The Impact of Macroeconomic News

on Exchange Rate Volatility

MSc Students: Stan Ana-Maria

Supervisor: Moisă Altăr, PhD

Bucharest

2010

Academy of Economic Studies

Doctoral School of Finance and Banking - DOFIN

Contents

1. Motivation and objectives

2. Literature review

3. Methodology

4. 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

5. Empirical estimation

6. Results

6.1 The impact of news immediate after announcements

6.2 The impact of news two hours after announcements

7. Conclusions

References

1. Motivation and objectives (1)1.1 Motivation 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 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 ReviewThis paper takes the results of the following articles as a benchmark:

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 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.

The FFF method is based on the following decomposition: (1) represents the return series computed as the differences of logarithmic prices 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 is measured by the demeaned absolute returns, and it can be decomposed into the daily volatility component , ,the intraday volatility component and the innovation i.i.d. (with mean zero and unit variance).

After replacing the expected return by mean return and eliminating the daily component (by dividing the volatility measure with where is GARCH (1,1), model estimate for daily

volatility), squaring and taking logs , equation (1) becomes:

(2)

,, , , ,1/2

( ) t nt n t n t n t nR E R s z

N

,t nRt

,t ns ,t nz

R,

1/2

ˆt nN

ˆt

,

, ,1/22ln 2ln( ) 2 ln( )

ˆ /

t n

t n t nt

R Rs z

N

,t nR , 1ln( / )t n t tR P P ,t nE R

3. Methodology – The model, cont. 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:

(3)

Where , c is the constant, and are

normalizing constants, sine and cosine variables are for the capturing of intraday periodicity,

are the indicator variables used for inserting macro news into the model.

When has been estimated, - the estimate for intraday volatility is then obtained with eq. 4.

(4)

This estimate is normalized so that the mean of the normalized seasonality estimate equals one: ,where T is the total number of observations in the data. (5) We get than the filtered return by dividing the original

return by the intradaily volatility component: (6)

2

, 1 2 , , ,1 11 2

2 2( , ) cos sin

D P

t n k k c p s p t nk p

n n p pf c I t n n n

N N N N

,

, 1/22ln

ˆ /

t n

t nt

R Rf

N

1 ( 1) / 2N N 2 ( 1)( 2) / 6N N N

kI

,ˆt nf ,t̂ ns

,ˆ

2,ˆ

t nf

t ns e

,t̂ ns

~,

, [ / ]

,1 1

ˆ

ˆ

t nt n T N N

t nt n

T ss

s

, , ,/t n t n t nR R s

4. Data 4.1 Data description - Eur/Ron exchange rate 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. There were considered two data sets:

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.

1ln( / )t t tR P P

4.2 The state of the economy in Romania and Euro Zone (I) 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.

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) 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).

Figure 2 Eur/Ron exchange rate 17.12.2009 – 19.05.2010

4.3 Macroeconomic news 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:

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.

 

Observations

05.01.2009–29.05.2009 17.12.2009–19.05.2010

Romania 89 49

Euro Zone 140 73

Announcements with forecast 50 52

5. Empirical estimation 5.1 Intraday dynamics of volatility 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; the European markets open around 7:00 GMT and volatility increases more; 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).

Figure 3 Autocorrelation coefficients of 288 Figure 4 Autocorrelation coefficients for 1500 lags

Five-minute lag

5.2 Flexible Fourier Form regression method (I) 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, I k 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:

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.

Table 2. Key statistical figures – filtering in subsets

Key statistical figures

Original returns

Filtered returns-week subsets

Mean -1.41E-07 -2.95106E-05

Standard Error 2.02E-06 7.10935E-05

Mean absolute return 6.38E-11 -2.95106E-05

Standard Deviation

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

Sum -0.0045 -0.935

Count

31,679

31,679

5.2 Flexible Fourier Form regression method (II) 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.

Table 3. Key statistical figures of raw and filtered Table 4. Key statistical figures of raw and filtered

five minute logarithmic return series of Eur/Ron five minute logarithmic return series of Eur/Ron

exchange rate from the period 05.01.2009– exchange rate from the period 17.12.2009–

29.05.2009. 19.05.2010

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

Kurtosis

44.46 82.76

Skewness

0.38 1.25

Minimum -0.0064 -0.0080

Maximum

0.0052

0.0071

Sum

0.04 -0.03

Count

30,239

30,239

Key statistical figures Returns Filtered returns

Mean -1.41E-07 -9.29E-06

Standard Error 2.02E-06 3.12E-05

Mean absolute return 6.38E-11 -9.29E-06

Standard Deviation

0.00036 0.00556

Kurtosis

21.03 129.84

Skewness -0.23 -3.34

Minimum -0.007 -0.180

Maximum

0.007 0.120

Sum -0.0045 -0.2942

Count

31,679 31,679

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. Results6.1 The impact of news immediate after announcements To test the impact that the macro figure has immediately after the announcement, we use the

model:

(7) ,

where denotes the filtered returns, the news variable, c is the constant term and 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 , 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: is 1.023164 and 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: is 1.076604 and 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.

2,

,1/21

2ln ( , )ˆ /

t n

k k t nkt

R Rc I t n

N

,t nR ( , )kI t n,t n

k

RomaniaEZ

RomaniaEZ

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)

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 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),

The impact of news on volatility , can than be computed for each 25 intervals as:

(9)

Now when the macro figure is announced, the news variable takes the value of

for the first 25 intervals after the announcement and zero otherwise.

3 2 21 2 3( ) (1 ( / 25) ) (1 ( / 25) ) (1 ( / 25))i b i b i i b i i

( )i

( )kM i

( )( ) exp 1

2k

k

iM i

,kI t n

( )i

6.2 The impact of news two hours after announcements (II)

We obtain the following results: for the first data set is 20.25673 and is 30.19419.

For the second data set we obtain is 20725.18 and is 1.138040.

a) Decay structure of volatility b) Decay structure of volatility

response pattern after Romanian news response pattern after Euro Zone newsFigure 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 12 th 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.

Romania EZ

Romania EZ

6.2 The impact of news two hours after announcements (III)

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.

Table 5 Impact of macroeconomic news on Eur/Ron volatility, following two hours after announcement

Romania-2009 Euro zone-2009 Romania-2010 Euro zone-2010

Mk(1)    Mk(1)    Mk(1)    Mk(1) 

Returns filtered withFFF regression method  20.25673 0.00059 30.19419 0.00479 20725.18 117.7851.13804 0.000010

k k k k

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 was greater than , 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 was much higher than the result obtained in the first data set, and also higher than the news from Euro-Zone from the second data set. The indicator Mk(1) shows the impact in the first five-minute after the announcement is higher when Romanian announcements were released, while the impact from the Euro Zone is not significant.

EZ Romania

Romania

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 15 th 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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