Transcript Forecasting with Bayesian Vector Autoregression

Academy of Economic Studies
Doctoral School of Finance and Banking - DOFIN
The Impact of Macroeconomic
News on Exchange Rate
Volatility
MSc Students: Stan Ana-Maria
Supervisor: Moisă Altăr, PhD
Bucharest
2010
Contents
1.
2.
3.
4.
5.
6.
7.
Motivation and objectives
Literature review
Methodology
Data
4.1 Data description - Eur/Ron exchange rate
4.2 The state of the economy in Romania and Euro Zone
4.3 Macroeconomic news
Empirical estimation
Results
6.1 The impact of news immediate after announcements
6.2 The impact of news two hours after announcements
Conclusions
References
1. Motivation and objectives (1)
1.1 Motivation
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The dynamics of exchange rates is something market participants, economists, and even
policymakers are interested in better understanding.
In order to understand what triggers the price change I have considered analyzing the impact of
scheduled macroeconomic news.
It had been shown that in order to find significant reactions in the foreign exchange market to the
macroeconomic variables, one needs to measure the precise impact of macro surprises at the
intra-day level.
Andersen, Bollerslev, Diebold and Vega (2003), find that when a narrow window of time is used,
they are able to find a strong relationship between certain macro-surprises and exchange rate
returns.
According to the efficient market hypothesis, all currently available information should be included
in the price of an asset. After the arrival of new information, rational market agents update their
beliefs on the value of an asset and the price moves to its new equilibrium. This requires,
however, that the new information really surprises the markets, because the present price also
contains expectations concerning future developments. (Fama 1970)
News that arrives during periods of high uncertainty may have different effects on the exchange
rate, than news that arrives in calmer periods*: the economic situation was different in the data
sets analyzed, and also different results were obtained.
Macroeconomic announcements are only one piece of information hitting financial markets.
* Dominguez, K and Panthaki, F (2005), “What defines ‘News’ in foreign exchange market?”, NBER
1. Motivation and objectives (2)
1.2 Objectives
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The purpose of this paper is to examine the micro characteristics of the
exchange rates: the intradaily periodicity of volatility and the impact of new
information on volatility.
First, this supposes filtering intraday seasonality of volatility which is caused
by differences in trading times in the global foreign exchange market.
Secondly, the reactions in the short run of the Eur/Ron exchange rate to the
surprise component of the macroeconomic announcements is examined,
using high-frequency data collected from the real trading platform.
The immediate impact after the news announcement was tested
The impact of news has been reported to last from one to two hours and the
decay structure of the volatility response pattern was estimated.
The impact of scheduled European and Romanian macroeconomic news on
the volatility of Eur/Ron 5-minute returns was tested by using the Flexible
Fourier Form regression method.
2. Literature Review
This paper takes the results of the following articles as a benchmark:
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Andersen, G., T., Bollerslev, T., (1997) "Intraday periodicity and volatility persistence in financial
markets“, Journal of Empirical Finance .

Andersen and Bollerslev (1997) developed the Flexible Fourier Form regression method to
model the periodical intraday structure of volatility, using different frequencies of sine and
cosine functions.
Andersen, G., T., Bollerslev, T., (1998) "Deutsche Mark-Dollar Volatility: Intraday Activity Patterns,
Macroeconomic Announcements, and Longer Run Dependencies" The Journal of Finance Vol.
LIII, No 1

The results indicate that the news causes a jump in the level of the exchange rate, and
increases the volatility of returns from an hour to two hours after the arrival of information.
Dominiguez, K., Panthaki, F., (2005), “What defines ‘News’ in foreign exchange market?”,
National Bureau of Economic Research, Working Paper 11769

The impact of macroeconomic announcements is considered to be very limited, because
announcements are retrospective, and are often revised substantially.

Whether news is scheduled or non-scheduled its influence on exchange rates may be related
to the state of the market at the time of the news arrival.
Laakkonen, H., (2007) “Exchange rate volatility, macro announcements and the choice of intraday
seasonality filtering method”, Bank of Finland Research, Discussion Papers 23

In this article three filtering methods were tested, and FFF model was found as the best for
seasonality filtering. The FFF method performs the best among a number of commonly
employed filtering methods because it produces the smallest bias in the estimated news
coefficients compared to other filtering methods.
3. Methodology – The model
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Of the alternative filtering methods proposed in the literature, we choose the Flexible Fourier Form
(FFF) model of Andersen and Bollerslev (1997) that uses different frequencies of sine and cosine
functions to capture the periodicity.
Filtering increases volatility during the low volatility periods of the day and decreases volatility
during the high volatility periods. The intraday seasonality of the volatility is divided away, but
other than that the returns remain the same.
 t ,n
R

E
(
R
)

s z
t ,n
1/2 t , n t , n
The FFF method is based on the following decomposition: t ,n
(1)
N
Rt , n represents the return series computed as the differences of logarithmic prices Rt ,n  ln(Pt / Pt 1)
E Rt ,n is the expected return
N represents the number of intervals in one day (288 intervals of 5 minute in 24 hours market.
t represents the day, and n the 5-minute interval
 
The idea behind the method is that the volatility of the return process Rt , n is measured by the
demeaned absolute returns, and it can be decomposed into the daily volatility component  t, ,the
intraday volatility component st , n and the innovation zt ,n i.i.d. (with mean zero and unit
variance).
After replacing the expected return by mean return R and eliminating the daily component (by
ˆ t is GARCH (1,1), model estimate for daily
dividing the volatility measure with ˆ t , n where 
volatility), squaring and taking logs N 1/ 2 , equation (1) becomes:
2 ln
Rt ,n  R
ˆ t / N 1/2
 2 ln( st ,n )  2 ln( zt ,n )
(2)
3. Methodology – The model, cont.
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The first is the component for the intraday volatility which can be modeled using the trigonometric
functions and the other component is the error term, which includes the extra volatility of the markets,
for example the volatility caused by new information.
The FFF regresion method is:
P

n
n2 D
 p  2  
 p  2   
ft ,n  c  1    2    k  I k (t , n)     c, p  cos 
n    s , p  sin 
n     t ,n
N1
N2 k 1
 N

 N

p 1 
Rt ,n  R
, c is the constant, N1  ( N  1) / 2 and N2  ( N  1)( N  2) / 6 are

Where f t ,n  2 ln

normalizing constants, sine and cosine variables are for the capturing of intraday periodicity, I k
are the indicator variables used for inserting macro news into the model.
When fˆt , n has been estimated, sˆt , n - the estimate for intraday volatility is then obtained with eq. 4.
fˆt ,n
2 (4)
t ,n

sˆ
ˆ t / N
1/ 2
(3)
e
This estimate sˆt ,n is normalized so that the mean of the normalized seasonality estimate equals one:

,where T is the total number of observations in the data.
~
ˆt , n
T

s

(5)
We get than the filtered return by dividing the original
st , n  [T / N ] N
return by the intradaily volatility component:
ˆ
s
t ,n

R  R / s (6)

 
t 1
n 1
t ,n
t ,n
t ,n
4. Data
4.1 Data description - Eur/Ron exchange rate
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The original data consists of 5-minute frequency spot transaction price data of the
EUR/RON exchange rate and is obtained from Reuters 3000 XTRA.
The prices are formed by taking the average between the bid and ask quotes, and
the returns are computed as the differences of logarithmic prices. Rt  ln( Pt / Pt 1 )
There were considered two data sets:
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The first analyzed period corresponds to 5 January 2009 -29 May 2009. There
were 30 240 observations altogether and 105 days in 5-minute data.

The second period analyzed corresponds to 17 December 2009 – 19 May 2010.
There were 31 680 observations altogether and 110 days in 5-minute data.
There are 288 5-minute intervals observations in 24 hours market.
If there were no transactions during the 5-minute period, the observations were
replaced by the weighted average of the previous and following observations. The
observations were missing usually around midnight (GMT), when the volume of the
foreign exchange is at its lowest. When a longer data period was missing during the
night, the missing observations were not replaced and the returns were set at zero.
The global foreign exchange works 24 hours a day, but at weekends the markets are
closed. Due to the lack of observations, the weekends were removed from the data
from Friday midnight (GMT) to Sunday midnight.
Absolute returns were used as a measure of volatility.
4.2 The state of the economy in Romania and Euro Zone (I)
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The economic environment in Romania in the beginning of the year 2009 started with fears against
recession, since the global financial crisis crashed into Central and Eastern Europe.
In January 2009, the domestic currency depreciated against the euro (although there were rumors
regarding interventions in FX market), and the Eur/Ron exchange rate was trading at 4.25, after
reaching a historical high level above 4.30.
On the 4th February 2009 Romania's central bank cut its key rate by 25 basis points to 10 percent,
easing the cost of money for the first time in 19 months as a shortage of cash threatened to strangle
the once buoyant economy.
On 25 March, Romania agreed for a 2-year EUR 20 bln financing package with international
institutions (IMF, European Commission, World Bank, EBRD).
On the 15th of May Romanian economy gets into recession, contracting sharply by 6.4 percent in
the first quarter of 2009, compared with a 2.9 percent growth in the fourth quarter of last year.
In the same time, the Euro Zone’s economy was in recession registering a level of GDP of -1.2
percentage in Q4 2008.
4.4000
4.3000
4.2000
4.1000
4.0000
3.9000
3.8000
Figure 1 Eur/Ron exchange rate 5.01.2009 – 29.05.2009
4.2 The state of the economy in Romania and Euro Zone (II)
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In the second data set , Eur/Ron exchange rate appreciated rapidly at the beginning of January
after a new government was approved by Parliament on 23 December.
The domestic currency remained almost stable in the period analyzed , due to the support from the
central bank via indirect interventions in the FX market and the external financing package that
Romania agreed with international institutions.
In February it was released the estimated GDP which shrank by 7.2 percent in 2009, worse than
market expectations for a 7.0 percent fall, and also contracting on the quarter in October-December
to -1.5 percent.
Romania benefitted of an improved market sentiment due to unlocking the financial aid from IMF,
that had been frozen in the end on 2009 because of a prolonged political crisis.
In April the leu started to depreciate because of the worries related to debt crisis in some Euro area
member countries (especially Greece).
4.3000
4.2500
4.2000
4.1500
4.1000
4.0500
4.0000
3.9500
Figure 2 Eur/Ron exchange rate 17.12.2009 – 19.05.2010
4.3 Macroeconomic news
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The announcements were collected from Reuters 3000 XTRA from the quotes Romania (referring
to the Romanian macro-news), and from the quote ECB (referring to the Euro Zone macro-news).
These announcements are macroeconomic indicators of which announcement dates and times
are known beforehand. Reuters provides also a survey of market participants’ expectations of
future macro figures and the expectation of the market is taken as the median of participants’
forecasts. The forecast is available only for some of the announcements.
According to the definition of news, the news should be something surprising. The difference of
the announced figure and the market forecast has been considered as the actual new information
that causes volatility.
There were altogether 230 announcements during the first estimation period. The Reuters
forecast was available for 51 announcements.
The impact of news was tested separately for Romania, and Euro Zone.
The number of macroeconomic announcements is presented below:
Observations
05.01.2009–29.05.2009
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17.12.2009–19.05.2010
Romania
89
49
Euro Zone
140
73
Announcements with forecast
50
52
Table 1. Number of macroeconomic announcements
The forecast was not available for all the announcements. Also when the forecast equaled the
macro announcements, the surprise was zero and therefore not taken into consideration.
The surprise was computed as percentage from the macro data released.
5. Empirical estimation
5.1 Intraday dynamics of volatility
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0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
0.4000
0.3000
0.2000
0.1000
0.0000
Figure 3 Autocorrelation coefficients of 288
Five-minute lag
-0.1000
1
73
145
217
289
361
433
505
577
649
721
793
865
937
1009
1081
1153
1225
1297
1369
1441
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Various kinds of ARCH models have usually been considered the best for modeling the
conditional heteroskedasticity of financial returns. However, when modeling the intraday returns,
the ARCH models do not seem to work at all. This is due to the systematic periodical structure of
volatility during the course of a day that ARCH models fail to consider.
The level of volatility during a day depends on the trading times in different markets:

the Far East markets open around 23:00 GMT and cause a small increase in volatility;
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the European markets open around 7:00 GMT and volatility increases more;
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and the US markets open around 14:00 GMT after which volatility reaches its highest level.
When this pattern is repeated every day, it causes a U-shape pattern in the autocorrelation
of volatility. Figure 3 presents the autocorrelation coefficients of 288 five minute lags, i.e. the
autocorrelogram for one day. The U-shape pattern can be clearly seen in the graph. If we draw
the correlogram for 1500 lags, we get the autocorrelogram for five days (Figure 4).
1
18
35
52
69
86
103
120
137
154
171
188
205
222
239
256
273

-0.2000
Figure 4 Autocorrelation coefficients for 1500 lags
5.2 Flexible Fourier Form regression method (I)
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In the FFF model (equation 3) Ik are the indicator variables, which were introduced in the model to
determine the impact of news on Eur/Ron exchange rate volatility( when filtering, Ik was set to
zero), t represents the day, and n the 5-minute interval in the data.
In order to eliminate daily volatility, Garch (1,1) had been determined. The sum of the GARCH
coefficients is very close to one, indicating that volatility shocks are quite persistent.
According to Akaike and Schwartz information criteria, p = 10 is the best choice for both Eur/Ron
exchange rate data sets. (p = 1…10 was tested).
The model was estimated once for the whole data set. If filtering is done in subsets of one week
the following results were obtained:
Key statistical
Original
Filtered returns-week
figures
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As previously mentioned, filtering increases
volatility during periods of low- volatility, and
decreases volatility in high-volatility periods,
other than that the returns should remain
the same. Given the results determined, and
mainly because the kurtosis jumps from
21.03 to 1,961,53 this possibility was not
taken into consideration in the analysis of
the determination of volatility.
Mean
returns
subsets
-1.41E-07
-2.95106E-05
2.02E-06
7.10935E-05
6.38E-11
-2.95106E-05
0.00036
0.01265
Kurtosis
21.03
1,961.53
Skewness
-0.23
-13.703
Minimum
-0.007
-1.019
Maximum
0.007
0.722
-0.0045
-0.935
31,679
31,679
Standard Error
Mean absolute
return
Standard
Deviation
Sum
Count
Table 2. Key statistical figures – filtering in subsets
5.2 Flexible Fourier Form regression method (II)
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As expected, filtering does not affect dramatically the mean or standard deviation of the returns.
On the other hand, filtering seems to have an effect on the third and fourth moments. The
distribution of financial return series is usually very leptokurtic compared to the normal distribution,
which indicates the overabundance of great returns compared to the normal distribution, applying
for both data set analyzed. The distribution of the Eur/Ron returns is positively skewed for the first
date set, which suggests that there are more great positive than negative returns. The distribution
of the second data set is negatively skewed, suggesting there are more negative returns. Although
the distribution of the returns seems to be closer to the normal distribution after filtering, because
of the excess kurtosis, neither the original nor filtered returns are normally distributed.
Key statistical figures
Returns
Filtered returns
Mean
1.25E-06
-8.49E-07
Standard Error
1.69E-06
1.80E-06
Mean absolute return
0.00013
0.00013
Standard Deviation
0.00029
0.00031
44.46
82.76
0.38
1.25
Minimum
-0.0064
Maximum
Kurtosis
Skewness
Sum
Count
Key statistical figures
Filtered returns
-1.41E-07
-9.29E-06
Standard Error
2.02E-06
3.12E-05
Mean absolute return
6.38E-11
-9.29E-06
0.00036
0.00556
Kurtosis
21.03
129.84
Skewness
-0.23
-3.34
-0.0080
Minimum
-0.007
-0.180
0.0052
0.0071
Maximum
0.007
0.120
0.04
-0.03
-0.0045
-0.2942
30,239
30,239
31,679
31,679
Table 3. Key statistical figures of raw and filtered
five minute logarithmic return series of Eur/Ron
exchange rate from the period 05.01.2009–
29.05.2009.
Mean
Returns
Standard Deviation
Sum
Count
Table 4. Key statistical figures of raw and filtered
five minute logarithmic return series of Eur/Ron
exchange rate from the period 17.12.2009–
19.05.2010
5.2 Flexible Fourier Form regression method (III)

Following is presented the correlogram of the raw and filtered absolute returns:
a) Five day correlogram of the filtered five minute absolute Eur/Ron returns compared to original
absolute returns for the second data set 17.12.2009–19.05.2010.
b) Five day correlogram of the filtered five minute absolute Eur/Ron returns compared to original
absolute returns for the first data set 05.01.2009–29.05.2009.
Figure 5. Autocorrelation coefficients of the original and filtered returns
6. Results
6.1 The impact of news immediate after announcements

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
To test the impact that the macro figure has immediately after the announcement, we
use the model:
2
Rt ,n  R
(7) ,
2 ln
 c   k I k (t , n)   t ,n
1/ 2
ˆ t / N
k 1
where R t ,n denotes the filtered returns, I k (t , n) the news variable, c is the constant term
and  t ,n is the error term of the model, while k=Rom, EZ
The news indicator Ik(t,n) was used like dummy variable, with the difference that when
the news was announced, the variable would take the value of the surprise, not value
one, and zero otherwise.
After estimating the equation (7) we obtain the value of k, the coefficient of news. For
the first data set (05.01.2009–29.05.2009) we obtain that both Romanian and Euro Zone
macroeconomic news have an impact on the Eur/Ron exchange rate volatility, Romanian
more than Euro Zone news, as follows: Romania is 1.023164 and EZ is 0.505239.
For the second data set we obtained that only Romanian news have an impact on the
Eur/Ron exchange rate volatility, while the news coefficient for the Euro Zone was
negative: Romania is 1.076604 and EZ is -0.102663. The coefficient of -0.10 can be
interpreted as indicating that news reports led to a ten basis points appreciation of the
ron against the euro.
6.1 The impact of news immediate after announcements (Cont.)

One reason for this result is that the economic environment changed dramatically in
2010 as compared to the previous year. In 2009 the macroeconomic indicators
announced for Romania showed the economy was weakening, but only on 15th of
May, almost the end of the first data set, was released the Gross Domestic Product
for Q1 2009 showing the economy contracted by 6.4 percent on the year. In the
same time, the Euro Zone economy which entered in recession from Q2-2008, was
continuing to fall, shrinking by 1.5 percent in Q4-2008.

Another aspect to consider is the fact that the most significant news from Euro Zone
comes from monetary policy. In 2009 The European Central Bank (ECB) had
constantly reduced the benchmark interest from 2.5 percent to 1.25 percent in
January-May 2009, to diminish the contraction in domestic demand and to impose
tighter financing conditions. In the second data set, between December 2009 and
May 2010, ECB kept the benchmark interest unchanged to 1 percent, the attention
moving to the high deficits registered by the euro-members.
6.2 The impact of news two hours after announcements (I)
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The impact of news has been reported to last from one to two hours (Andersen and
Bollerslev 1998). The simple possibility would be using dummy variables, meaning
choosing 1 for the first 25 intervals, but Helina Laakonen (2004), and earlier Bollersev,
used a 3 order polynomial which surprises better the descending structure of the
average of the effect, and that gets to be 0 after 2 hours.
Therefore we first estimate the average news impact pattern  (i) by computing the
average absolute return at each five-minute interval following the news announcement
minus the average absolute return over the entire sample period. All the
announcements are pooled in computing this average.
We than estimate the decay structure of the volatility response pattern of news by fitting
a third order polynomial to the average news impact pattern.
(8),
 (i)  b (1 (i / 25)3 )  b (1 (i / 25)2 )i  b (1 (i / 25))i2
1

3
The impact of news on volatility M k (i) , can than be computed for each 25 intervals as:
    (i) 
M k (i)  exp  k
 1
2



2
 
(9)
Now when the macro figure is announced, the news variable Ik t, n takes the value of
 (i) for the first 25 intervals after the announcement and zero otherwise.
6.2 The impact of news two hours after announcements (II)
Romania for the first data set is 20.25673 and EZ is 30.19419.

We obtain the following results:

For the second data set we obtain
Romania
0.0002
is 20725.18 and
EZ is 1.138040.
0.0004
0.0003
0.0001
0.0003
0.0001
0.0002
0.0002
0.0000
1
3
5
7
9 11 13 15 17 19 21 23 25
0.0001
0.0001
-0.0001
0.0000
1
-0.0001
mean absolute return


polynomial fitted values
3
5
7
9
mean absolute return
11 13 15 17 19 21 23 25
polynomial fitted values
a) Decay structure of volatility
b) Decay structure of volatility
response pattern after Romanian news
response pattern after Euro Zone news
Figure 6. Decay structure of volatility response pattern after news for the first data set
(05.01.2009–29.05.2009)
The decay structure of volatility response pattern for Romanian news shows the average absolute
return had a big jump in the third interval, and declined after the 12th interval, showing the impact
of Romanian news is lasting for one hour.
For the Euro Zone news, the impact of the news on volatility is the strongest in the first 5-minute
period after the announcement, and declines two hours after the announcement.
6.2 The impact of news two hours after announcements (III)
0.0005
0.0003
0.0004
0.0002
0.0003
0.0002
0.0002
0.0001
0.0001
0.0001
0.0000
1
-0.0001
3
5
7
mean absolute return
9 11 13 15 17 19 21 23 25
0.0000
mean absolute return
1
3
5
7
9 11 13 15 17 19 21 23 25
-0.0002


a) Decay structure of volatility response
b) Decay structure of volatility response
pattern after Romanian news
pattern after Euro Zone news
Figure 7. Decay structure of volatility response pattern after news for the second data set
(17.12.2009–19.05.2010)
The decay structure of volatility response pattern for Romanian news in the second data set
shows the average absolute return is the highest immediately after the announcement, and
declines two hours after the announcement.
The macro announcements from the Euro Zone have no impact on the Eur/Ron exchange rate
volatility.
Romania-2009
Euro zone-2009
Romania-2010
Euro zone-2010
k
Mk(1)
k
Mk(1)
k
Mk(1)
k
Returns filtered with
FFF regression method 20.25673 0.00059 30.19419 0.00479 20725.18 117.785 1.13804
Mk(1)
0.000010
Table 5 Impact of macroeconomic news on Eur/Ron volatility, following two hours after
announcement
6.2 The impact of news two hours after announcements (IV)



Data from table 5 presents the results obtained when the impact of news for two
hours is tested.
For the first data set we obtained that EZ was greater than Romania , showing the
Euro Zone news have a longer impact on the exchange rate volatility. Also the
indicator Mk(1) shows the impact in the first five-minute after the announcement is
higher when euro-announcements were released, rather than Romanian news.
The results obtained for the second data set show that Romania was much higher
than the result obtained in the first data set, and also higher than the news from EuroZone from the second data set. The indicator Mk(1) shows the impact in the first fiveminute after the announcement is higher when Romanian announcements were
released, while the impact from the Euro Zone is not significant.
7. Conclusions





The strong intradaily periodicity in the autocorrelation which is caused by differences in trading
times in the global foreign exchange markets, was found.
For the first data set it was obtained that macroeconomic news from Romania have a greater
impact on the returns volatility than news from Euro Zone, when the immediate impact after the
announcement was tested. When the decay structure of volatility was estimated, it was found that
the impact of news from Romania lasts for one hour, while the impact of news from Euro Zone
lasts for two hours.
When analyzing the second data set different results were obtained. In this case the immediate
impact after the news announcement was higher after the announcement of Romanian news, and
very low after the Euro Zone announcements. After estimating the decay structure for two hours
after the announcement, the Romanian news impact lasted for two-hours, while the Euro Zone
had the weakest impact on volatility. Given the fact that the impact was assumed to last two
hours, Euro Zone news seemed to decrease volatility.
One explanation for these differences comes from the economic environment which defines the
data sets considered. In the first data set the Euro Zone economy was in recession, while in
Romania the economy was seen weakening, but entered the recession only on 15th of May,
almost at the end of the first data set. In the second data set both economies from Romania and
Euro Zone were reporting negative figures.
Another key aspect is the fact that most significant European news comes from monetary policy.
In the first data set ECB was constantly reducing the benchmark interest to diminish the
contraction in domestic demand and to impose tighter financing conditions, while in the second
data set the benchmark interest was the same during the estimation period, in line with market
expectations.
Thank you!
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