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IMPACT OF MACROECONOMICRELEASES ON HIGH FREQUENCY
EXCHANGE RATE BEHAVIOR:THE CASE OF THE CZECH CROWN/USD
SPOT EXCHANGE RATE
Marek KOPECKÝ
Discussion Paper No. 2004 � 115
January 2004
P.O. Box 882, Politických vězňů 7, 111 21 Praha 1, Czech Republichttp://www.cerge-ei.cz
Impact of Macroeconomic Releases on High Frequency Exchange Rate Behavior:
The Case of the Czech Crown/USD Spot Exchange Rate
by
Marek Kopecky
Supervising Faculty Member: Dr. Evžen Kočenda
Abstract:
By using high frequency exchange rate data I examine the reaction of the Czech
Crown/USD spot exchange rate to public macroeconomic announcements emanating from the
U.S. and the Czech Republic1. I directly test the efficient market hypothesis. By using data
spaced at 5-minute intervals I identify significant impacts of the news on the exchange rate and
its volatility, and test for the presence of announcement specific effects. Analysis of the volatility
yields a spike in the ten minutes following the Czech announcements, however, tests of efficient
market hypothesis do not give support to any announcement specific effects due to Czech
macroeconomic announcements. The volatility of CZK/USD returns does not increase following
the U.S. announcements but surprises in the U.S. Unemployment, PPI and CPI are shown to have
a significant effect on the CZK?USD returns in the period five and ten minutes past the
announcement. This article contributes to the existing efficient market hypothesis research by (to
my knowledge) being the first paper which examines the high frequency exchange rate in a
transition economy such as the Czech Republic while using announcements from both countries
giving rise to the exchange rate2.
1 I would like to thank Olsen and Associates for their generous educational discount. My thanks go also to theEconomics Department of the University of North Carolina at Charlotte for their generous help in paying for part ofthe data and to the World Bank Research fellowship which allowed me to purchase additional years of data. Mythanks go also to Ing. Jan Vejmelek for his generosity in providing the Reuters’ survey data, to Dr. Evžen Kočendafor his patience and many helpful comments and to Jozef Knap for his help with data manipulation.
2 Podpiera (2000) examines, among other things, the reaction of the exchange rate quoted at daily interval to selectedCzech macroeconomic announcements, but does not use macroeconomic announcements from the other country
1
1 Introduction
Many empirical studies have been performed in the past to analyze the reactions of
markets to macroeconomic announcements. The majority of these studies focused on developed
countries whose markets are documented to process information efficiently3. Research on
markets of developing or transition economies is much less frequent. The intended contribution
of this article lies in the fact that when one considers the Czech Republic as an economy in
transition, analyzing the evolution of the measures of market efficiency will shed important
insights on how efficient the market has become over the years. The time period under study is
marked by factors characteristic for a transition economy.
The Czech Crown has been under managed float since 1997. There was a period of
downturn and recession in the years 1997-1999. Inflation targeting was implemented and
continuing privatization caused large money inflows into the economy. Foreign investment
played a large role in the changing structures of firms. The years 1997-2002 were indeed
transformation years where the market, which itself was evolving, had to process a large amount
of information. I will study the market efficiency and its behavior as the degree of market
efficiency has implications not only for traders and seekers of arbitrage profits but also for policy
makers.
The remainder of the paper is structured in the following manner: Section 2 contains the
Review of related literature. The methodology employed in the study and the data are discussed
in Section 3. Section 4 presents the main empirical findings. Section 5 concludes the paper and
considers possible directions for future research. The Appendix contains a table with a
description of the macroeconomic announcements used and a graph showing the operation of the
major world markets and the timing of the announcements.
3 Classical examples are Hakkio and Pearce (1985), Ito and Roley (1987), Ederington and Lee (1993), Almeida,Goodhart and Payne (1998) and Andersen et al. (2003)
2
2 Review of Related Literature
The 1970’s saw heightened activity in work regarding models of exchange rate
determination. The study of the exchange rate determination at that time could be divided into
three main streams: 1) purchasing power parity, 2) monetary approach via balance of payments
and 3) asset market/portfolio balance approach. While all three approaches have at least some
merit, the models they produced fared relatively poorly as far as their predictive power was
concerned when compared to a random walk model of the exchange rate (Frankel and Rose,
1995).
The 1980’s saw the rise of a new strand in the literature. The focus shifted, at least to a
certain degree, towards considering the shorter-run or more immediate effects of certain
phenomena on the behavior of prices of financial instruments. The scope of this literature not
only focuses on considering any possible effects of macroeconomic news on the prices of
financial instruments under study, but also on the market’s incorporation of such news. These are
the so-called efficient market hypothesis tests. In essence, an efficient financial market
incorporates all available information and the value of this information is reflected in the price of
the instrument under study.
Pearce and Roley (1985) study the daily response of stock prices to money supply,
inflation, industrial production, unemployment rate, and the discount rate. The S&P 500 index
serves as a proxy for the market. The study covers the period 1977-1982. The authors find that
news related to monetary policy does affect stock prices in a significant way. The expected part
of the announcements has no significant effects on the stock price, this result being consistent
with the efficient market hypothesis. In cases of some announcements, there is evidence that the
effect of the news on the stock prices carries over to the next day, that is, sometimes the market
3
appears not to incorporate all the information in a quick manner as predicted by the efficient
market hypothesis.
Smirlock (1986) complements the studies of market responses to news by examining the
“response of the long-term bond market to inflation announcements”. While the previous studies
focus on the short-term interest rates, spot exchange rates, and stock prices, with findings that
generally support market efficiency, Smirlock ventures to study the news effects on a slightly
different financial market than those used in past studies. Smirlock finds that unexpected
inflation measured by CPI and PPI has a significant impact on long-term rates for the period
1978-1983. The response does not allow the distinction between the expected inflation and the
policy anticipation hypotheses. Smirlock’s results support efficient market hypothesis with the
announcement adjustment being complete by the end of the day on which the announcement is
made.
In contrast to the previous studies Hakkio and Pearce (1985) use exchange rate sampled at
market open, noon, and market close. They study “short-run responses of exchange rates“ to
money stock changes, inflation, and changes in real activity for the period 1977-1984. This
“shortening” of the sample interval allows for potentially better detection of news effects on the
exchange rate as the efficient market hypothesis predicts quick market response to any
unanticipated news. The exchange rates under study are those of the dollar, DEM, UK, Swiss
franc, JPY, Canadian dollar, French franc, and Italian lira.
Hakkio and Pearce find that the exchange rates under study do react to unanticipated news
regarding the money stock, but not to the unanticipated part of the news of the other
macroeconomic variables under study. This might be due to the “coarseness” of the sampling
interval. As will be discussed below, studies using much shorter sampling intervals find the
reaction to occur within the first couple of minutes after the announcement. The results tend to
4
support the efficient market hypothesis and the reaction to the news appears to be complete
within 20 minutes after the announcement.
While the work discussed above made much progress (and raised many new questions), it
shares a major drawback. The studies mentioned so far only utilize macroeconomic news
originating in the United States. Ito and Roley (1987) study the reactions of the JPY/USD
exchange rate to Japanese and U.S. news regarding money supply, industrial production, and PPI.
The exchange rate data set consists from U.S. market open, noon, and close, and the Japanese
market open and close. The time difference between the two countries allows for the separation
of news effects emanating from the two countries. The authors find that the JPY/USD exchange
rate during the period 1980-1985 reacted primarily to the news emanating from the U.S. The
news from Japan had a significant impact on the exchange rate only in the case of industrial
production and money supply in one sub-period in the sample. The authors’ main contribution to
the literature of news effects on exchange rates consists from the use of news from both of the
countries which give rise to the exchange rate under study.
The 1990’s were characterized by two factors, which when considered together, allowed
for dramatic advances in the field. These were the cheaper and yet more powerful PC’s and the
increasing availability of high-frequency exchange rate data sets. The technology of the 1990’s
could handle large high frequency data sets containing literally tens of millions of observations at
a low cost. And the vendors of these data sets, which prior to the 1990’s were mostly sold to
business customers at higher prices, became willing to sell their data to academic institutions at
sizeable discounts. The two “technical” ingredients necessary for the rise of high frequency
finance were in place. The third ingredient - the human appetite in academia to utilize such
possibilities was most likely ever present. Thus, the issue regarding the before ever-lurking
“coarseness” of the sampling interval could be resolved.
5
Classical examples of such work utilizing high frequency exchange rate data are three
papers by Ederington and Lee (1993, 1995, 1996). The authors analyze the impact of scheduled
macroeconomic releases on exchange rates, interest rates and their volatilities. In their 1993
paper, Ederington and Lee examine impact of news on exchange rates, interest rates, and their
volatilities for the period 1988-1991 with the rates sampled at 5-minute intervals (Ederington and
Lee, 1993). They find significantly increased volatility in the five-minute interval following the
news release.
The releases with the most significant impact on interest rates were employment, CPI, and
PPI. The DEM/USD exchange rate was most affected by employment report, merchandise
deficit, and PPI. The authors further find that the greatest part of the adjustment occurs within the
first minute after the news release with some adjustments occurring up to 15 minutes after the
news. Their results are a significant improvement over the previous studies as they demonstrate
that the adjustments to news tend to be very rapid and therefore might have been lost in the
previous analyses using “coarser” sampling intervals.
The 1995 article is a refinement of Ederington and Lee (1993) using data sampled at 10-
second intervals. By using finer sampling intervals, the adjustment period for most news releases
is found to be 40 seconds after the news release. The results indicate some overreaction in the
first 40 seconds with a subsequent correction in the following 2 minutes. In their 1996 article,
Ederington and Lee (1996) compare the exchange rate and interest rate volatility before and after
scheduled releases and after unscheduled macroeconomic releases. They find that since the time
of the scheduled announcements is known, volatility will be lower shortly after the
announcement, i.e., shortly after the adjustment. In the case of the unscheduled announcement,
the reverse is observed. Since the announcement was not anticipated, the volatility after the
6
announcement will increase in response to the expectation of another unexpected announcement.
Thus, the authors’ findings tend to support the efficient market hypothesis.
Up to this point, it was a standard practice that the forecasted announcement series, or the
so-called “expected” components of the news were taken from the Money Market Services
International, a company gathering survey forecasts. When testing the efficient market
hypothesis, the researchers assumed these forecasts to be rational. However, in their 1995 article,
Aggarwal, Mohanty, and Song (1995) examine the rationality of forecasts used in most studies
concerned with market reaction to macroeconomic news and find that forecasts of some
macroeconomic series used in past studies are not rational, since past information can be used to
improve the forecasts.
While it has been shown by Mussa (1979) that the log of the exchange rate is a virtual
random walk, Payne (1997) finds some seasonality in DEM/USD volatility and the effects of
news on the exchange rate. In general, however, the results conform to the findings of Ederington
and Lee (1993). The response of the market is rapid, with increased volatility following the
announcement. The announcements with the greatest impact on the exchange rate are the
employment report and mercantile trade figures.
While ignoring the seasonality issue, Almeida, Goodhart, and Payne (1998) study the
response of the DEM/USD exchange rate to scheduled macroeconomic news. Following Ito and
Roley (1987), the authors utilize macroeconomic news emanating from both of the countries that
give rise to the interest rate. They find that in both cases the market reaction is rather quick,
lasting two hours at most. The German news tends to be incorporated more slowly and has lesser
impact on the exchange rate than the U.S. news.
Example from an emerging market in transition is Podpiera (2000) who tests the efficient
market hypothesis in the Czech Republic, using Czech macroeconomic announcements for the
7
period 1997-2000. He finds that the surprises contained in the announcements affect market
behavior several days after the announcement. The market also responds to the expected part of
the news for a period of up to several days. In the case of the CPI, the market efficiency appears
to improve with time. This is the first study examining market efficiency in the Czech Republic.
The major shortcoming in Podpiera’s work is the fact that he fails to account for U.S. news in the
case of the CZK/USD exchange rate.
In the latest published work in the field, Andersen et al. (2003) examine the response of
the conditional mean of the exchange rate to 28 U.S and 14 German macroeconomic
announcements. The authors use a data set spanning seven years, the longest high frequency data
set so far. The main findings are consistent with previous work. The announcements do matter
in the sense that there is a statistically significant sudden increase in the conditional mean and it
occurs very shortly following the announcement (Andersen et al., 2003).
The authors also compute impact curves, documenting the asymmetric reaction of the
mean to the surprise announcements. The “bad” surprise part of the announcement tends to have
a higher impact than the corresponding “good” part. The authors do not analyze the behavior of
exchange rate volatility, nor do they consider any structural stability issues in relationship to the
individual announcements’ effects.
Advances in technology together with changes in our thinking about the factors affecting
the exchange rate in time “windows” of various length have led to a multitude of articles
analyzing this issue. While many questions remain unanswered or perhaps even have not yet
been raised, several stylized facts tend to emerge from the results of the research done up to date.
Macroeconomic announcements do, in a statistically significant manner, affect the
behavior of the exchange rate and its volatility. The fact that such adjustments occur over very
short time periods (minutes after the announcement) requires the use of high frequency exchange
8
rate data sets. Before employing the forecasted series of the announcements, it is necessary to
check for the rationality of such forecasts. Some forecasted series have been found not to be
rational in the sense that past values of such forecasts allow for their improved predictability
(Aggarwal, Mohanty and Song, 1995). It is possible that the market participants are aware of this
fact and utilize it to their advantage. In such cases, using just the actually reported forecasts
values without considering the forecasts’ history would invalidate any of the efficiency tests.
When the length of the data set increases, it is necessary to realize that the effects found might
lack structural stability, as the underlying process driving the response, either its direction or size,
might very well change. The above general characteristics have been found to hold for relatively
efficient markets of developed market economies. The aim of this article is to help shed the light
on the workings of these processes in an economy in transition.
3 Methodology and Data
3.1 Methodology
To examine the intraday volatility I will use an approach similar to the one employed by
Ederington and Lee (1993). I will calculate log “returns” or changes, ln(Pt/Pt-1), in the CZK/USD
exchange rate in five-minute intervals. I will then calculate the standard deviations of these log
“returns”, which I will separate into subsets according to trading days with a) at least one
announcement from either country, b) at least one announcement from the U.S., c) at least one
announcement from the Czech Republic.
This will allow me to study the potential effects of the announcements in a very precise
fashion. The various super-impositions of different graphs of the standard deviations should
serve as a rough indicator of the effects of news releases on the exchange rate volatility. As most
9
U.S. releases occur at 8:30, and one at 9:15EDT (this translates to 14:30-15:15 Czech local
time=GMT+1), the a priori expectation is that the volatility measured by the standard deviation
will be higher “somewhere” on the noon to close interval for the days with at least one U.S.
announcement as compared to the volatility on days without any announcements. The Czech
news releases occur at 9:00 Czech local time, therefore, there is an a priori expectation of a higher
volatility “somewhere” during the open to noon interval on days with at least one Czech
announcement as compared to days with no announcements. Imposing a five-minute grid on the
tick-by-tick data will allow me to trace the increased volatility with greater accuracy.
To better appreciate the operation of the various exchanges and the timing of the
announcements, I refer the reader to the two figures contained in the Appendix which depict the
hours of operations of major markets and the timing of the announcements with their brief
descriptions.
Before we can proceed with the analysis itself, an important issue having a direct impact
on the intended analysis should be addressed: the rationality of the survey forecasts. Previous
studies such as Pearce and Roley (1985) and others use survey forecasts to test the efficient
market hypothesis in the form similar to Almeida, Goodhart and Payne (1998), presented in
Equation 3 below. However, without accounting for the stationarity of the forecast variables, the
estimates will be biased downwards.
In other words, if the market participants are rational, they will incorporate all information
available up to time t into their decision making process. If the forecasts of the survey variables
are not stationary, they can be improved by simple ARIMA(p,d,q) processes. Thus, using survey
forecasts without pretesting them for stationarity opens the possibility of capturing a spurious
relationship and misrepresenting the effects of the true decision-making process employed by the
market participants (Aggarwal, Mohanty and Song, 1995). Therefore, the forecast survey
10
variables need to be pre-tested for unbiasedness. I will utilize the test based on Muth’s 1961
article as presented in Equation 1.
If at least one of the conditions above is not satisfied, hypothesis of unbiasedness must be rejected
(Aggarwal, Mohanty and Song, 1995). As was already mentioned, the time series under study
must be stationary, otherwise, a bias toward rejecting the unbiasedness will be present. To test
for stationarity of the survey series, I will utilize the Augmented Dickey Fuller and the KPSS tests
(Dickey and Fuler, 1979, 1981, Kwiatkowski, Phillips, Schmidt and Shin, 1992). While both of
these tests are used to check for the existence of a unit root in the residuals they diametrically
differ with regard to their hypotheses. The null hypothesis of the ADF is that the data contain a
unit root, i.e. they are non-stationary. The null hypothesis of the KPSS test is that the data do not
contain a unit root and are therefore stationary.
In particular, the ADF is an improved version of a Dickey-Fuller test originally designed
to test for the presence of unit roots in an AR(1) process. The ADF allows us to carry out the test
for unit roots for AR processes of order p. The critical values for the ADF test tabulated by
Dickey and Fuller provide a guideline for either rejecting or failing to reject the null hypothesis of
non-stationarity. When performing the ADF test it is necessary to take care when selecting the
number of lagged differences used in the test. To select the appropriate value we use an iterative
procedure by checking for the last significant lag of the differences starting with lag 8 and going
down. In case none of the lagged differences is significant we use the original Dickey Fuller test.
11
10)(1,0: 1010 , tte
tt EandgivenYY
The KPSS test is an alternative test for non-stationarity. Whereas the ADF tests for non-
stationarity versus stationarity, the KPSS tests stationarity versus difference stationarity. There
are two sets of critical values designed for the KPSS test which allow us to test for level and trend
stationarity. The expectations’ series data are tested for both and the differenced data for level
stationarity since the trend was already removed from them by differencing. The null hypothesis
of the test is that the samples under study come from the same population. Often, the failure to
reject the null hypothesis is interpreted as that the means of the two populations are the same.
However, just because the populations are different does not, in the strict sense, mean that the
means are different as well, for it could be that only the variances differ (Kruskal and Wallis,
1952).
For this reason, I will calculate the Brown-Forsythe-modified Levene F-test used by
Ederington and Lee (1993) to test for the homoskedasticity of the return variates. Conover,
Johnson, and Johnson (1981) find the Brown-Forsythe-modified Levene F-test robust to non-
normality and to be among the most powerful of fifty examined tests for homogeneity of
variances. Lockwood and Linn (1990) use this F-test to test for homogeneity of intraday return
variances. The test will be conducted three times for days with U.S. announcements , days with
Czech announcements, and days with at least one announcement. The Brown-Forsythe-modified
Levene test statistic is calculated according to the Equation 2 presented below. It is distributed
with FJ-1,N-J under the null hypothesis of homoskedasticity.
12
J
jj
n
tjtj
n
ttj
J
jj
tjtj
jtj
nj
t
J
j
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j jj
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M
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11
_
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,D
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M,
j intervalfor median thefromdeviation absolutemean theis )/(
mean grand theis )/( testin the included days nover j intervalfor median sample is
2j interval day t,for return theis r and - where)1()(*
)(
)(
To analyze the impact of individual news releases on the exchange rate I employ a model
directly stemming from the efficient market hypothesis and presented in Equation 3.
error term theis , t at time i series of valueannounced actual , , valueexpected
(3) t at time quote CZK/USD theis , )/ln( ,
,
,,,,,
,,,2,1,
ti
tietiti
uti
eti
tkttktitie
tiu
tikti
xxxxx
PPPrxxr
If the market is efficient, only the unexpected part of the announcement should have any
discernible effects on the exchange rate, i.e., only the 1 coefficient should be significantly
different from zero. Finding a statistically significant coefficient on 2 would lead to the rejection
of the hypothesis that the market is efficient.
3.2 Data
The exchange rate data cover the period 1997 – 2002. The tick by tick data set comes as a
continuous set of bid and ask quotes with a time stamp. The announcements of the actual/official
data used in this paper consist of U.S. and Czech macroeconomic announcements for the same
period as above. The U.S. announcements come from different agencies of the U.S. federal
government. The Czech announcements come from the Czech Statistical Office, a governmental
institution responsible for gathering and releasing all country-related data. The announcements of
13
expected values of the macroeconomic releases come form Money Market Services International,
via Business Week’s the “Week Ahead” section for the U.S. expectation series, and from the
Reuters’ Survey of Analysts and Economists for the Czech expectation series. Both expectations
surveys are based on the forecasts of 30 to 40 leading practitioners and are released on the Friday
prior to the announcement of the official figures. The knowledge of the actual and expected
values for each series allows for the decomposition of the actual announcement into the expected
and unexpected parts. According to the efficient market hypothesis, only the unexpected part of
the announcement should have any effect on the behavior of the exchange rate.
Almeida, Goodhart and Payne (1998) test the MMS International survey data for
rationality and despite an opposing view presented by Aggarwal, Mohanty, and Song (1995), they
conclude the data to be unbiased and suitable for use, a result also found by Pearce and Roley
(1985). I will perform a test for the rationality of the forecasted data before any further analysis.
The list of the announcements employed in the analysis is as follows: for the U.S., the
CPI, PPI, Industrial Production, Foreign Trade, Employment, and Durable Goods Orders are
used. These announcements have been found to significantly affect the DEM/USD exchange rate
in studies by Almeida, Goodhart, Payne (1998) and Ederington and Lee (1993). For the Czech
announcements the PPI, CPI, Industrial Production, and Foreign Trade are used. These
announcements were found to have significant effects on the Czech financial markets in Podpiera
(2000).
4 Empirical Results
4.1 Unbiasedness of the Expectations Series
As mentioned above, the issue of unbiasedness or stationarity of the expectations’ series
14
has a direct bearing on any empirical findings. Under the efficient market hypothesis, the market
participants are expected to utilize all available information which is then reflected in the prices
prevailing at the market. Nonstationarity present in the expectations’ series may be exploited via
an ARIMA(p,d,q) process. The results of both the ADF and the KPSS tests for stationarity are
summarized in Figure 1. Detailed results for each series may be found in the Appendix.
Both the ADF and the KPSS tests were employed on the levels of all 10 time series and
on the differences where necessary. Of the U.S. announcements, Durable Goods Orders, PPI and
CPI were found to be level stationary by both tests. The Industrial Production series was found to
be trend stationary. Based on the results of the tests, the International Trade series is difference
stationary. Only in the case of the U.S. Unemployment did the results of the two tests differ.
While both test performed on levels point to nonstationarity, the ADF test on the differences
allows us to conclude stationarity whereas the results of the KPSS test on the differences lead us
to conclude nonstationarity4.
As far as the Czech announcement series are concerned, based on the results of the two
tests, the Czech PPI and CPI were found to be trend stationary. The International Trade series
was found to be level stationary. The two tests produce conflicting results in the case of the
Czech Industrial Production. The results of the ADF test suggest the series to be nonstationary in
levels whereas the results of the KPSS suggest stationarity. Both tests find the series stationary in
first differences. These findings indicate that for the greater part, the announcements’ series
under study are stationary suggesting that the market participants employ nearly all available
information when forming their expectations.
4 Tables with detailed results of both the ADF and the KPSS tests are available upon request.
15
Figure 1
Comparison of the ADF and KPSS Test Results ADF KPSS Trend and Level Level Level Trend U.S. Announcements USDGR S SUSUNM NS NSDUSUNM S NSUSPPI S SUSCPI S SUSINDPR S SUSINTR NS NSDUSINTR S S CZ Announcements CZPPI S SCZCPI S SCZINDPR NS SDCZINDPR S SCZINTR S SS in case of the AFD test denotes the fact that the H0 of unit root was rejected at α=5%. S in case of the KPSS test denotes the fact that the H0 of stationarity could not be rejected at α=5%NS in case of the AFD test denotes the fact that the H0 of unit root was not rejected at α=5%.NS in case of the KPSS test denotes the fact that the H0 of stationarity was rejected at α=5%.
4.2 Intraday Return Volatility
The graphs of the intraday return volatility for the various sets of days are shown in
Figures 2-4, depicting the volatility of the returns in five minute intervals for the No
Announcement vs. Any Announcement Days, No Announcement vs. U.S Announcement Days
and the No Announcement vs. Czech Announcement Days respectively.
The time interval for all three graphs is from 7:45 GMT until 21:15 GMT. The times
were chosen to capture the volatility 15 minutes before the opening of the London Stock
Exchange and the release of the Czech announcements at the same time. The final time of 21:15
16
GMT coincides with the time 15 minutes after the closing of the NYSE. Since a great majority of
the Czech and the U.S. announcements occur on same days, the graphs of country specific
announcement days were constructed in such a manner as to capture all of that country’s
announcement days, without excluding days where announcements from the other country
occurred as well.
The visual inspection of Figure 2 reveals that for the No vs. Any Announcement Days,
there is a large spike between the time 8:00 and 8:10. This corresponds to the time when Czech
Announcements are released. During this time, the volatility is 7 times its average value over the
graphed time interval where it remains practically flat. For the volatility on U.S. vs. No
Announcement Days shown in Figure 3, the expected spikes at and/or after the U.S.
announcement times of 13:30 and 14:15 do not show. This could be either because of the still
relatively coarse interval, since previous studies show the U.S. reactions to occur with a few
minutes after the announcement or because the effect is simply not there. Attempts to refine the
interval to less than five minutes failed due to the limitations of software available. Figure 4, the
case of the Czech vs. No Announcement Days, presents a very similar picture to that in Figure 2
except that now the volatility during the 8:00-8:10 window is more than 10 times its average
value.
17
Figure 2
Intraday Return Volatility, No vs. Any Announcement Days
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
7:45:0
0
8:10:0
0
8:35:0
0
9:00:0
0
9:25:0
0
9:50:0
0
10:15
:00
10:40
:00
11:05
:00
11:30
:00
11:55
:00
12:20
:00
12:45
:00
13:10
:00
13:35
:00
14:00
:00
14:25
:00
14:50
:00
15:15
:00
15:40
:00
16:05
:00
16:30
:00
16:55
:00
17:20
:00
17:45
:00
18:10
:00
18:35
:00
19:00
:00
19:25
:00
19:50
:00
20:15
:00
20:40
:00
21:05
:00
Stan
dard
Dev
iatio
n
Any Anns Days No Anns Days
The results of the Brown Forsyth modified Levene F-tests are presented in Figure 5. The
results of the test indicate that within the three samples, the return volatilities differ significantly.
As the visual inspection of Figures 2-4 reveals the volatility to be flat anywhere outside the 10-
minute post-announcement window, and knowing that the volatility during the post-
announcement window is at least 7 times as high, this is where the differences have to occur.
18
Figure 3
Intraday Return Volatility, No vs. US Announcement Days
0
0.001
0.002
0.003
0.004
0.005
0.006
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0.008
0.009
0.01
7:45:0
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9:25:0
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10:15
:00
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11:55
:00
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12:45
:00
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:00
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:00
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:00
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:00
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:00
18:35
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:00
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19:50
:00
20:15
:00
20:40
:00
21:05
:00
Stan
dard
Dev
iatio
n
No Anns Days US Anns
Figure 4
Intraday Return Volatility, No vs. CZ Announcement Days
0
0.002
0.004
0.006
0.008
0.01
0.012
7:45:0
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8:10:0
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:00
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11:55
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13:10
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13:35
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:00
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:00
Stan
dard
Dev
iatio
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No Anns Days CZ Anns
19
Figure 5
Brown Forsythe modified Levene F- Testsfor the Equality of Variances
No. of No. of Critical BFmL FSample Days Obs. Value Statistic Any Anns. Days 573 165,024 1.35 17.5193U.S. Anns Days 340 97,920 1.35 12.886Czech Anns. Days 286 82,368 1.35 5.4553Critical value is taken for F120,120
Even though the spike for the U.S. announcements does not show in Figure 2 and 3, the
test still shows that return volatilities differ. As mentioned above, since many of the country
specific announcements occur on the same day, the sample U.S. Announcement Days also
includes Czech announcements which occurred on the same days as U.S. announcements. The
spike is clearly observable in Figure 3.
4.3 Impact of U.S. and Czech Announcements
To examine the impact of the U.S. announcements on the CZK/USD returns, Equation 3
was estimated in its full form. Based on the results of the initial regression for each
announcement series and the two time spans over which the returns were calculated, a more
fundamental version of Equation 3 was estimated. Specifically, if in the initial regression, which
included intercept term and the expected component besides the surprise, one or both of the
coefficients on these two terms were not significantly different from zero, a version of Equation 3
containing only the remaining significant term and the surprise was estimated.
As can be seen from Figures 6 and 7, this was very often the case. In general, this implies
that for the two time windows under study, the market participants do fully incorporate the
20
expectation of the series to be announced. For the greater part, the results could be grouped into
two categories. Either the surprise component does not play any significant role in affecting the
return in both the post 5 and 10 minute window, or the impacts, although negligible, are very
significant as. From examining Figure 6, it can be seen that surprises pertaining to U.S.
Unemployment, PPI and CPI announcements have negative impacts on the returns for both of the
time windows. Given the way the return is calculated, except in the case of CPI announcement 5
minutes past the news release, for every percentage point increase in U.S. Unemployment, PPI
and CPI, there is a 1 % decrease in the return over the interval under study. In plain terms, if one
considers rising unemployment and price levels to be “bad” news, then the results can be
interpreted so that higher than expected “bad” surprises are detrimental to the value of the dollar
or they are “good” news for the Czech Crown.
As far as Czech macroeconomic announcements presented in Figure 7 are concerned, only
surprises in the International Trade produce something close to a significant response 10 minutes
after the
actual announcement is made. This result seems rather puzzling given the large spike identified
in the intraday volatility occurring within ten minutes after the Czech announcements. Perhaps
the only plausible interpretation which can account for both the volatility spike and the lack of
significance on the surprise component of any Czech announcement series is the fact that the
London Stock Exchange opens for trading at the same time.
21
Figure 6
The Impact of U.S. Announcements on CZK/USD Returns
# AnnouncementAnnouncement Obs. Adj. R2 Constant Surprise F-statistic Matters*
Durable Goods O. 5 63 0.014 5.72E-05 1.919 No (.096) (.171) Durable Goods O. 10 63 -0.01 -3.31E-05 0.373 No (.491) (.543) Unemployment 5 67 1 0.566 -1 1.05E+23 Yes (0) (0) (0) Unemployment 10 67 1 0.569 -1 5.01E+22 Yes (0) (0) (0) PPI 5 69 1 0.567 -1 1.32E+22 Yes (0) (0) (0) PPI 10 69 1 0.566 -1 1.52E+22 Yes (0) (0) (0) CPI 5 69 0.135 -0.236 0.414 11.617 Yes (.001) (.001) (.001) CPI 10 69 1 0.569 -1 2.19E+20 Yes (0) (0) (0) Industrial Prod. 5 69 0.018 -0.001 -0.221 1.582 No (.301) (.241) (.223) Industrial Prod. 10 69 -0.017 3.32E-05 -4.014 0.471 No (.814) (.417) (.626) Int'l. Trade 5 66 0.002 -7.93E-05 1.152 No (.162) (.287) Int'l. Trade 66 -0.047 2.77E-06 0.081 No (0.953) (.922) *…at 5% significance level p-values in parentheses
22
Figure 7
The Impact of Czech Announcements on CZK/USD Returns # AnnouncementAnnouncement Obs. Adj. R2 Constant Surprise F-statistic Matters*
PPI 5 72 -0.047 -6.06E-05 1.70E-02 No (.849) (.983) PPI 10 72 -0.0002 -0.0001 7.36E-01 No (.508) (.483) CPI 5 72 0.002 0.0001 0.0001 1.15 No (.115) (.286) (.287) CPI 10 72 -0.027 0.0008 7.00E-03 No (.814) (.993) Industrial Prod. 5 71 -0.024 -8.77E-06 0.083 No (.683) (.920) Industrial Prod. 10 71 0.002 3.00E-04 1.96E-05 1.2 No (.033) (.277) (.277) Int'l. Trade 5 71 -0.011 4.95E+00 0.229 No (.625) (.634) Int'l. Trade 71 0.038 1.85E-05 3.874 No (.036) (.053) *…at 5% significance level p-values in parentheses
5 Conclusions and Proposals for Future Research
In this paper, I have studied the effects of macroeconomic announcements on exchange
rate, using high frequency data for the CZK/USD. As was discussed in the previous section, for
both the five and ten minute post announcement interval, the surprises in the U.S.
Unemployment, PPI and CPI have significant impacts on the exchange rate. The reaction of the
exchange rate to higher than expected “bad” news is a negative one for the U.S. dollar. While
this statement is supported by the results of the statistical analysis, it should be noted that the
magnitude of the effects themselves is very small, almost negligible.
Although expected, similar results were not found for the Czech announcements, despite a
large volatility spike in the period ten minutes after the Czech announcements. The alternative
23
explanation is that the spike occurs at least partially due to the fact that at the same time the
Czech announcements are made, the London Stock Exchange opens for trading. The partiality of
the effect can be seen to be supported by the fact that the volatility spike is up to seven times
higher on days with any announcement and up to 10 times higher on Czech announcement days
as opposed to non-announcement days.
The results of the present study are limiting in the fact that they only consider two specific
points in time in the post announcement “window”. Analysis of the reaction dynamics over a
finer interval would allow us to understand the price formation process as it relates to the
announcements in a more accurate manner.
24
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