Macro-News Impact on Exchange Rates
Evidence from high-frequency EUR/RON and EUR/USD dynamics
MSc Student: Maria-Magdalena Stoica
Supervisor: Professor PhD. Moisă Altăr
Topics of the paper
1. Importance of the theme
2. Exchange rates link to fundamentals (Brief literature review)
3. Objectives of the paper
4. Theoretical considerations
5. Model
6. Data construction and analysis
7. Empirical estimation & Results
8. Conclusions
9. Future Research
10. References
1. Importance of the theme

Proof that exchange rates are linked to fundamentals (long lasting
puzzle in International Economics)

Understanding the underlying determinants of exchange rates is
important for further understanding and f’casting the impact of
exchange rates on macro variables (e.g. inflation – pass through)

Provides inside in trading the macro-news arrival on the
EUR/RON market
2. Exchange rates link to fundamentals
(Brief literature review)

International economics puzzle: difficulty of tying floating exchange
rates to macroeconomic fundamentals

“Efficient markets” theory suggests that asset price should completely
and instantaneously reflect movements in underlying fundamentals

Meese and Rogoff (1983): fundamental variables do not help predict
future changes in exchange rates

Engle and West (2004): exchange rates manifests near random walk
behavior, in a rational expectations present value model

Andersen, Bollerslev, Diebold and Vega (2002): high-frequency
exchange rate dynamics are linked to fundamentals
4. Theoretical considerations

Exchange rate models (since 1970): nominal exchange rates are
asset price, thus influenced by expectation about the future

Frenkel & Mussa (1985):...”exchange rates should be viewed as
prices of durable assets, determined in organized markets, in which
current prices reflect market’s expectations concerning present and
future economic conditions relevant for determining the appropriate
values of these durable assets and in which […] price changes
reflect primarily new information that alters expectations concerning
these present and future economic conditions”

“Asset-market approach to exchange rates”: exchange rate is
driven by a present discounted sum of expected future fundamentals
4. Theoretical considerations

Obstfeld & Rogoff (1996): “the nominal exchange rate must be
viewed as an asset price, depending on expectations of future
xt  (nx1) vector of fundamentals
variables ”
Rt  (1  b)a'1 xt  j  ba' 2 xt  j  bEt Rt 1
b  discount
factor (0  b  1)
a1 , a2  (nx1) vectors

The “no-bubble” solution of the model is :


Rt  (1  b) b Et (a x )  b b j Et (a ' 2 xt  j )
j
j 0
'
1 t j
j 0
bEt st 1   0 as j   
5. The model - equations
Following Andersen, Bollerslev, Diebold & Vega, we use a model that allows
for news affecting both the conditional mean and conditional variance:
Mean model: we allow for the disturbance term to be heteroskedastic

I
K
(1) Rt ,n   0   i Rt ,n j 
i 1
Volatility model:

k 1
 t ,n
J

j 0
kj
S k ,t , n  j   t , n
S k ,t , n
(2)  t ,n  c  l   t ,nl 
l 1
^
 d (t )
Q
N
K
J'
   kj ' S k ,t ,n j '
k 1 j '0
J"
q 2n
q 2n
  ( c,q cos(
)   s ,q sin(
))    j" Dt ,n j"  ut ,n
N
N
q 1
j "0
Q
->volatility over the day containing the 15-minute interval in question (estimated using GARCH)
 ( c,q cos(
q 1
 d (t )
-> k-type news
proxies for the volatility in 15-min interval t
^
L
Rt , n-> 15-minute spot exchange rate return
q 2n
q 2n
)   s ,q sin(
))
N
N
->calendar effect pattern (FFF – capture the high-frequency
rhythm of deviations of intra-day volatility from daily average)
5. The model – about variables
S kt ,n 
Akt ,n  Ekt ,n
^
k
->standardized ”news” quantifies the deviation of the
announcement relative to what the market expected (facilitates
meaningful comparison of response of the pair to different
pieces of news)
Akt , n
->announced value of fundamental indicator k
E kt , n
->market expected value for indicator k (Bloomberg survey
median forecast – ECO: calendar of economic releases
including surveys )
^
k

->sample standard deviation of
Akt  Ekt
Contemporaneous Exchange Rate News Response Model
(3) Rt ,n   k S k ,t ,n  t ,n
5. The model – about the “news”
There is the possibility that the market expectation may not capture all info
available immediately before the announcement, namely ECO f’cast may be stale
Balduzzi, Elton and Green (1998): most of market expectations contain
information, which is unbiased and does not appear significantly stale
Ait ->actual announcement
Ait   0i  1i Fit   2i yt  eit
Fit->market consensus
yt ->change in (very announcement sensitive) 10-yr note
yield from the time of the survey to announcement
 0 i -> insignificant => survey information is unbiased
1i -> positive and significant (there is info in survey) and insignificantly different from unity
 2i -> the hypothesis that this coefficient = 0 cannot be rejected =>market consensus is not
stale
6. Data construction and analysis
- 15-minute EUR/RON returns 

Rt ,n  ln( qt ,n )  ln( qt ,n 1 )
15-minute EUR/RON logarithmic returns:
The return series was constructed from Reuters tick-by-tick (30.000) records
of EUR/RON quotes over 19th Sep 2008 to 15th April 2009 time span:
- At the end of each 15-minute interval we used
the immediately preceding and following quote to
generate the relevant quote (the quotes were
weighted by their inverse relative distance to the
endpoint);
- We kept the days with at least 8 trading hours;
- We maintained a fixed number of return per
trading day, ending up with: 119 days x 32 15minute interval = 3.808 returns
-Volatility clusters indicating periodical intraday
volatility
6. Data construction and analysis
- macro announcements

Macro-news data series – constructed from realized and expected
macroeconomic fundamentals (Bloomberg ECO)
The macro-news series are similar to a dummy variable, with the
“standardized news” replacing the 1 terms (different importance of the macronews as per the magnitude of the difference between realizations and
expectations)

News for US, Euro-Zone and Romania: 35 “news” categories

US and Euro-Zone announcements time are known in advance

For Romania not all the timing of the announcements are known in advance

No expectations for some of the Romanian fundamentals: use of dummies

Matched “news” with return data, by placing the “standardized news” to the
relevant return
6. Data construction and analysis
- basic statistics -
Mean
EUR/RON


2.76E-05
St. Deviation
Skewness
0.0014
-0.45
Kurtosis
17.54
Negligible mean
Approximately symmetric, but definitely non-Gaussian, due to excess kurtosis
5. Data construction and analysis
- basic statistics -



The raw returns display tiny, but statistically significant serial correlation
The absolute returns exhibit strong serial correlation
Testing for Unit Root – neither of the variables have a unit root
7. Empirical estimation & Results
- the mean model for EUR/RON Variable
Coefficient
Std. Error
t-Statistic
Prob.
RAND(-1)
0.069051
0.016054
4.301163
0.0000
RAND(-2)
-0.03045
0.016039
-1.89831
0.0577
BNR
-0.00434
0.000512
-8.46543
0.0000
US_CONS_CONFID
-0.00169
0.000555
-3.04934
0.0023
US_RET_SALES
-0.00128
0.0006
-2.13837
0.0326
US_CAP_UTIL
-0.00162
0.000734
-2.20753
0.0273
Variable
Coefficient
Std. Error
t-Statistic
Prob.
RAND(-1)
0.069051
0.032461
2.127173
0.0335
RAND(-2)
-0.03045
0.024548
-1.24034
0.2149
BNR
-0.00434
0.002038
-2.12849
0.0334
US_CONS_CONFID
-0.00169
0.000563
-3.00388
0.0027
US_RET_SALES
-0.00128
0.000687
-1.86872
0.0617
US_CAP_UTIL
-0.00162
0.000305
-5.32368
0.0000
EUR/RON pair is determined by news about fundamentals
It is important the overall risk aversion
OLS Estimation

A/C and heteroskedastic errors (used in
the volatility model)

R-squared ~ 2% (only half of the days in
the sample contain a news
announcement and each day has 32
15-min intervals, which corresponds to
~2% of the sample)
HAC Estimation

All news coefficients remain significant

News incorporating info about state of US
economy are significant (natural in the
current economic environment – focus on
growth)

Contemporaneous news are significant

The exchange rate adjusts to news
immediately
7. Empirical estimation & Results
- the mean model for EUR/RON -
Identifying and introducing more “news” in the model would probably increase fit
7. Empirical estimation & Results
- contemporaneous exchange rate news response model -
News
Coefficient
R-squared
Retail sales
-0.001286**
0.436349
Capacity utilization
-0.001529**
0.749179
-0.000806*
0.136207
Consumer Confidence Index
When focusing only on the importance of the news during announcement periods we
obtain significantly larger R-squared

Only the news exerting significant influence in model (1) remain significant

The news fount not significant with model (1) remain insignificant

7. Empirical estimation & results
- volatility model ^
L
(2)  t ,n  c  l   t ,nl 
l 1
 d (t )
32
K
J'
   kj ' S k ,t ,n j '
k 1 j ' 0
J"
q 2n
q 2n
  ( c,q cos(
)   s ,q sin(
))    j" Dt ,n j"  ut ,n
32
32
q 1
j "0
Q
^
 d (t )
->volatility over the day containing the 15-minute interval in question
->one-day ahead volatility forecast for day t that contains the 15-minute interval in question
->extracted from a GARCH(1,1) with an AR term (daily returns over 12/27/2005 – 4/14/2009)

The GARCH model
Rt    AR(1)   t
 t 2     t21  t21
->mean equation
->variance equation
Constraints: ω > 0 and α+β<1
7. Empirical estimation & results
- volatility model 
 We impose polynomial structure on the response patters associated with
j"
(Polynomial specifications allow for tractability & flexibility. Using PDL we can ensure that the response
patterns are completely determined by the response horizon J”, the polynomial order P, and the
endpoints constraint imposed on p(J”), p(0))

If an NBR intervention affects volatility from time t 0 to time t0  J ' ' we can
represent the impact over the vent window   0,1,..., J ' ' by a polynomial
specification (PDL):
  p( )  c  c   ...  c  p (Weierstrass Theorem)



0
1
p
J ''
J ''
J ''
 0
 0
i 0
We can further write:   ...  c
p
t ,n
0  Dt , n   c1  Dt , n   ...  c p  Dt , n   ut
Defining:
J ''
J ''
 0
i 0
Z 0t   Dt ,n  ...Z pt  p Dt ,n 
we may write:
 t ,n  ...  c0 Z 0t  c1Z1t  ...  c p D pt  ut

We take J’’=8, P=4 and p(8)=0 and P(0)=0 for NBR
7. Empirical estimation & results
- volatility model AR(1) - GARCH(1,1) output
C
AR(1)
Coefficient
Std. Error
z-Statistic
Prob.
-0.000244
0.000139
-1.75355
0.0795
0.115489
0.046762
2.469738
0.0135
Variance Equation
C
5.35E-07
1.71E-07
3.121631
0.0018
ARCH(1)
0.115039
0.035945
3.200436
0.0014
0.87304
0.028648
30.47497
0.0000
GARCH(1)
The sum of the ARCH and GARCH coefficients is very close to one, indicating
that volatility shocks are quite persistent.
7. Empirical estimation & Results
- volatility model -
Exchange rate volatility adjusts gradually, with
complete adjustment after about one hour
News that are not significant for the mean model,
affect the volatility (confusion in the market given
the current macroeconomic environment)
8. Conclusions

News produce very quick conditional mean jumps to EUR/RON pair

The exchange rate adjusts to news immediately: contemporaneous news are
statistical significant in the mean model

News incorporating info about state of US economy are significant (natural in
the current economic environment – focus on growth)

Favorable US “growth news” tends to produce RON appreciation (risk
aversion improves, buy RON vs. EUR )

Exchange rate volatility adjusts gradually, with complete adjustment after
about one hour (news up to lag 4 are significant/ up to lag 8 for NBR)

News that are not significant for the mean model, affect the volatility
(confusion in the market given the current macroeconomic environment)
9. Future Research

Asymmetric response of exchange rates to news

Order flow implication in news transmission to exchange rates (Is news
affecting exchange rates via order flow?)

Explore not only the effects of regularly-scheduled quantitative news on
macroeconomic fundamentals, but also the effects of irregular news

Analysis of joint responses of FX, stock market and bond market to news
10. References
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Foreign Exchange Markets”, National Bureau of Economic Research Working Papers, 11312
Anderesen, G., T., T. Bollerslev, X. Diebold and C. Vega (2002), “Micro Effects of Macro Announcements: RealTime Price Ddiscovery in Foreign Exchange", NBER Working Papers, 8959
Cai F., H. Joo, and Z. Zhang (2009) “The Impact of Macroeconomic Announcements on Real Time Foreign
Exchange Rates in Emerging Markets”, Board of Governors of Federal Reserve System, International
Finance Discussion Paper, No. 973
Anderesen, G., T., and T. Bollerslev (1996), “DM-Dollar Volatility: Intraday Activity Patterns, Macroeconomic
Announcements, and Longer Run Dependencies”, National Bureau of Economic Research Working
Papers, 5783
Engel, C., N. Mark, and K. D. West (2007), “Exchange Rate Models Are Not as Bad as You Think”, National
Bureau of Economic Research Working Papers, 13318
Engel, and K. D. West (2004), “Exchange Rates and Fundamentals”, National Bureau of Economic Research
Working Papers, 10723
Laakkonen, H., “The Impact of Macroeconomic News on Exchange Rate Volatility” (2007), Finnish Economic
Papers
Evans, M., D., D., and R. K. Lyons (2005), “Do Currency Markets Absorb News Quickly”, National Bureau of
Economic Research Working Papers, 11041
Evans, M., D., D., and R. K. Lyons (2003), “How is Macro News Transmitted to Exchange Rates”, National
Bureau of Economic Research Working Papers, 9433
Dominguez, K., and F. Panthaki (2005), “What Defines ‘News’ in Foreign Exchange Markets?”, National Bureau
of Economic Research Working Papers, 11769
Laakkonen, H., and M. Lanne (2008), “Asymetris News Effects on Volatility: Good vs. Bad News in Good vs. Bad
Times”, Helsinki Center of Economic Research, Discussion Paper No. 207