Macro-News Impact on Exchange Rates Evidence from high-frequency EUR/RON and EUR/USD dynamics

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Macro-News Impact on Exchange Rates Evidence from
high-frequency EUR/RON and EUR/USD dynamics

• 
  MSc Student Maria-Magdalena Stoica
• 
  Supervisor Professor PhD. Moisa Altar

2
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

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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 fcasting 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

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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

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3. Objectives of the paper

• Explore the link between exchange rates and
  fundamentals and news about fundamentals using
  high-frequency EUR/RON data
• Study determinants of high-frequency EUR/RON
  movements
• Study the response of EUR/RON pair to
  macro-economic news (conditional mean jumps)
• Study how news about fundamentals is incorporated
  by EUR/RON (quick adjustment of returns)
• Study EUR/RON volatility adjustment to macro-news

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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 markets 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

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4. Theoretical considerations

• Obstfeld Rogoff (1996) the nominal exchange
  rate must be viewed as an asset price, depending
  on expectations of future variables

• The no-bubble solution of the model is

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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
• 
  
• -gt 15-minute
  spot exchange rate return
• 
  -gt k-type news
• Volatility model proxies for the
  volatility in 15-min interval t
• 
  
  
• -gtvolatility over the day
  containing the 15-minute interval in question
  (estimated using GARCH)

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5. The model about variables

• -gtstandardized 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)
• -gtannounced value of fundamental indicator k
• -gtmarket expected value for indicator k
  (Bloomberg survey
• median forecast ECO calendar of economic
  releases
• including surveys )
• -gtsample standard deviation of

• Contemporaneous Exchange Rate News Response
  Model

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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 fcast may be stale
• Balduzzi, Elton and Green (1998) most of market
  expectations contain
• information, which is unbiased and does not
  appear significantly stale
• -gtactual announcement
• -gtmarket consensus

-gtchange in (very announcement sensitive) 10-yr
note yield from the time of the survey to
announcement
-gt insignificant gt survey information is unbiased
-gt positive and significant (there is info in
survey) and insignificantly different from unity
-gt the hypothesis that this coefficient 0
cannot be rejected gtmarket consensus is not
stale
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6. Data construction and analysis -
15-minute EUR/RON returns -

• 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 15-minute interval 3.808
returns -Volatility clusters indicating
periodical intraday volatility
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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
  macro-news 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

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6. Data construction and analysis -
basic statistics -

• Negligible mean
• Approximately symmetric, but definitely
  non-Gaussian, due to excess kurtosis

  Mean St. Deviation Skewness Kurtosis
EUR/RON 2.76E-05 0.0014 -0.45 17.54
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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

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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

• 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

EUR/RON pair is determined by news about
fundamentals It is important the overall risk
aversion
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7. Empirical estimation Results - the mean
model for EUR/RON -
Identifying and introducing more news in the
model would probably increase fit
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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
Consumer Confidence Index -0.000806 0.136207

• 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

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7. Empirical estimation results
- volatility model -

• -gtvolatility over the day containing the
  15-minute interval in question
• -gtone-day ahead volatility forecast for day t
  that contains the 15-minute interval in question
• -gtextracted from a GARCH(1,1) with an AR term
  (daily returns over 12/27/2005 4/14/2009)
• The GARCH model

• -gtmean equation

• -gtvariance equation

Constraints ? gt 0 and aßlt1
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7. Empirical estimation results
- volatility model -

• We impose polynomial structure on the response
  patters associated with
• (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 to time we can represent
  the impact over the vent window
  by a polynomial specification (PDL)
• 
  (Weierstrass Theorem)
• We can further write
• Defining
  we may write
• We take J8, P4 and p(8)0 and P(0)0 for NBR

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7. Empirical estimation results
- volatility model -
AR(1) - GARCH(1,1) output AR(1) - GARCH(1,1) output AR(1) - GARCH(1,1) output AR(1) - GARCH(1,1) output AR(1) - GARCH(1,1) output
Coefficient Std. Error z-Statistic Prob.
C -0.000244 0.000139 -1.75355 0.0795
AR(1) 0.115489 0.046762 2.469738 0.0135
Variance Equation Variance Equation Variance Equation Variance Equation
C 5.35E-07 1.71E-07 3.121631 0.0018
ARCH(1) 0.115039 0.035945 3.200436 0.0014
GARCH(1) 0.87304 0.028648 30.47497 0.0000
The sum of the ARCH and GARCH coefficients is
very close to one, indicating that volatility
shocks are quite persistent.
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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)

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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)

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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

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10. References

• Anderesen, G., T., T. Bollerslev, X. Diebold and
  C. Vega (2005), Real-Time Price Discovery in
  Stock, Bond and 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 Real-Time 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