these times and that rational agents would instantaneously impound the news in asset
prices without requiring any trading activity. To be sure, the mapping from a multi-dimensional data release into future
economic outcomes is complex, and working out the implications of a news release is something that could well be
thought of as private information, as in the skilled information process models of Kim and Verrecchia (1994, 1997).
However, in a rational expectations and efficient markets framework, there should be no systematic relationship
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
105
Fig. 5. Intraday regression betas and average trading volume. 95 percent confidence intervals constructed using heteroskedasticity robust
standard errors are also shown Index 100: average volume per one-minute period over the whole sample period (separate index for each currency pair).
between order flow and the headline surprise (the unexpected component of the headline news announcement).
Nonetheless, some authors including Evans and Lyons (2006) and Love and Payne (in press) have shown that, in their
data, macroeconomic news surprises are correlated with subsequent order flow. A quite mundane explanation for this
correlation is possible. Leaving live quotes near the pre-announcement price in an anonymous limit order book at the
moment of a news announcement is surely not a profit-maximizing strategy, as it amounts to taking a one-sided bet
against oneself. Yet, Carlson and Lo (2006), studying in great detail the impact in the foreign exchange market of one
news announcement, argue that some foreign exchange traders choose to do precisely this.16 If the price jumps
16
Carlson and Lo (2006) argue that these traders follow at all times a rigid rule of mechanically attempting to cover in the interdealer market
positions opened while trading with their customers.
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D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
Fig. 6. Rolling regressions of return regressed on order flow (1999–2004). 95 percent confidence intervals constructed using heteroskedasticity robust
standard errors are also shown One-minute rolling regressions constructed using a 30 day moving window; Daily rolling regressions used a 250 day
moving window.
following an announcement, any such quotes on one-side of the original price will be swept out. Under this scenario,
the observed order flow will then be, at least in part, simply a byproduct of the price movement arising from the direct
reaction of the exchange rate to the surprise component of the news announcement.
Using our long span of high-frequency data, we study the behavior of order flow at times of U.S. macroeconomic
announcements that come out at 8:30am. We regress order flow from 8:30 to 8:31 on the day of a news announcement on
the surprise component of the data that were released at 8:30 that day. We repeat this regression changing the dependent
variable to order flow from 8:31–8:35 and then to order flow in each successive five-minute window from 8:35–8:40 up to
8:55–9:00. Separate regressions are run for each type of data announcement. The releases that we consider are GDP (the
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
107
Table 5
Correlations between selected spreads and rolling slope coefficients in regressions of returns on order flow
Currency
Rolling slope coefficient
Forex bid-ask spread
10-year Treasury/swap
Euro
30-day rolling estimates (1-min horizon)
250-day rolling estimates (1-day horizon)
30-day rolling estimates (1-min horizon)
250-day rolling estimates (1-day horizon)
0.82
0.77
0.86
0.49
0.75
0.73
0.60
0.69
Yen
This table reports the correlations between rolling estimates of the slope coefficient in Eq. (1) and rolling estimates of two liquidity measures: the EBS
average bid-ask spread for the same currency pair, and the spread of ten-year swap rates over comparable maturity Treasury securities, obtained from
Bloomberg. For example, 0.82 is the time series correlation between the 30-day rolling estimate of β in Eq. (1) at the 1-minute horizon for the euro
and the average euro–dollar bid-ask spread over the same 30-day rolling window.
quarterly advance release), and, all at monthly frequency, the employment report, CPI, housing starts, retail sales, PPI,
durable goods sales, the trade balance and the unemployment rate. For each type of news release, the surprise component of
that release, is measured as the difference between the actual released value and the ex-ante median expectation taken from
the Money Market Services survey, scaled by its standard deviation. Love and Payne (in press) considered very similar
regressions, but, because of the short span of their sample, they had to pool all the different announcement types. In our sixyear span of data, we have 72 observations for each type of data announcement, except for GDP which is quarterly and so
we only have 24 observations. Our results are shown in Table 6. The sign of the unemployment rate has been flipped, so
that, for each announcement type, a positive surprise means stronger-than-expected economic activity. In the 8:30 to 8:31
minute, all announcement surprises are estimated to have a negative effect on euro–dollar order flow and a positive effect
on dollar–yen order flow, meaning news of stronger economic activity is associated with orders by market “takers” to buy
dollars. The association is statistically significant at the 5% level in a majority of cases. However, the headline data surprise
only impacts order flow for a very short interval after each type of announcement. In the windows from 8:31 to 8:35 and
from 8:35 to 8:40, most announcement surprises do not have a significant correlation with the order flow, though some are
significant and with the expected sign. Beyond 8:40, there is very little sign of any relationship—across the two currency
pairs, eight announcements and four time windows between 8:40 and 9:00, we estimated a total of 64 coefficients of which
only 6 were statistically significant at the 5% level. This is roughly what one would expect by chance alone in the absence
of any real relationship.
Table 6
Slope coefficient estimates in regression of order flow on standardized announcement surprises
Currency
Euro
Yen
Surprise
Nonfarm payrolls
CPI
GDP
Housing starts
PPI
Retail sales
Trade balance
Unemployment †
Nonfarm payrolls
CPI
GDP
Housing starts
PPI
Retail sales
Trade balance
Unemployment†
8:30–8:31
− 27.16
− 2.68
− 61.05
− 21.58⁎⁎⁎
− 4.41⁎⁎⁎
− 35.33⁎⁎
− 46.12⁎⁎⁎
− 41.06⁎⁎⁎
58.93⁎⁎⁎
0.72
43.81⁎⁎⁎
6.23⁎⁎
5.55⁎⁎
19.70⁎⁎⁎
24.91⁎⁎⁎
8.14
8:31–8:35
8:35–8:40
8:40–8:45
8:45–8:50
8:50–8:55
8:55–9:00
0.20
− 1.93
2.78
− 15.55⁎⁎
13.24
− 35.23⁎⁎⁎
− 23.86
− 24.33
37.10
− 7.62
22.70
1.61
− 2.82
14.64⁎⁎⁎
13.26
11.94
− 49.21⁎⁎
11.47
31.72
− 20.47⁎⁎⁎
11.03
16.13
− 4.99
2.79
44.40⁎⁎
2.01
12.19
7.39
− 3.01
− 1.55
− 6.96
−11.50
− 7.37
− 1.09
− 2.65
− 9.77
1.47
− 20.29⁎⁎
− 17.84
− 10.00
− 4.52
− 8.98
29.94
− 2.61
0.23
− 5.20
0.71
10.83
16.74
15.26
0.39
6.46
12.08
− 3.97
11.85
36.16
6.74
0.94
−13.79
− 0.01
2.99
5.56
15.11⁎⁎
− 11.28
20.62
0.56
0.26
− 4.46
17.88⁎⁎
19.24⁎⁎⁎
0.91
− 1.70
7.42
3.17
− 7.05
8.47
− 5.24
2.46
6.89
0.19
9.49
− 13.22
− 29.93⁎⁎
−8.46
6.39
−6.79
−1.92
−2.77
5.53
−4.87
23.05⁎⁎
8.84
7.11
1.96
5.20
16.51⁎
This table reports the regression coefficients in a regression of order flow in windows after news announcements on the surprise components of those
announcements. A separate regression is run for each announcement type. Our data span six years and all the announcements are monthly, meaning
that there are 72 observations in each regression, except for GDP which is quarterly and so has 24 observations. The symbol † means that the sign of
this countercylical indicator has been flipped. ⁎, ⁎⁎ and ⁎⁎⁎ indicate statistical significance of an individual coefficient at the 10%, 5% and 1% level,
respectively, using heteroskedasticity-robust White standard errors.
108
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
Our results in the first minute are entirely consistent with the findings of Love and Payne (in press) and therefore
highlight the same puzzle: in a rational expectations framework with efficient markets, the surprise component of these
data releases, released simultaneously to all the trading public, should not be correlated with order flow. The fact that,
for both currency pairs, all macroeconomic announcements are accompanied by this pattern in order flow is consistent
with the behavior highlighted by Carlson and Lo (2006) accounting for at least some of the order flow seen in the first
minute, as is the fact that there is little or no relationship in the subsequent 29 minutes.
6. Conclusion
We have studied the relationship between exchange rates and order flow in an important new dataset from EBS
which represents a majority of global interdealer foreign exchange trading in the top two currency pairs from 1999 to
2004. Using these data, we confirm the presence of a strong association between exchange rate returns and interdealer
order flow at horizons of up to two weeks. At longer horizons, however, this relationship becomes a good bit weaker,
and there is little evidence of cointegration between cumulative order flow and exchange rates. We examine intradaily
and longer-term time-variation in the relationship between order flow and exchange rates and show that, in both cases,
the relationship appears to be strongest when market liquidity is lowest.
Overall, our analysis of this new dataset points to an important role for liquidity effects in the relationship between
order flow and exchange rates. This could in turn offer support to the “weak flow-centric” view of exchange rate
movements or especially to models like that of Bacchetta and Van Wincoop (2006) that combine both information and
liquidity channels, with the liquidity effects reinforced by the fact that order flow can also convey fundamental
information.
References
Andersen, T.G., Bollerslev, T., Diebold, F.X., Vega, C., 2003. Micro effects of macro announcements: real-time price discovery in foreign exchange.
American Economic Review 93, 38–62.
Bacchetta, P., Van Wincoop, E., 2006. Can information heterogeneity explain the exchange rate determination puzzle? American Economic Review
96, 552–576.
Bjonnes, G.H., Rime, D., Solheim, H.O.A., 2005. Liquidity provision in the overnight foreign exchange market. Journal of International Money and
Finance 24, 175–196.
Brandt, Michael, Kavajecz, K.A., 2004. Price discovery in the U.S. treasury market: the impact of orderflow and liquidity on the yield curve. Journal
of Finance 59, 2623–2654.
Breedon, F., Vitale, P., 2004. An empirical study of liquidity and information effects of order flow on exchange rates. European Central Bank Working
Paper, 424.
Carlson, J.A., Lo, M., 2006. One minute in the life of the DM/US$: public news in an electronic market. Journal of International Money and Finance
25, 1090–1102.
Chaboud, A.P., Chernenko, S.V., Howorka, E., Krishnasami Iyer, R.S., Liu, D., Wright, J.H., 2004. The high-frequency effects of U.S.
macroeconomic data releases on prices and trading activity in the global interdealer foreign exchange market. International Finance Discussion
Paper, 823.
Diether, K.D., Lee, K.H., Werner, I.M., 2005. It's SHO Time! Short-sale price-tests and market quality. Working Paper. Fisher College of Business,
Ohio State University.
Dunne, P., Hau, H., Moore, M., 2004. Macroeconomic order flows: explaining equity and exchange rate returns, unpublished manuscript.
Engle, R., Granger, C., 1987. Cointegration and error correction: representation. Estimation and Testing. Econometrica 55, 251–276.
Evans, M.D.D., Lyons, R.K., 2002. Order flow and exchange rate dynamics. Journal of Political Economy 110, 170–180.
Evans, M.D.D., Lyons, R.K., 2005. Exchange rate fundamentals and order flow, unpublished manuscript.
Evans, M.D.D., Lyons, R.K., 2006. How is macro news transmitted to exchange rates? unpublished manuscript.
Froot, K.A., Ramadorai, T., 2005. Currency returns. Intrinsic Value and Institutional-Investor Flow. Journal of Finance 60, 1535–1566.
Gereben, A., Gyomai, G., Kiss, N.M., 2006. Customer order flow, information and liquidity on the Hungarian Forint Exchange Market. Central Bank
of Hungary working paper 2006-8.
Giradin, E., Lyons, R.K., 2006. Does intervention alter private behavior? Unpublished manuscript.
Gonzalo, J., Lee, T.H., 1998. Pitfalls in testing for long run relationships. Journal of Econometrics 86, 129–154.
Harris, L., 1998. Optimal dynamic order submission strategies in some stylized trading problems. Financial Markets, Institutions and Instruments 7, 1–75.
Hau, H., Killeen, W., Moore, M., 2002. How has the euro changed the foreign exchange market? Economic Policy 0, 149–177.
Johansen, S., 1988. Statistical analysis of cointegrating vectors. Journal of Economic Dynamics and Control 12, 231–254.
Jones, C.M., Kaul, G., Lipson, M.L., 1994. Transactions. Volume and Volatility. Review of Financial Studies 7, 631–651.
Killeen, W., Lyons, R., Moore, M., 2006. Fixed versus flexible: lessons from EMS order flow. Journal of International Money and Finance 25,
551–579.
D.W. Berger et al. / Journal of International Economics 75 (2008) 93–109
109
Kim, O., Verrecchia, R., 1994. Market liquidity and volume around earnings announcements. Journal of Accounting and Economics 17, 41–67.
Kim, O., Verrecchia, R., 1997. Pre-announcement and event-period information. Journal of Accounting and Economics 24, 395–419.
Love, R., Payne, R., in press. Macroeconomic news, order flow and exchange rates. Journal of Financial and Quantitative Analysis.
Lyons, R., 1997. A simultaneous trade model of the foreign exchange hot potato. Journal of International Economics 42, 275–298.
Payne, R.K., 2003. Informed trade in spot foreign exchange markets: an empirical investigation. Journal of International Economics 61, 307–329.
Phillips, P.C.B., Hansen, B.E., 1990. Statistical inference in instrumental variables regression with I(1) processes. Review of Economic Studies 57,
282–306.