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 variables ”
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5. The model - equations
Following Andersen, Bollerslev, Diebold & Vega, we use a model that allowsfor news affecting both the conditional mean and conditional variance:
Mean model: we allow for the disturbance term to be heteroskedastic
-> 15-minute spot exchange rate return
-> k-type news
Volatility model: proxies for the volatility in 15-min interval t
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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 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 ->actual announcement
->market consensusittiitiiit eyFA 210 itA
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->change in (very announcement sensitive) 10-yr note yield from the time of the survey to announcement
i1 -> positive and significant (there is info in survey) and insignificantly different from unity
i2 -> the hypothesis that this coefficient = 0 cannot be rejected =>market consensus is not stale
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:
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- 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
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
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
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
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
immediatelyEUR/RON pair is determined by news about fundamentals
It is important the overall risk aversion
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
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
7. Empirical estimation & results - volatility model -
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Constraints: ω > 0 and α+β<1
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 responsepatterns are completely determined by the response horizon J”, the polynomial order P, and theendpoints 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)
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Defining: we may write:
We take J’’=8, P=4 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
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
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.
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
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