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DYNAMIC ECONOMETRIC MODELS 
Vol. 11 – Nicolaus Copernicus University – Toruń – 2011 

Barbara Będowska-Sójka 
Poznan University of Economics 

The Impact of Macro News on Volatility  
of Stock Exchanges† 

A b s t r a c t. The vast of literature concerning the reaction to macroeconomic announcements 
focus on American releases and their impact on returns and volatility. We are interested if the 
news from the German and the Polish economy are significant for the stock exchanges in these 
two countries. Using high-frequency 5-minute returns from 2009-2010 we show that the periodi-
cal patterns of the German and the Polish main indices is very similar and their reaction to the 
macroeconomic announcements too. In both cases the domestic and neighbor-country announce-
ments are much less important comparing to American releases. 

K e y w o r d s: high frequency data, macroeconomic announcement, flexible Fourier form, intra-
day periodicity, volatility modeling.  

Introduction  

 A growing literature has documented the significance of macroeconomic news 
announcements in price formation process. The literature on the effect of macro news 
on returns and volatility is huge and includes surveys concerning foreign exchange 
market (Bauwens, Omrane, Giot, 2003), bond market (Dominguez, 2003) and equity 
market (Hanousek, Kocenda, Kutan, 2008). It is worth to stress that surveys focused on 
FOREX are most popular and exhaustive (see for instance works of Andersen and 
Bollerslev, 1998, Faust et al., 2007). The literature considering high frequency returns 
of the European stock markets in the presence of US macroeconomic announcements is 
very much limited. Harju and Hussein (2011) examine four major European equity 
markets in the aspect of US announcements. They find that US fundamentals have an 
impact on Europeans investor’s behavior. Both equity returns and volatility are sensitive 
to American macro releases. Moreover, the indices (CAC40, DAX, DMI and FTSE100) 
show similar strong intraday seasonality pattern and react in the similar direction to the 
macroeconomic information. 

                                                 
† This work was financed from the Polish science budget resources in the years 2010-2012 as 

the research project N N111 346039. 



Barbara Będowska-Sójka 100 

 Routinely the announcements considered in the literature are from US, mainly due 
to the importance of American economy and the timing of US macroeconomic releases. 
These releases are characterized by specific features that make them useful: they are 
periodically publicized with timing of announcements being strictly predetermined to 
the date and the hour. Additionally the releases are preceded by the expectations which 
are obtained as a consensus between different financial analytics (Li, Engle, 1998). The 
crucial is the fact that the announcements are released at the time when European stock 
exchanges are open providing the area for the research of market reaction to the news. 
Contrary to this, majority of European macroeconomic announcements is released be-
fore the opening or after the closing of the session, only some of them being announced 
within the session time.  

 The impact of news releases may be observed on returns and in volatility with the 
latter more popular. What in fact is influencing the prices is not a news itself, but the 
surprise content of the news – the more surprising it is, the more volatility increases. In 
the world of continuously flowing information the only way to measure the reaction to 
announcements is to focus on intraday data. 

 Hanousek, Kocenda and Kutan (2008) estimate the impact of EU-wide macroeco-
nomic news from different countries on composite stock returns of three markets, the 
Czech, the Polish and the Hungarian. They conclude that the emerging markets react 
similarly to foreign news and this reaction is in line with the reaction of more advanced 
western European markets. However, in their paper all observations from the opening 
and the closing of the sessions are removed from the sample.  

 Our analysis contributes to the existing works in several ways. We study the reac-
tion to several macroeconomic announcements, domestic and neighbor-country as well 
as the American and compare which of them have a stronger influence on intraday vola-
tility. We focus on the Polish and the German stock markets and macroeconomic re-
leases from these two markets. From the previous works we know that on both markets 
the reaction to American macro releases is quite strong (Będowska-Sójka, 2010), but 
the size or the strength of the reaction to Polish or German releases is unknown. From 
the seminal paper of Wood et al. (1985) it is known that high-frequency series are char-
acterized by the strong periodical pattern in volatility and that the reasonable intraday 
dynamic analysis requires the estimation of intraday periodic component. Following 
works of Andersen and Bollerslev (1998) we use flexible Fourier form framework to 
model intraday series and find out what is the reaction to the announcements on both 
European markets. Two approaches are adopted: in the first the announcement effects 
are estimated within the flexible Fourier form regression, whereas in the second the 
regression is used only for the purpose of filtering from periodicity and the filtered 
series are introduced to FIGARCH models.  

 Considering the reaction to macro releases from different countries in the short  
5-minute interval our main finding is that volatility of indices react stronger to Ameri-
can announcements than to similar in category macro news from Germany or from 
Poland. For the domestic releases the reaction is weak in Germany and not significant in 
Poland. The intraday periodical pattern is quite similar on both markets and the reaction 
to American news is similar in size and direction.  

 The rest of the paper is as follows: in Section 1 we describe the data, in Section 2 
periodical pattern is considered. Section 3 is devoted to the intraday effect of macroeco-
nomic announcements. In Section 4 we present the methodology of volatility modeling 



The Impact of Macro News on Volatility of Stock Exchanges 101

and in Section 5 the results of AR-FIGARCH models are presented. Section 6 con-
cludes. 

1.  The Data 

 We use the data of the German and the Polish indices in conjunction with the data 
on expectations and realizations of scheduled macroeconomic announcements from the 
American, the German and the Polish markets.   

1.1. The Return Series  

 The sample consists of 5-minute intraday percentage logarithmic returns of the 
German DAX and the Polish WIG20 main stock exchange indices within the period 
5.01.2009-30.12.2010. We consider the percentage logarithmic returns, observed with 
frequency 1/Δ (with 1/Δ being an integer bigger than 0). As quite common when deal-
ing with intraday data the overnight return is excluded from the analysis. The length of 
the trading day is normalized to unity and therefore the time that elapses between two 
consecutive returns is equal (Boudt et al., 2010). 

 The DAX index is quoted from 9am to 5.30pm, and WIG20 from 9am to 4.10pm. 
After excluding overnight return we obtain 101 observations per day on the German 
market and 85 on the Polish. We use the data available at database www.stooq.pl. The 
estimation and charts are made in OxMetrics 6.0, in particular G@RCH 6 software and 
Ox codes (Laurent, Peters, 2010).  

 The sample mean of the five minute returns in both series is not distinguishable 
different from zero with standard deviation higher for WIG20 series. The distribution of 
both series is asymmetric and the kurtosis is very high. Hence, the distribution is not 
Gaussian. The significant autocorrelation in the series is observable only for low lags. 
This changes diametrically when we move to absolute returns which are characterized 
by very strong autocorrelation function – we will focus on this issue when describing 
periodical pattern in the intraday series.  

Table 1.  Descriptive statistics of DAX and WIG20 returns  

 DAX WIG20 
Mean -0.0002 -0.0005 
Standard deviation 0.1235 0.1569 
Minimum -1.8979 -1.9750 
Maximum 1.1926 1.9750 
Skewness -0.2765 -0.2319 
Excess kurtosis 7.0612 11.1270 
Observations 51510 42755 

1.2. The Macroeconomic Announcements  

 From the broad spectrum of macroeconomic announcements we choose only few 
that are very often used in the papers and are regularly released together with forecasts 
on the three markets: the American, the German and the Polish. We consider only an-
nouncement surprises, that means that the release is treated as a news only when it is 
different from the value of previously announced expectation. The macroeconomic data 
are from websites: ww.macronext.pl and www.deltastock.com.   



Barbara Będowska-Sójka 102 

 Some announcements are released regularly before the opening of the markets. 
These could not be included in the study (and are marked in the Table 2 with grey col-
or). Therefore we get only 5 types of announcements from Germany, 8 from Poland and 
10 from United States.  

Table 2.  The macroeconomic announcement and the timing on the three markets 

 Germany Poland United States 
Gross Domestic Product GDP 08:00 10:00 14:30 
Consumer Price Index CPI 08:00 14:00 14:30 
Producer Price Index PPI 08:00 14:00 14:30 
Unemployment Rate UN 09:55 10:00 14:30 
Industrial Production IP 12:00 14:00 15:15 
Retail Sales RS 08:00 10:00 14:30 
Economic Sentiment Indicator ESI* 11:00 10:00 16:00 
Durable Goods Order DGO** 12:00 na 14:30 
Trade Balance TB 08:00 14:00 14:30 
Purchasing Manager Index PMI 09:30 09:00 15:45 

Note:  * for Germany we take ZEW (Zentrum fűr Europaische Wirtschaftsforschung Economic Sentiment) that 
measures institutional investor sentiment. In Poland it is consumers’ confidence indicator published by GUS 
on 10:00 or 14:00. In case of US it is Conference Board Consumer Confidence; ** in Germany the Factory 
Orders are taken  into account. There is no such announcement that could stand for proxy in Poland. For the 
United States it is Durable Goods Order ex Transportation.   

As the Daylight Saving Time is changing in different time in Europe and America, we 
control for that when modeling the reaction to American announcements.  

2.  The Intraday Pattern in Volatility 

 The intraday periodical pattern is very well described in number of papers (see e.g. 
Dacorogna et al., 2001, Rossi, Fantanzzini, 2008) and usually described as U-shaped or 
inverted J curve of averages of absolute returns. What is characteristic for the shapes of 
averages of absolute returns for European stock markets is a sharp increase in volatility 
at the time of American macroeconomic announcements at 14:30 and 16:00 (Będowska-
Sójka, 2010, Harju, Hussein, 2011).  

 
Figure 1. Average absolute returns for DAX and WIG20  



The Impact of Macro News on Volatility of Stock Exchanges 103

Both the U-shape of autocorrelation of absolute returns (Figure 1) and the inverted 
J shape in averages of absolute returns (Figure 2) are visible for DAX and WIG20.  
In case of averages of absolute returns they reach higher values at the opening of the 
markets and then decrease in the lunch time. Finally they go up at the closing.  
This pattern is very similar in both series – the only difference is that in case of WIG20 
at the end of the session volatility goes up higher.  

 The repeating pattern of autocorrelation function is observed every 101 lags for 
DAX and 85 lags for WIG20 (Figure 2). This structure of the ACF and shape of intra-
day volatility demand the proper treatment of periodicity. Additionally numerous stud-
ies have found the day-of-the-week effects, that should be accounted for (Bauwens  
et al., 2000). 

 
Figure 2.  Sample autocorrelation function (ACF) of the DAX and WIG20 series of 

absolute returns 

 In the paper the periodicity removal is achieved with Gallants’ (1981) flexible Fou-
rier form regression adopted by Andersen and Bollerslev (1998).   

We consider the 5-minute returns, where n refers to the number of intraday returns per 
day (n = 1,….N), and t is the number of trading days in the sample  
(t = 1, …, T):  

, ,
, .( ) ,t t n t n

t n t n

s Z
r E r

N


   (1) 

where σt,n is daily volatility factor, st,n is periodicity factor and Zt,n is i.i.d. mean zero 
unit variance innovation term. The daily volatility component is measured as realized 
volatility, RV, which means it is a sum of squares of intraday returns, whereas periodici-
ty component is estimated with flexible Fourier form regression:  

  2 2 2
, , , , ,2 log ( ) log log log log ,t n t n t n t t n t nx r E r N s Z       (2) 



Barbara Będowska-Sójka 104 

2 2
, , , , , ,log (log ) ,t n t n t n t n t n t nx f Z E Z f u      (3) 

2

, 0 1 2
11 2

2 2
cos sin ,

P

t n p p
p

n n p p
f n

N N N N

     


      
 

   (4) 

where pp  ,,,, 210  are estimated parameters and 1 ( 1) / 2,N N   

2 ( 1)( 2) / 6N N N    are normalized constants (Andersen, Bollerslev, 1998). After 

some experimentation, we found that the order of expansion P=8 is sufficient to capture 
the basic shape of the series.  

 The estimator of periodic component on day t and interval n: 

.

)2/ˆexp(

)2/ˆexp(
ˆ

1 1
,

,
,

 
 


T

t

N

n
nt

nt
nt

f

fT
s  (5) 

 Finally we obtain periodically filtered series by dividing original series by the esti-
mated seasonal pattern: 

.~

,

,
,

nt

nt
nt s

r
r 

 (6) 

 We show both the average absolute returns with periodical pattern of volatility in 
Figure 3. After periodicity filtering the intraday pattern for both series is removed, while 
the effects of macroeconomic announcements remain in the series.   

 

Figure 3. Average absolute returns and absolute filtered returns for DAX series 

 

 



The Impact of Macro News on Volatility of Stock Exchanges 105

 

Figure 4. Average absolute returns and absolute filtered returns for WIG20 series 

 The descriptive statistics of series before and after filtering are presented in Table 3. 
The filtering of the data with FFF regression does not substantially change the descrip-
tive statistics of the series.  

Table 3. Descriptive statistics of DAX and WIG20 returns before and after periodicity 
removal  

 DAX DAX after FFF WIG20 WIG20 after FFF 
Mean -0.0002 -0.0001 -0.0005 -0.0005 
Standard deviation 0.1235 0.1224 0.1569 0.1506 
Minimum -1.8979 -1.9688 -1.9750 -1.4531 
Maximum 1.1926 1.6331 1.9750 1.5642 
Skewness -0.2765 -0.2176 -0.2319 -0.2319 
Excess kurtosis 7.0612 7.7792 11.1270 11.1270 
Observations 51510  42755  

 After filtering from periodicity we expect that the strong U-shaped autocorrelation 
observed previously in absolute returns is removed from the data. In fact, after filtering 
the periodical pattern is not observed in the series of absolute returns, but in both cases 
they are still characterized by long memory (Figure 5). This slow decay in autocorrela-
tion function is typical for long memory process. Therefore we will model volatility in 
Section 5 with an appropriate GARCH model that allows for such a long memory. 

3. The Intraday Effects of Macroeconomic Announcements  
on Volatility – FFF Regression 

 Our approach is aimed to study the influence of macroeconomic announcements of 
the same type from three markets on the volatility of two indices, the German and the 
Polish. The regression specification is than: 



Barbara Będowska-Sójka 106 

,
2

sin
2

cos
12

2

2
1

10, ii

P

p
ppnt X

N

p
n

N

p

N

n

N

n
f  






  



(7) 

where λi is the estimated coefficient and Xi is the time-stamped to the nearest 5-minute 
return announcements dummy variable. In some works it is suggested to filter series 
using only the control days – which means days without events under study (Conrad 
and Lamla 2007, Dominguez 2003). We rather agree with Boudt et al. (2010) that “con-
ditioning on the days without any news, would lead to a too small sample”. What is 
actually important is the difference between releases and the expected values – it is not 
the event itself, but the surprise that is causing the price change. 

 

Figure 5. Sample autocorrelation function of the series of absolute filtered returns 

 In our approach the macroeconomic announcements take the value 1 if they were 
different from previously released forecasts and 0 otherwise. Additionally the dummies 
representing day-of-the-week effect are also included in the regression. The estimates of 
FFF regression are shown in Table 4 (DAX) and 5 (WIG20). 

 We consider the reaction in first 5 minutes after macro news releasing. For both 
series, DAX and WIG20, American announcements do increase volatility of returns in 
such a short period. In case of DAX the domestic announcements increase volatility  
(IP, ES, DGO), whereas in Poland domestic releases play no role in very short time 
interval. The neighbor-country announcements have no impact on volatility within first 
5-minutes. The reaction to announcements on 14:35 which are clearly visible in average 
absolute returns (Figure 3) are now confirmed by the estimated parameters. The most 
powerful announcement in a short run is the American unemployment rate.  

 The estimated coefficients for day-of-the-week effect are omitted, however all of 
them are statistically significant. The considered announcements together with day-of-



The Impact of Macro News on Volatility of Stock Exchanges 107

the-week dummies explain only 4% and 5% of intraday volatility in case of DAX and 
WIG20 respectively.  

Table 4. Parameters estimated in FFF regressions for DAX 

 United States Germany Poland 

GDP 3.1392 (0.5588)   1.2857 (0.8425) 
CPI 1.7249 (0.5462)   0.7035 (0.5504) 
PPI 1.4704 (0.5020)   0.1714 (1.6936) 
UN 4.104 (0.5446) -0.3629 (0.5938) 0.3127 (1.6935) 
IP 1.3241 (0.4947) 1.4878 (0.4957) -0.2941 (0.9892) 
RS 2.5896 (0.4932)   0.1797 (0.6346) 
ES 2.3163 (0.4952) 1.7297 (0.4858) 0.8400 (0.6461) 
DGO 2.1283 (0.4946) 0.9873 (0.4853)   
TB 1.0291 (0.4952)   0.3455 (0.5006) 
PMI -0.1474 (0.4949) 0.4309 (0.4857)   

Observations 51510      
R2 0.044905  0.0418  0.0415  
Adj. R2 0.044312  0.0413  0.0409  

Note: The estimated parameters together with standard errors (in italics) are in the upper part. The bolded 
parameters are statistically significant at α=0.05. 

5.  Modeling with FIGARCH Models  

 For the purpose of modeling with FIGARCH models we filter the series again with 
FFF regression but this time including only the day-of-the-week dummies. The series 
filtered from periodicity with only day-of-the week dummies are introduced into 
FIGARCH models with dummy variables in the conditional variance equations. For 
these models the sample is restricted to 2009 only. 

 The conditional mean equations are modeled with the AR(2) process:  

 1 1 2 2 .t t t tr c r c r a        (8) 

 Due to the long memory in series of absolute filtered returns, the innovations are 
modeled with FIGARCH (p, d, q) process with specification given by BBM’s method 
(1999): 

2 2 2(1 ) ( ) ( )( ),d
t t tL L a L a        (9) 

where lag polonymials 

  ,1)(
1




q

i

i
i LL   ,1)(

1




p

i

i
i LLB   

and 10  d being the fractional differencing parameter. Bollerslev and Mikkelsen 
(1996) define the sufficient condition of non-negative conditional variance in (9) as 

31 f with jdjf j /)1(  . An extensive discussion of the properties of 

FIGARCH model can be found in Conrad and Haag (2006) where the necessary and 
sufficient conditions have been described in details. 



Barbara Będowska-Sójka 108 

Table 5. The parameters estimated in FFF regressions for WIG20 

 United States Germany Poland 

GDP 2.7633 (0.5358)   0.4863 (0.8511) 
CPI 0.5254 (0.5520)   0.3784 (0.5558) 
PPI 1.4518 (0.5076)   -0.2171 (1.7103) 
UN 4.0206 (0.5504) 0.8649 (0.6000) 1.2194 (1.7103) 
IP 1.792 (0.4999) 0.2251 (0.5011) -0.6301 (0.9989) 
RS 2.2259 (0.5093)   0.7275 (0.6411) 
ES 1.6942 (0.5014) 0.2867 (0.4912) -0.1671 (0.6526) 
DGO 2.2502 (0.4999)     
TB 1.4016 (0.5004)   -0.4574 (0.4951) 
PMI 1.239 (0.5005) -0.1594 (0.5013)   

Observations 42925  42925  42925  
R2 0.0572  0.0535  0.0535  
Adj. R2 0.0564  0.0529  0.0529  

Note: The estimated parameters together with standard errors (in italics) are in the upper part. The bolded 
parameters are statistically significant at α=0.05. 

 We introduce dummy variables into conditional variance equation: 

.))()1()(()( 2
3

1
, t

d

i
iti aLLLXL  



  (10) 

 These dummy variables are defined in the way that they take the value of 1 at the 
time of macroeconomic surprise release on the particular market (American, German or 
Polish) and 0 otherwise.  

Table 6. The estimates of AR(2)-FIGARCH(1, d, 1). 

 DAX WIG20 
c1 0.0052 (0.0064) 0.0011 (0.0112) 
c2 -0.0201 (0.0082) -0.0312 (0.0121) 
ω 0.0014 (0.0005) 0.0034 (0.0010) 
φ  0.0997 (0.1178) 0.0791 (0.0999) 
β 0.2936 (0.1375) 0.2599 (0.1144) 
d 0.2681 (0.0211) 0.2425 (0.0172) 
ω1 (Germany) 0.0142 (0.0051) 0.0108 (0.0074) 
ω2 (Poland) 0.0032 (0.0039) 0.0084 (0.0080) 
ω3 (United States) 0.0936 (0.0293) 0.1540 (0.0424) 

Note: The estimated parameters together with standard errors (in italics). The bolded parameters are statisti-
cally significant at α=0.05. 

6. Results of AR-FIGARCH Estimations 

 We estimate AR(2)-FIGARCH(1,d,1) model including aggregated dummy varia-
bles standing for announcements. As can be seen in Table 6 the autoregressive parame-
ters c2 in conditional mean equation are statistically significant. In conditional variance 
equation ω, β and d are statistically significant and the values are reasonable according 
to suggestions in the literature (Bollerslev and Mikkelsen, 1996), but φ estimates are not 



The Impact of Macro News on Volatility of Stock Exchanges 109

statistically significant. It suggest that squares of previous shocks do not impact volatili-
ty. The estimated persistence parameters β are highly significant and around the value of 
0.25. When we consider the aggregate dummy variables standing for announcements, it 
is visible that in Germany domestic announcements do increase volatility, but the pa-
rameter standing at the variable for American news is higher and that confirms our 
earlier findings. In case of Polish market only American announcements increase vola-
tility and this impact is stronger than in Germany.  

Conclusions 

 The periodical pattern in high frequency index returns on two European stock ex-
change markets, the German and the Polish, is very strong and might be successfully 
removed with flexible Fourier form (FFF). Our results of FFF regression indicate that 
US announcements have definitely stronger impact on volatility of both European indi-
ces, DAX and WIG20 than domestic and neighbor country macroeconomic news.  
It might be partly due to the fact that the number of German news released within the 
session is very limited. In Poland the only significant macro releases are those from 
America. In both countries, Germany and Poland, neighbor country news releases gen-
erally have no effect on volatility. The reaction to American announcements is immedi-
ate and recognized in first five minutes after announcements. If there is any cross-
reaction to the announcements from the neighbor countries, it is not observable in such 
a short time. 

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The Journal of Finance 40, 723–739. 

Wpływ ogłoszeń makroekonomicznych na zmienność rynków akcji 

Z a r y s  t r e ś c i. Celem artykułu jest zbadanie wpływu ogłoszeń makroekonomicznych z trzech 
krajów, Stanów Zjednoczonych, Niemiec i Polski na zmienność śróddziennych indeksów DAX 
i WIG20. Dla obu indeksów opisano wzorzec zmienności i zastosowano elastyczną postać Fourie-
ra w modelowaniu szeregów. W stosunkowo krótkim przedziale czasowym pięciu minut w obu 
indeksach zaobserwowano silną reakcję na ogłoszenia ze Stanów, a w niemieckim indeksie DAX 
słabszą reakcję na ogłoszenia niemieckie. Dla polskiego WIG20 nie wychwycono w tak krótkim 
interwale czasowym reakcji na polskie ogłoszenia. Dodatkowo oba indeksy nie reagują na ogło-
szenia z rynku kraju sąsiadującego.    

S ł o w a  k l u c z o w e: dane śróddzienne, ogłoszenia makroekonomiczne, elastyczna forma 
Fouriera, cykliczność śróddzienna, modelowanie zmienności.  


