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

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j jj

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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

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Figure 4

Intraday Return Volatility, No vs. CZ Announcement Days

0

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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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