The role of information arrival for the Australian dollar trading volume and volatility
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Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
Suk-Joong Kim (Australia), Michael D. McKenzie (UK), Di Amy Shi (Australia)
The role of information arrival for the Australian dollar trading
volume and volatility
Abstract
This study investigates the impact of scheduled and unscheduled information arrival on realized volatility and volume
in the USD/AUD exchange rate. The authors find that trading outside of Australian business hours dominates and this
is mostly due to the higher frequency of information arrivals during the offshore trading period. Where the different
sources of information are considered, our findings reveal that it is offshore money market news that is the most
important determinant of AUD volatility. Fixed income and, to a lesser extent, foreign exchange related news however,
were found to be the most important determinants of AUD volume. Finally, while Australian macroeconomic
announcements were found to have a more consistent impact on AUD volatility, US macroeconomic news had a
considerably larger impact.
Keywords: news, Australian dollar, foreign exchange, market microstructure.
JEL Classification: F31, G15.
Introduction context. A number of studies report a significant
positive relationship between exchange rate volatility,
The Australian dollar is unique amongst the major
order flows and the flow of information, measured as
currencies1 in that the majority of trading activity
the amount of news headlines in addition to scheduled
(and hence price discovery), occurs outside of
news (see Bauwens, Omrane and Giot, 2005; and
Australian business hours (Reserve Bank Bulletin,
Dominguez and Panthaki, 2006). Another branch of
2007). Hogan and Batten (2005) suggest that this
this literature has ignored the information content of
phenomenon is a direct result of the intensity of
the news altogether and focused solely on some
information arrival (consisting of both scheduled
measure of the intensity of information flows (see
macroeconomic announcements and unscheduled
Melvin and Yin, 2000; and Chang and Taylor, 2003).
newswire flashes), which are higher when the
European and North American markets are trading2. The purpose of this paper is to provide insights into
the response of the foreign exchange market to both
A variety of literature exists that focuses on the
forms of information arrivals. To the best of the
relationship between scheduled announcements (such
as macroeconomic and interest rate policy announce- authors’ knowledge, this is the first such paper to do
ments) and high frequency exchange rate return so. Specifically, we investigate the relationship
volatilities (see inter alia Goodhart et al., 1993; between realized volatility and trade volume on the
Degennaro and Shrieves, 1997; Andersen and one hand and scheduled and unscheduled information
Bollerslev, 1998; Cai et al., 2001; Anderson et al., arrivals on the other. The advantage of our research
2003). More recent studies have delved deeper into framework is that it allows us to consider whether the
this relationship and have found that scheduled market responses to information arrivals are
macroeconomic information releases are reflected in different, distinguishing between different types of
price and volatility via order flow (Evans and Lyons, news and trading during and outside of Australian
2008; and Love and Payne, 2008). Further, order market hours. Further, our focus on the Australian
flow was found to have greater explanatory power dollar provides an important alternative perspective
during periods of information releases compared to on the U.S. dollar focused prior literature.
other times (see Love and Payne, 2008). The major findings of this paper can be summarized
In addition to scheduled news, unscheduled as follows. First, we find significant volatility and
information arrivals have also been considered in this volume responses to unscheduled Reuters news
headlines about 20-30 minutes before the release.
Even after factoring in the possibility of a 5 to 20
Suk-Joong Kim, Michael D. McKenzie, Di Amy Shi, 2014.
Suk-Joong Kim, Ph.D., Associate Professor of Finance, Discipline of minute delay for some of the news headlines, this
Finance, The University of Sydney Business School, Australia. finding suggests heightened market activity prior to
Michael D. McKenzie, Ph.D., Professor of Finance, University of
Liverpool Management School, UK.
the arrival of unscheduled news. In addition, we find
Di Amy Shi, Deutsche Bank AG Australia and New Zealand, Australia. that the responses are predominantly driven by
1
The 2010 BIS Triennial Central Bank Survey ranks the Australian offshore news components.
dollar as the fifth most heavily traded currency in the world and the
USD/AUD exchange rate is also ranked fifth most heavily traded Second, we find that there are significant and
currency pair by market turnover with a daily average of US$249b.
2
Note that the intensity of information arrival in the overnight session is immediate volatility and volume responses to
compounded for the AUD as it is influenced by commodity price macroeconomic announcements. The market
movements, whose trading is also predominantly after-hours. The CAD
is another major commodity-based currency, however, its trading
responses were largely driven by the Australian and
overlaps with that of the US market. the U.S. announcements, whereas the scheduled
8Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
news from the Eurozone and Japan were not found raw tick-by-tick data, the entire length of the sample
to be important. In addition, we find evidence of was then divided into equal length holding periods of
elevated volume prior to any scheduled news five minutes each, beginning at 11:00:00 am AEST on
release, however volatility was found to respond 1 April 2008 and ending at 9:59:99 am AEST on 1
only during the post-announcement period. October 2008. The initial and final mid rates that fall
within the bounds of each holding period are recorded
Third, we find that volatility and volume are
respectively as the opening and closing mid rates for
generally more sensitive to macroeconomic
that period, thereby treating them as though they fell
announcements than the unscheduled news. This is
exactly on the boundaries of the period.
consistent with previous findings in information-
related foreign exchange literature (Dominguez and The variables of interest in this study are volume
Panthaki, 2005; Bauwens et al., 2005). and volatility measured over the fixed intervals of five
minutes1. Volume is defined as the number of quotes
Fourth, upon disaggregating the unscheduled news
made in a five minute interval2. Volatility is the
into three Reuters news sub-headings of foreign
realized volatility estimator which is the sum of all
exchange, money and fixed income market reports,
squared tick-by-tick returns within a five minute
we find that money market news is most active in
determining the volatility response to unscheduled interval, where returns are defined as the log difference
news due no doubt to their macro-related headlines, between successive mid quotes3. For our sample, there
while fixed income news is the driver of the volume are 184 days and 288 five minute intervals for each
response to the aggregate unscheduled news. day. Excluding weekends and non-trading intervals as
in the literature (e.g. Melvin and Yin, 2000) we end up
Thus, by jointly simultaneously considering both with a total of 34,104 five minute holding periods.
types of news, this paper is able to provide new
insights into the relative important of un/scheduled The final transformation of data consists of adjusting
information to the market. The results of our the volume and volatility time series for seasonal
investigations have important implications for all patterns. Literature has shown that high frequency time
levels of participants in the AUD market. By detailing series are often subject to seasonal patterns (Andersen
the volume and volatility responses surrounding both and Bollersev, 1994; Dacorogna et al., 1993). To
types of information arrivals, market participants can account for seasonal effects for any given time series,
factor these into their trading strategies. Also, our 288 cross-sectional standard deviations for each of
findings are of relevance for policy makers in relation the five minute intervals in the day are calculated.
to the timing and the method of delivery of policy After estimating these ‘seasonal multipliers’ for a
announcements (e.g. target interest rate announce- given time series, each observation is divided by
ments and foreign exchange intervention announce- the seasonal multiplier to obtain the de-
ments) and intervention activities. seasonalized time-series. This seasonal adjustment
process follows the methodology outlined in
The rest of the paper is organized as follows.
Melvin and Yin (2000) and is similar to the
Section 1 explains the types of data used in this
approach taken by Taylor and Xu (1997)4.
research and section 2 discusses the empirical
methodology. Empirical results are then presented
and discussed in section 3, which is followed by
conclusion in the final section. 1
Five minute intervals have been chosen as the length of each holding
1. Data description period in order to best capture short-term changes within the variables
as well as to allow for the possibility of pre-emptive and delayed
response from the market. The choice of five minutes as the holding
1.1. Tick by tick AUD exchange rate data. We use period length is consistent with the literature (Berger et al., 2008; Faust
tick by tick indicative quotes from all quoting banks et al., 2007; Andersen et al., 2007)
2
in the USD/AUD market on a 24-hour basis. The Berger et al. (2008) argue that the tick-frequency is an effective proxy
for the actual volume. By employing actual volume measured in terms
data is sourced from the Reuters Tick Data History of order flow, they find that their measure was not significantly different
(RTDH) via SIRCA. The sample period for the from volume as measured on a tick-by-tick basis as each trade is more
or less standardized in size.
intra-day quote data is from 1 April 2008 to 30 3
In preliminary estimations, volatility was also estimated using the
September 2008. The database contains dates and method provided by Garman and Klass (1980). While Garman and
time-of-day stamped to the second in Greenwich Klass (1980) argued that GK volatility is more efficient than either the
PARK (Parkinson, 1980) estimator or the realized volatility estimator,
Mean Time (GMT), the dealer bid and ask quotes, subsequent studies such as Andersen and Bollerslev (1998) and Cai et
and a bank identifier. As the focus of this paper is al. (2001) have provided more compelling evidence for the superiority
on trading during and outside of Australian business of the realized volatility estimator for high frequency analyses.
4
In preliminary estimations, various other seasonal adjustment methods,
hours, each quote was then adjusted to Australian such as using the cross-sectional average as the seasonal multiplier, and
Eastern Standard Time (AEST, GMT+10 and +11 adding an additional seasonal adjustment factor as a regressor
(Andersen and Bollerslev, 1998) were also considered. However, these
during daylight saving period). We use the mid rate methods proved to be weak as seasonal patterns persisted in the de-
between bid and ask quotes for each tick. From the seasonalized series.
9Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
To aid in understanding the data, Figure 1 presents a The database contains news headlines which are
comparison between the cross-sectional average of date and (GMT) time stamped to the second. Each
overall news intensity and the cross-sectional averages news headline also includes a news category code
of the volume and volatility for the AUD data across which classifies every headline into foreign
the 288 five minute intervals in the day. The two exchange-related (FX), fixed income-related (FI) or
vertical lines in each graph delineate three broad money market-related (MM) news. In general, the
trading zones for Sydney, London and New York and Reuters news headlines are a collection of real time
it is evident that the overwhelming majority of news reporting of breaking news and the general reporting
arrival and market activity occurs during offshore of market events as they occur (e.g. “The Australian
trading periods. Volume is at its lowest at around 8:00 FX market rose 2.5% in the opening”). It is worth
am AEST, increasing to the first peak at around 11:30 noting that there are potentially two minor limitations
am AEST, which coincides with the scheduled release to the Reuters News Headlines database: first, there are
of all Australian macroeconomic announcements by occurrences of overlaps in the headlines classified as
the Australian Bureau of Statistics. The second peak FX, FI or MM news; and second, some headlines
occurs around 5:00 pm AEST, which is approximately reports may be delayed by 5 to 20 minutes1.
the time of the commencement of London trading. The
News intensity is measured as the number of news
third peak appears at around 10:30 pm – 12:00 am
headlines within a five minute interval. A news
AEST, which corresponds to the period of scheduled intensity variable is computed for the aggregate news
economic data announcements in the United States. and also for each of the three disaggregated forms of
These pronounced peaks in trading volume clearly news: FX News, FI News and MM News2. After
highlight the importance of scheduled macroeconomic adjusting the timestamps from GMT to AEST and
news variables to be foreign exchange market. excluding weekends from the sample, we end up with
The cross-sectional averages of the intraday a total of 11,421 news headlines, of which
volatility display a similar multi-peak pattern to the approximately 80% are classified as FX News, 15% as
cross-sectional averages of volume. The peaks in the FI News and 5% as MM News. Table 2 presents some
volatility appear to occur at around the same time as examples of each the three type of news headlines.
the peaks in volume: 11:30 am, 5:00 pm, and around To test the differential impact of onshore and
10:30 pm AEST. This is not surprising given the well offshore news on market activity, onshore and
documented positive correlation between volume and offshore dummies were constructed. The onshore
volatility. Moreover, a seasonal effect in volatility that dummy is 1 from 7:00 am to 4:00 pm AEST, which
is induced by the regularly scheduled news corresponds to the Australian trading period, and 0
announcements is also evident. The timing of the otherwise. The offshore dummy is 1 from 4:00 pm to
volatility peaks observed within the data differs 7:00 am AEST the next calendar day (approximately
slightly from those found by Hogan and Batten (2005). the offshore trading hours), and 0 otherwise. We then
However, as with volume, the same fundamental construct news intensity variables for offshore and
multi-peak pattern exists. One potential source for this onshore news by multiplying the news intensity
difference may be the longer sample period of this variables by appropriate dummies. The on/offshore
study, rendering it more representative of the market measures of FX, FI and MM News are also
for the AUD. In addition, a different method for constructed in the same manner. Table 2 summarizes
estimating realized volatility is utilized, which could the relative frequency of each of the different news
be an additional cause of these discrepancies. groups. We note that while the Australian trading
period accounts for one-third of the trading day, it
1.2. Scheduled and unscheduled information only provides 21.40% of the total news headlines, of
arrivals. The primary source of unscheduled news which the majority (around 80%) are FX news.
is the Reuters Headline News database obtained
from the SIRCA RTDH database. The database 1
In general, Reuters news items are initially released in real time as a
contains all news headlines that were released on the news alert, which is just a short headline of the event (e.g. “BOJ
Reuters alert screens, which are standard equipment announces it intervention intentions”). Within about 5 to 20 minutes a
more detailed commentary (usually a few lines of report) is added and
on foreign exchange trading desks and are also used then the news reported again. This is known as a news headline or the
by non-dealer market participants. Reuters collects first take. Another 20-25 minutes may elapse when a second take is
released with additional information that is either clarification or
news reports from approximately 150 bureaus and corrections of the first take.
the presence of an editorial process means that most The Reuters news database during our sample period does not contain
news items in the data set are viewed as containing distinct alerts and headlines. Instead, both types of releases are classified
as headlines. There are however, second takes which we removed from
newsworthy information. At the same time, the sample. Therefore, some of the headlines are contemporaneous (alerts)
competition between Reuters, Bloomberg and Dow and the others are with 5-20 minutes of delay (first takes). We
acknowledge this as a potential weakness of our database.
Jones ensures publication delays are minimized 2
It should be noted that there are news headlines which overlap
(Evans and Lyons, 2008). amongst the three news categories. However, this is not widespread.
10Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
A macroeconomic announcement database for SNewst,i-j where j is the number of leads and lags for
Australia, the U.S., the European Union and Japan is both news variables. As stated earlier, news intensity is
also constructed. These announcements are sourced calculated as the number of news headlines or
from the Bloomberg database of Economic Releases announcements that fall within the holding period.
and contains the date, time-of-day stamped to the
To account for the established positive relationship
minute (AEST), and the name of the macroeconomic
between volume and volatility2, volatility (RVt,i) and
announcement. The macroeconomic announcements volume (Vlmt,i) are ‘controlled’ against g lags of
we consider include major macroeconomic headlines each other. We set the number of lags for the
such as GDP, CPI and unemployment, but the data ‘control’ variable, g, to be three3. In addition, we use
also includes monetary policy announcements from p and q lags of autoregressive and moving average
the central banks in Australia, the U.S., European in each equation. The optimal combination of p and
Union and Japan. Table 3 presents a description of the q for each estimation is determined according to the
macroeconomic headlines captured in this database. Schwartz Information Criterion4. The number of
2. Methodology leads and lags for unscheduled news intensity and
scheduled news intensity, j, is six, i.e. j = -6 to 6.
2.1. Model 1: The aggregate model. The aim of This represents 30 minutes of analysis prior to the
this paper is to explore the impact of scheduled and release of a news announcement and 30 minutes of
unscheduled information arrivals on the trading of analysis subsequent to the release of a news
the Australian dollar. To ensure that the time series announcement – a total of 65 minutes of analysis on
characteristics of the data, as well as the economic the response of market activities surrounding the
effects of the news variables are adequately release of news to the market5.
captured, autoregressive moving average models
with independent variables (ARMAX) are utilized. The primary focus of the baseline model presented
Model 1 is designed to capture the differential in Equation 1 is, bj, the impact of the aggregate
unscheduled news on market activity, and, cj, the
impact of aggregate scheduled and unscheduled
impact of the aggregate scheduled news on market
news. The aggregate form of an ARMAX model to
activity. The sign and significance of the coefficients
be estimated for volume and volatility is1:
will provide insights on the nature of the impact of
RVt ,i or Vlmt ,i D0 ¦ b jUNewst ,i j each types of news on volume and volatility, the
j time taken to process new information and also
¦ c j SNewst ,i j ¦ d gVlmt ,i g
whether the Australian dollar market exhibits
(1)
evidence of participants pre-empting news.
j g
¦ e p RVt ,i pH t ,i ¦ f qH t , i q , A positive (negative) and statistically significant
p q contemporaneous unscheduled (or scheduled) news
coefficient indicates that market activity in the
where, RVt,i = Realized Volatility in the ith 5 minute AUD/USD is greater (lower) within the first five
holding period on day t; Vlmt,i = Volume, or the minute interval during which the information is
number of ticks within an ith 5 minute holding period released onto the market6. This suggests that volume
on day t; UNewst,i-j = the intensity of unscheduled or volatility is almost immediately higher (lower) as
Reuters news within an ith 5 minute holding period on result of greater (lower) news intensity. Statistically
day t at the jth lead/lag; SNewst,i-j = the intensity of significant lagged unscheduled news coefficients
scheduled macroeconomic announcements within an
ith 5 minute holding period on day t at the jth lead/lag;
Ht,i = the white noise error term. 2
3
e.g. Melvin and Yin (2000), Chang and Taylor (2005).
The number of lags for the ‘control’ variable was determined during
As noted, t spans from 1 April 2008 through to 1 preliminary estimations and up to 12 lags were considered. In all
October 2008, with i encompassing the 288 five- specifications of the baseline model, it was found that volatility (volume)
was significant to volume (volatility) for no more than 3 lags, i.e. 15 minutes.
minute holding periods in a trading day. In (1) the 4
An alternative to estimating the volatility is via a GARCH
independent variables to be modelled are realized methodology. This is consistent with the approach of Hsieh (1988),
Gallant et al. (1991), Bollerslev et al (1992), Degennaro and Shrieves
volatilities, RVt,i and trading volumes, Vlmt,i, using the (1997), and Melvin and Yin (2000). GARCH volatilities utilize significantly
intensity of all unscheduled news within a five-minute fewer price observation points in a given holding period, usually opening
holding period as represented by the term UNewst,i-j and closing prices, and so they are in essence smoothed approximation of the
holding period volatility and can be less accurate than the realized
and the intensity of all scheduled news for one five- volatilities that use all available price observation points.
minute holding period as represented by the term, 5
The number of leads and lags for the news variables was determined
during preliminary estimations, where up to 12 leads and lags of
unscheduled and macroeconomic announcements variables were considered.
In all specifications, news was found to have an impact on market activity
1
The research methodologies and the methods of presenting summary for no more than 30 minutes before and after the news release.
6
results in graphs developed in this paper were also used in a follow up For the scheduled announcements, there is no problem of delayed
paper, Daniel, Kim and McKenzie (2014). reporting. Thus, lead/lag zero represents contemporaneous holding period.
11Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
would indicate that market activity is higher (lower) presence of informed traders trading on private
after the actual arrival of the news in the information, 3) evidence of traders closing out their
contemporaneous five minute interval. This suggests positions to avoid surprises when news is actually
1) a delayed market reaction to news arrival, 2) an released, or 4) the retrospective characteristic of a
increase in market uncertainty stimulates higher levels proportion of reported news headlines classified as
of trading as a result or 3) market heterogeneity in the unscheduled news (Bauwens et al., 2005).
interpretation of the news content (Bauwens et al.,
2.2. Model 2: Onshore vs. offshore news model.
2005; Degennaro and Shrieves, 1997).
In order to capture the differential impacts of
Statistically significant leads of unscheduled news unscheduled onshore and offshore news we apply
would indicate that market activity is higher (lower) on and offshore dummies to the unscheduled news
before the actual arrival of news in the variables in (1A) and (1B). This will allow us to
contemporaneous five minute interval. This suggests determine which of the two trading zones is the
four possible explanatory factors: 1) systematic delays principal driver of the aggregate unscheduled news.
in the reporting of Reuters news headlines1, 2) the The modified ARMAX models are:
RVt ,i or Vlmt , i D 0 ¦¦ (b1,k ,jOnshore Dumtk,i,Onshore b2,k ,Offhore
j Dumtk,i, Offshore ) UNewstk, i j
k j
(2)
¦ c j SNewst , i j ¦ d gVlmt , i g ¦ e p RVt , i p H t , i ¦ f qH t , i q ,
j g p q
where, Dumtk, i, onshore = the onshore news dummy Figure 1, the cross-sectional average of volatility
which takes the value 1 between 7:00 am and 4:00 and volume were found to increase during offshore
pm AEST, and 0 otherwise; Dumtk, i, offshore = The trading periods, which coincides with heightened news
offshore news dummy which takes the value 1 from intensity. Thus, Equation 2 provides a more formal test
4:00 pm to 7:00 am AEST, and 0 if otherwise. of this relationship and, a priori, offshore news is
expected to be the driver of market activity. This
The intensity of onshore unscheduled news within a hypothesis is also consistent with empirical findings
five-minute holding period is represented by the term, from the current literature which, 1) establishes that
Dum tk,,ionshoreUNewstk,i j , and the intensity of offshore news intensity during offshore trading hours for the
unscheduled news for one five-minute holding period AUD are comparatively greater than the news intensity
k , offshore
is represented by the term, Dum t ,i UNewstk,i j , during domestic Australian trading hours (Clifton and
where j is the number of leads and lags for the news Plumb, 2007) and, 2) finds statistically significant
variable in both time zones. positive relationships between news intensity and
market activity variables in other global currency
The coefficients of interest in this model are b1,kj,onshore , pairs (Dominguez and Panthaki, 2006).
which measures the impact of onshore news on 2.3. Model 3: The disaggregated news model.
volatility and volume, and b2,k , offshore
j
, which captures the Where unscheduled news is disaggregated into the 3
impact of offshore news on volatility and volume. Reuters news categories (FX, FI, and MM News)
An analysis of these variables will provide insight and scheduled news is disaggregated into 4
into the differential impact of onshore and offshore countries of origin (Australia, the U.S., the Euro-
news on volume and volatility. Recall from zone and Japan), the ARMAX model becomes:
RVt , i or Vlmt , i D 0 ¦¦ (b1,k ,jFX Dumtk,i, FX b2,k , jFI Dumtk, ,jFI b3,k ,jMM Dumtk, i, MM ) UNewstk,i j
k j
¦¦ (c l , AUS
1, j Dum l , AUS
t ,i c2,l ,USj Dumtl,,iUS c3,l , EU
j Dumt , i
l , EU
c4,l , JPj Dumtl,i, JP ) SNewstl,i j (3)
l j
¦ d gVlmt ,i g ¦ e p RVt , i p H t , i ¦ f qH t ,i q ,
g p q
where,1 Dum tk,,iFX , Dumtk,i, FI , Dumtk,i, MM = FX, FI and respectively. Dumtl,,iAUS , Dumtl,,iUS , Dumtl,,iEU , Dumtl,,iJP =
MM news dummy which takes the value 1 if news is Australian, US, Eurozone and Japanese macro-
classified as FX, FI and MM and 0 if otherwise, economic news dummy which takes the value 1 if
news originates from Australia, US, Eurozone and
1
Japan and 0 if otherwise, respectively.
As discussed in the data section, some of the headlines may be
reporting market events of 5 to 20 minutes earlier and as such, In Equation 3, the disaggregated unscheduled news
significant leads up to 20 minutes (4th lead) may be regarded as
variables are FX news í Dumt , i UNewst , i j , FI
k , MM k
potentially contemporaneous.
12Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
News í Dumtk,i, FI UNewstk,i j , and MM news í and each lead and lag is represented along the
Dum k , MM
UNewsk horizontal axis. Leads 2-6 represent 25 minutes of
t ,i , whereas the disaggregated
t ,i j
the pre-announcement period prior to the actual
scheduled news variables are Australian macro- release of the news announcement. Lead 1 to Lag 0
economic announcements Dumtl,,iAUS SNewstl, i j , represents the 10 minutes surrounding the news
U.S. macroeconomic announcements í release. Lags 1-6 represent up to 35 minutes of the
Dumt ,i SNewst ,i j , Eurozone macroeconomic
l ,US l
post-announcement period. The significance of the
coefficients is represented by the color of each bar:
announcements í Dumtl,,iEU SNewstl,i j , and
black columns represent statistical significance at 1%;
Japanese macroeconomic announcements í columns in grey represent statistical significance at
Dumtl,,iJP SNewstl,i j where j is the number of leads 5%; and the white columns represents insignificant
and lags for both types of news variables. coefficients (p-value of 5% or larger).
We focus on 1) the coefficients of the disaggregated The positive and statistically significant coefficients
unscheduled news variables: b1,k ,jFX , b2,k , jFI and b3k,,jMM , for the two contemporaneous periods, Lead 1 and
and 2) the coefficients of the disaggregated Lag 0, indicate that the aggregate unscheduled
l , AUS l ,US l , EU Reuters news has a positive impact on the volatility
macroeconomic news variables: c1, j , c2, j , c3, j
l , JP of the exchange rate. This is consistent with the
and c4, j . The analysis of the first group will findings of Chang and Taylor (2003) and
provide insight into the differential impact of the Dominguez and Panthaki (2006). Moreover, the
Reuters news classification on market activity and the concentration of positive and significant lead news
analysis of the second will shed light on the variables in the pre-announcement periods suggest
differential impact of macroeconomic news by country that volatility is greater for up to 30 minutes prior to
of origin. This will enable us to identify the main the news headline release3. Considering the potential
drivers of aggregate unscheduled and macroeconomic of some of the news being delayed anywhere
announcements respectively. A priori, FX news are between 5 to 20 minutes, the pre-announcement
expected to drive the volume and volatility response to responses can be as short as 10 minutes.
aggregate unscheduled news as it accounts for the
greatest portion of the total Reuters news headlines The significance of the news variables in the pre-
and relates directly to the foreign exchange market. announcement periods is potentially driven by the
In addition, a priori, the Australian and the U.S. following three factors í volatility in the AUD rises
macroeconomic news are likely to have more before the news hits the market as a result of: 1)
significant impacts on the market activities as they systematic delays in the reporting of Reuters news
are directly linked to the currency pair. headlines, 2) the presence of informed traders trading
on private information, and 3) the retrospective
3. Empirical results
characteristics of a proportion of reported news
3.1. Aggregate news impacts. The estimation results headlines classified as unscheduled news.
for the aggregate model given in equations (1A) and
The first explanation is unlikely as intense
(1B) are presented in Table 4. The coefficients for the
competition among data providers means that the
lead and lag of both types of news variables are
extent of delay by Reuters is likely to be trivial. The
reported in panels A and B, while a summary of the
second and third explanations for the volatility
optimal combinations of AR and MA components that
response appear to be more plausible. As suggested
produce white noise residuals are reported in Panels C
by Degennaro and Shrieves (1997) and Bauwens et
and D along with some estimation diagnostics1.
al. (2005), an increase in volatility during pre-
To aid the reader in interpreting these results, Figure announcement is linked to the presence of informed
2 provides a graphical representation of the traders who exploit their private information.
information contained in Table 32. For example, Therefore, under the second hypothesis, the positive
Panel A of Figure 2 presents a plot of the estimated and statistically significant lead news coefficients
unscheduled news coefficients where the vertical suggest that some market participants possess
axis captures the size of the estimated coefficients private information regarding the content of the
1 3
While all of the ARMAX models required different ARMA lag In preliminary estimations, up to 12 leads and lags of the news
combinations, we can report that around 8 to 9 lags of both AR and MA variable were estimated for both market activity variables (i.e. 60
components were required and the estimated coefficients are mostly minutes of analysis pre-announcement and post-announcement). While
positive. This is as expected given the positive relationship between there were significant lead or lag variables with an order that was higher
volume and volatility reported in the literature. than Lead/Lag 6, these were isolated incidences where e.g. only one of
2
To conserve space, only the graphical representation of the estimated the leads in the 60 minute period was significant. In contrast, most news
coefficients is presented for all other models estimated. The unreported variables were significant for up to 30 minutes prior to and subsequent
full results are available from the authors on request. to the news release.
13Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
unscheduled Reuters news headlines obtained from in section 3.3 below. With positive and statistically
their customer order flow and hence, the ability to significant coefficients to Lag 4, the results suggest
react to these headlines before the actual time of that volatility remains at an elevated level even after
release. The third hypothesis suggests that the around 20-25 minutes after the news release.
heightened volatility is evidence of a response to a
There are two potential explanations for the volatility
real time event which is later reported in a
response to macroeconomic announcements in the
retrospective Reuters news headline up to 20-30
post-announcement period. The first suggests that
minutes after the occurrence of the event. The
the significance of news variables in the post-
Reuters News Headlines database is a collection of
announcement period indicates the presence of
different types of headlines which includes both 1)
heterogeneity in the market’s interpretation of the
real time reporting of macroeconomic events, policy
content of the news. This is consistent with the
announcements, etc. which are generally not
findings of Evans and Lyons (2008) who found that
predictable, and 2) general reporting of market
dealers in the DM/$ spot market observe
observations and events that have already occurred.
macroeconomic announcements, but have little idea
As a result, it is likely that both types of news could
of how to interpret it, or how the rest of the market
account for the significant leads. The volatility
will interpret it. In the second, the volatility
response graph supports this hypothesis as volatility
response is seen as a reflection of surprised
diminishes around 5 minutes subsequent to the news
reactions by market participants and the closing of
release, indicating that the impact of news on
positions based on prior anticipations (Bauwens et
volatility tapers off shortly after the release as more
al., 2005). Interestingly, Lag 5 is statistically
market participants become aware of the event and
significant at 1% with a negatively signed
adjust their views accordingly.
coefficient. This suggests that volatility generally
The second column of Panel A shows that the diminishes around 25-30 minutes after
impact of unscheduled Reuters news on volumes in macroeconomic announcements which can be
the AUD is markedly different to that of the impact interpreted as a market correction to an overreaction
on the volatility. All of the estimated coefficients in the variation of USD/AUD quotation which
are significant at the 1% level and they indicate that follows an announcement.
volumes in the USD/AUD generally rise prior to the
news announcement, peak in the contemporaneous The impact of the scheduled news on volume are
period and fall in the post-announcement period. shown in the second column of Panel B. The
The significance of the pre-announcement news volume effect generally increases in magnitude up
variables suggest the presence of anticipatory trades to the time of the announcement, however unlike
and the possible access to private information by a unscheduled news where all of the coefficients were
segment of the market. This is consistent with the significant, here the significant news variables are
implications suggested by the volatility response to concentrated in the pre-announcement periods. This
unscheduled news as discussed above. The can be driven by two factors: 1) a greater number of
significance of the post-announcement news variables anticipatory or speculative trades initiated on the
indicates evidence for market heterogeneity in the ‘private’ information, or 2) by traders who rebalance
interpretation of news content for up to 30 minutes. their positions in order to avoid announcement
Table 4 and Panel B of Figure 2 report the estimated ‘surprises’. We conjecture that while the market is
coefficients for the impact of aggregate scheduled able to make anticipatory trades prior to the release
news events. For volatility, positive and statistically of macroeconomic announcements (as seen in the
significant coefficients at the 1% level are found for statistical significance of Lead 2 and Lead 1 at 1%),
lead 2, 1 and 0, indicating that aggregate macro- it does so immediately prior to the release of the
economic announcements have a positive impact on news on a less extensive scale in comparison to the
volatility. Unlike the volatility response to volatility response to unscheduled Reuters news.
unscheduled Reuters news however, the volatility Moreover, only 2 out of the 6 news lags in the post-
response to macroeconomic announcements is not announcement period are positive and statistically
concentrated in the pre-announcement period, rather it significant at least at 5%. This suggests that while
is also pronounced in the post-announcement period. there is still some evidence of market heterogeneity
The significant volatility responses shown during the in the interpretation of the news content there is less
pre-announcement periods might suggest potential heterogeneity regarding the scheduled news
information leakages surrounding macroeconomic releases. This finding is consistent with Bauwens et
announcements (e.g. private information based al.’s (2005) argument that there should be a greater
trading). However, this lacks empirical support as divergence in price post-announcement with regards
discussed in the disaggregated news impact analyses to unscheduled news announcements.
14Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
3.2. The differential impacts of impact of on- and investigation into the content of MM news explains
offshore unscheduled Reuters news. Equation 2 this pattern whereby macroeconomic related headlines
captures the volatility and volume responses to were found to account for a sizeable portion of MM
unscheduled Reuters news released during on and news. Furthermore, MM news have the highest
offshore trading hours. The estimation results are number of statistically significant variables of the three
graphically summarized in Figure 3 and reveals that news type and an examination of the magnitudes of the
the volatility response to offshore news shown in coefficients shows that it also has the greatest impact
Panel A is very similar to the aggregate volatility on the volatility response to aggregate unscheduled
response discussed in Section 3.1. In contrast, the Reuters news. Therefore, the volatility response to the
volatility response to onshore news shown in Panel aggregate unscheduled Reuters news appear to be
B of Figure 3 is markedly different as all onshore predominantly driven by MM news.
news variables, except for Lead 6, are insignificant. The volume responses to FX and FI news display a
Thus, the volatility response to the aggregate pyramid-shaped pattern of increasing and then
unscheduled Reuters news is principally driven by decreasing coefficients, that is similar to the pattern
the impact of offshore news on volatility. This result previously observed in the volume response to
addresses our first research question and provides a aggregate unscheduled Reuters news. An examination
confirmation that a major determinant of heightened of the magnitudes of the coefficients for FX and FI
volatility is the intensity of news arrival. news shows that the volume response is almost twice
The volume graph in Panel A shows the pyramid- as sensitive to FI news compared to FX news. Further,
shaped pattern that was observed for the aggregate while FX news is significant across the board, the
model originates from the offshore period. In effect of FI news is predominantly concentrated in the
contrast, the periods with significant news variables pre-announcement and contemporaneous periods,
on volume are negative for the onshore news. This suggesting the presence of anticipatory trades leading
suggests that volumes in the AUD diminish during up to the release of the news. Volume drops off
the release of unscheduled news headlines onto the considerably during post-announcement periods
market during Australian trading hours. A plausible suggesting low levels of market heterogeneity with
interpretation is that in comparison to their regards to FI news. The volume response to MM news
counterparts in the offshore markets, Australian is significant only in the post-announcement period
traders in the AUD refrain from trading around suggesting market heterogeneity. In general, we find
information events. that FI and FX are the main drivers of the volume
response to the aggregate unscheduled Reuters news.
3.3. The impact of unscheduled Reuters news by
3.3.1. The impact of scheduled macroeconomic
category type. To further investigate the results of
news by region of origin. Equation 3 also assesses the
the previous section, we estimate equation (3),
impact of Australian macroeconomic announcements
which disaggregates news into its various types. The
on volatility and volume and the estimation results are
estimated coefficients are summarized in Figure 4
summarized in Panel A of Figure 5. The volatility
and the volatility responses to FX and FI news lack
responses to Australian macroeconomic announce-
any distinct pattern. For FX news, only Lead 6 and
ments appear to take a similar pattern to the volatility
Lead 3 are statistically significant indicating that the
response to aggregate macroeconomic announce-
volatility response to FX news is concentrated in the
ments. However, there is no strong evidence of
pre-announcement period where volatility rises
information leakage as only Lead 1 is significant at
around 15-30 minutes before the release of the
1% in the pre-announcement periods. This reflects
news. As discussed previously, the significant lead
the difficulty of speculative trading on
coefficients might represent either mostly
macroeconomic announcements given its public
contemporaneous volatility responses or information
nature and suggests that anticipatory trades before
leakage on some of the news by about 10 minutes.
unscheduled Reuters news are more prevalent than
The contemporaneous periods (Lead 1 and Lag 0)
anticipatory trades before macroeconomic news as
for FI news are statistically significant at 5%,
private information related to unscheduled news
suggesting a lack of information leakage for FI
might be more accessible to market participants.
news. MM news have the greatest number of
statistically significant news variables, most of Where U.S. announcements are considered however,
which are concentrated in the post-announcement the volatility responses show a different pattern
period. This pattern suggests some heterogeneity in (Panel B of Figure 5). U.S. announcements have a
the interpretation of MM news content by the positive and statistically significant impact at 1% at
market and resembles the volatility responses to Lead 1 and at Lag 4 (around 20-25 minutes after the
macroeconomic announcements rather than the release of the news). This provides some evidence for
volatility response to unscheduled Reuters news. An market heterogeneity in the interpretation of the news
15Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
content in U.S. macroeconomic announcements, but economic announcements, there is still sufficient
this evidence is relatively weak in light of the evidence to surmise that the aggregate macroeconomic
negative Lag 3 which is statistically significant at announcements are driven by the Australian and the
5%. The lack of significance and relatively smaller U.S. macroeconomic announcements. This suggests
estimated coefficients of the volatility response to that the AUD market is mostly determined by the
US macroeconomic announcements compared to bilateral information flows between the U.S. and
Australian macroeconomic announcements suggest Australia. Closer examination of the source of the
that the latter are a more significant driver of the announcements however, does reveal some interesting
aggregate volatility response. differences between the markets response to U.S. and
Australian macroeconomic news.
The volatility and volume responses to Eurozone
and Japanese macroeconomic announcements were Conclusion
also modelled and we found that while there were
This paper investigates the impact of information
some significant news variables, the impact they had
arrival events on the realized volatility and volume
on volatility and volume were relatively minor
in the USD/AUD foreign exchange market. For the
compared to the impact of Australian and U.S.
sample period from 1 April to 30 September, we
macroeconomic announcements1. This suggests that
examined the impact of news for up to 30 minutes in
the AUD market is mostly determined by the bilateral
the pre-announcement period, and 35 minutes in the
information flows between the U.S. and Australia.
post-announcement period. This study offers an
Where we turn our attention to consider the volume economic explanation for the relationship between
response of the market to Australian macroeconomic market activities and news announcements in the
announcements, we find that the impact is primarily Australian dollar market. We provide insights into
concentrated in the post-announcement period. the nature of price discovery in the market during
Specifically, the response is greatest during the the post-announcement period, as well as evidence
contemporaneous periods (Lead 1 and Lag 0), with for the market to be able to pre-empt various types
the volume response gradually decreasing for higher of news and undertake anticipatory trades in the pre-
lags. This is in contrast to the volume response to announcement period.
the aggregate macroeconomic announcements, in Specifically, we find evidence to support the
which volume impact is concentrated in the pre- hypothesis that heightened news intensity is a key
announcement periods. This disparity suggests that driver for the dominance of offshore market activity
Australian traders are less inclined to close out their in the Australian dollar. The impact of unscheduled
positions prior to the news release in order to avoid news on volatility and volume is largely driven by
surprises in the pre-announcement periods, and 1) offshore news. Most interestingly, this study shows
experience greater levels of market uncertainty and that it is offshore money market news that is the most
2) they might be comparatively less efficient in its important determinant of AUD volatility. Fixed
absorption of the news content in the post- income and, to a lesser extent, foreign exchange
announcement periods. related news however, were found to be the most
In contrast, the volume response to U.S. important determinants of AUD volume. Finally,
macroeconomic announcements are more consistent while Australian macroeconomic announcements were
with volume response to the aggregate found to have a more consistent impact on AUD
macroeconomic announcements. Almost all news volatility, US macroeconomic news had a
considerably larger impact (while Euro and Japanese
coefficient in the pre-announcement period are
news were found to have a relatively insignificant
positive and statistically significant at 1% which
role to play).
suggests that U.S. traders are more inclined to close
out their positions prior to the news release in order The observed pattern of volatility and volume
to avoid surprises. The statistical significance of Lag responses to news may suggest that traders are
3 and Lag 4 also provides some evidence of market inclined to close outpositions in the pre-
heterogeneity in interpreting the U.S. macro news. announcement period to avoid surprises and provides
evidence for market heterogeneity in the interpretation
Despite some differences in the pattern of volume of the news content. Moreover, while there is evidence
responses between the aggregate macroeconomic of heightened volatility in the pre-announcement
announcements and U.S. and Australian macro- period for unscheduled news (due to private
information and retrospective headlines) the volatility
response to macroeconomic announcements suggests
1
These results are not reported to save space. Interested readers may that there are few information leakages and less
obtain them from the corresponding author. anticipatory trading as a result.
16Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
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Appendix
Table 1. Summary statistics for USD/AUD volatility and volume
Indicative quotes
Volatility Volume
a. Raw series
Mean 0.19 156.8
Variance 0.11 5880.62
Skewness 11.32 0.27
Kurtosis 229.51 -0.11
b. De-seasonalized series
Mean 0.75 2.46
Variance 1.05 2.54
Skewness 3.76 0.23
Kurtosis 20.33 -0.64
17Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
Table 2. Reuters news headline categories
All
FX News FI News MM News
news
Frequency 11421 9085 1741 595
% of all news - 79.50% 15.20% 5.20%
Onshore 2446 1928 287 231
% of all news 21,40% 16.90% 2.50% 2.00%
Offshore 8975 7157 1454 364
% of all news 78,60% 62.70% 12.70% 3.20%
Headline
n/a Dollar to rebound versus, euro, pound í BNP European credit spreads tighter RBA head says 4% inflation likely
examples
US 2Y swap spread narrows with Libor Money mkt rates remain high, spreads
Lehman shorts euro/dollar
broadly lower wide
Dollar seen gaining ground ahead of G7 Treasury yield curve flattest since BOJ gov nominee: Japan economy
meeting February faces risks
Lower volatility and buying buoying
Won lead Asian FX down vs firmer dollar 3-month Euribor slips to 4964 pct
mortgage bonds
Watch resistance at 10576 in USD/CAD í Treasuries dip in Asia, eye bailout Nomura sees TED spreads shrinking in
RBC approval mid-Q4
Table 3. Summary statistics of macroeconomic news announcements
Announcement Frequency Local time AEST time
Australian announcements (total = 31)
CPI 2 11:30 am 11:30 am
GDP 2 11:30 am 11:30 am
International trade on goods and services 6 11:30 am 11:30 am
Retail sales 7 11:30 am 11:30 am
Unemployment rate 6 11:30 am 11:30 am
Current account 2 11:30 am 11:30 am
RBA cash rate target announcements 6 2:30 pm 2:30 pm
US announcements (total = 36)
CPI 6 8:30 am 10:30 pm
GDP 6 8:30 am 10:30 pm
Trade balance 6 8:30 am 10:30 pm
Advance retail sales 6 8:30 am 10:30 pm
Unemployment rate 6 8:30 am 10:30 pm
Current account 2 8:30 am 10:30 pm
FOMC announcements 4 2:15pm (D-1) 4:15 am
Eurozone announcements (total = 51)
CPI 12 11:00 am 7:00 pm
GDP 6 11:00 am 7:00 pm
Trade balance 8 11:00 am 7:00 pm
Retail trade 6 11:00 am 7:00 pm
Unemployment rate 6 11:00 am 7:00 pm
Current account 4 11:00 am 7:00 pm
Employment 3 11:00 am 7:00 pm
ECB announcements 6 1:45 pm 9:45 pm
Japanese announcements (total = 38)
CPI 6 8:30 am 9:30 am
GDP 4 8:50 am 9:50 am
Trade balance 6 8:50 am 9:50 am
Retail trade 6 8:50 am 9:50 am
Unemployment 6 8:30 am 9:30 am
Tankan survey 1 8:50 am 9:50 am
BOJ announcements 9 11:00 am – 1:00 pm 12:00 am – 2:00 pm
Notes: The frequency of news reported correlates to the data samples containing 37716 observations before the exclusion of non-
trading intervals. The exclusion of non-trading intervals leads to the omission of 5 macro announcements in the indicative and inside
quote data sample and 6 macro announcements in the trade data sample. BOJ’s announcements do not have a set time but usually
fall between 11am – 1pm local time.
18Investment Management and Financial Innovations, Volume 11, Issue 4, 2014
Table 4. The aggregate news model
The table below presents the results from estimation of the ARMAX(p,q) for Model 1: The aggregate model.
RVt ,i orVlmt ,i D 0 ¦ b jUNewst ,i j ¦ c j SNewst , i j ¦ d gVlmt ,i g ¦ e p RVt ,i p H t ,i ¦ f qH t ,i q (1)
j j g p q
Panel A: Volatility Panel B: Volume
Coeffa) p-value Coeffa) p-value
Constant b) 0.664*** {0.0000} Constant b) 2.4707*** {0.0000}
Unscheduled Unscheduled
Lead 6 0.0208*** {0.0015} Lead 6 0.0403*** {0.0000}
Lead 5 0.0056 {0.4159} Lead 5 0.0351*** {0.0000}
Lead 4 0.0200*** {0.0038} Lead 4 0.0509*** {0.0000}
Lead 3 0.0215*** {0.0019} Lead 3 0.0753*** {0.0000}
Lead 2 0.0095 {0.1719} Lead 2 0.0681*** {0.0000}
Lead 1 0.0216*** {0.0020} Lead 1 0.0738*** {0.0000}
Lag 0 0.0187*** {0.0074} Lag 0 0.0856*** {0.0000}
Lag 1 0.0157** {0.0243} Lag 1 0.0756*** {0.0000}
Lag 2 0.0030 {0.6689} Lag 2 0.0585*** {0.0000}
Lag 3 0.0169** {0.0157} Lag 3 0.0641*** {0.0000}
Lag 4 -0.0031 {0.6504} Lag 4 0.0384*** {0.0000}
Lag 5 0.0071 {0.2992} Lag 5 0.0385*** {0.0000}
Lag 6 0.0024 {0.7182} Lag 6 0.0316*** {0.0000}
Macro news Macro news
Lead 6 0.0252 {0.6503} Lead 6 0.0580 {0.2998}
Lead 5 0.1354** {0.0203} Lead 5 0.1913*** {0.0014}
Lead 4 0.0186 {0.7538} Lead 4 0.2391*** {0.0001}
Lead 3 0.1340** {0.0254} Lead 3 0.1618*** {0.0099}
Lead 2 0.2771*** {0.0000} Lead 2 0.2189*** {0.0005}
Lead 1 0.4030*** {0.0000} Lead 1 0.4105*** {0.0000}
Lag 0 0.3482*** {0.0000} Lag 0 0.2356*** {0.0002}
Lag 1 0.2081*** {0.0006} Lag 1 0.1211* {0.0578}
Lag 2 0.1854*** {0.0020} Lag 2 0.1602** {0.0115}
Lag 3 0.1409** {0.0187} Lag 3 0.0495 {0.4303}
Lag 4 0.1769*** {0.0029} Lag 4 0.2018*** {0.0011}
Lag 5 -0.1671*** {0.0042} Lag 5 0.0520 {0.3856}
Lag 6 -0.0689 {0.2148} Lag 6 0.0188 {0.7372}
Panel C: Volatility diagnostics Panel D: Volume diagnostics
BIC Selected ARMAX ARMAX (8,10) BIC Selected ARMAX ARMAX (10,9)
BIC 431 346.98 BIC 432 808.42
Log likelihood -38 089.43 Log likelihood -38826.55
Adjusted R2 0.4995 Adjusted R2 0.7283
Q(10) 1.3170 (0.9994) Q(10) 3.7313 (0.9587)
Observations 33 827 Observations 33 827
Notes: a) Significance at the 1%, 5% and 10% significance levels is signified by ***, ** and * respectively. b) For presentation
purposes, the autoregressive component, the white noise component and volume (volatility) for the volatility (volume) model are not
presented.
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