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Journal of International Money and Finance 49 (2014) 319e339
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Journal of International Money and Finance
journal homepage: www.elsevier.com/locate/jimf
The euro exchange rate during the European sovereign debt crisis e Dancing to its own tune?*
Michael Ehrmann a, *, Chiara Osbat b, Jan Str�aský c, Lenno Uusküla d
a Bank of Canada, 234 Laurier Avenue West, Ottawa, ON K1A0G9, Canada b European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany c Organisation for Economic Co-operation and Development, 2, rue Andr�e Pascal, 75775 Paris Cedex 16, France d Bank of Estonia, Estonia pst 13, 15095 Tallinn, Estonia
a r t i c l e i n f o
Article history: Available online 5 July 2014
JEL codes: E52 E62 F31 F42 G14
Keywords: Exchange rate volatility Fundamentals Announcements Public debate Sovereign debt crisis
* This paper presents the authors' personal opin European Central Bank, the Bank of Estonia, the E * Corresponding author. Tel.: þ1 613 782 8111.
E-mail addresses: [email protected] (J. Str�aský), [email protected] (L. Uuskül
http://dx.doi.org/10.1016/j.jimonfin.2014.06.008 0261-5606/© 2014 Elsevier Ltd. All rights reserved
a b s t r a c t
This paper studies the determinants of the euro exchange rate volatility during the European sovereign debt crisis, allowing a role for macroeconomic fundamentals, policy actions and the public debate by policy makers. It finds that the euro exchange rate mainly danced to its own tune, with a particularly low explanatory power for macroeconomic fundamentals. The findings of the paper also suggest that financial markets might have been less reactive to the public debate by policy makers than previously feared. Still, there are instances where exchange rate volatility increased in response to news, such as on days when several politicians from AAA-rated countries went public with negative statements, sug- gesting that communication by policy makers at times of crisis should be cautious about triggering undesirable financial market reactions.
© 2014 Elsevier Ltd. All rights reserved.
ions and does not necessarily reflect the views of the Bank of Canada, the urosystem or the OECD.
(M. Ehrmann), [email protected] (C. Osbat), [email protected] a).
.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339320
1. Introduction
The global financial crisis and the subsequent European sovereign debt crisis had substantial ef- fects on global exchange rate configurations (see, e.g., Fratzscher, 2009). Compared to the years 2007e2009, the turbulence in foreign exchange markets has receded at the global level, but the exchange rate of the euro against many currencies has remained extremely volatile during the entire European sovereign debt crisis. Compared to the implied volatilities in the early years of European Monetary Union (EMU), those experienced during 2010 and 2011 were extreme, amounting to a 3- standard deviation event for the euro-U.S. dollar exchange rate, a 4-standard deviation event for the euro exchange rates against the British pound and the yen, and a 10-standard deviation event for the euro-Swiss franc exchange rate.1
Several commentators have attributed this exchange rate turbulence not only to the economic fundamentals, but also to the public controversy about the European sovereign debt crisis and the required policy actions. In particular, there had been the concern that the heated public debate among policy makers could instil unnecessary volatility in financial markets, to such an extent that the Eu- ropean Central Bank (ECB) President Jean-Claude Trichet, on July 18th, 2011, stressed the “absolute need to improve ‘verbal discipline’” and asked governments “to speak with one voice on such complex and sensitive issues as the crisis.”2
Against this background, the current paper studies the determinants of the euro exchange rate volatility during the European sovereign debt crisis. It allows for a role of macroeconomic funda- mentals as well as for actions and statements by policy makers, and also analyses the impact of rating agencies' decisions. To study the role of the public debate on exchange rate volatility, the paper de- velops a unique database covering more than 1100 public statements about the sovereign debt crisis by policy makers at the national European and at the international level, covering the period from October 1st, 2009 until November 30th, 2011.
The paper first demonstrates the enormous intensity of the public debate about the European sovereign debt crisis, which involved politicians in virtually all countries of the euro area, central bankers and policy makers at the IMF and the European Union level. The intensity of the public debate and its controversy evolved in accordance with the severity of the crisis: with increasing government bond spreads of the countries under an EU/IMF adjustment program, the number of statements grew substantially, along with the dispersion of views. Generally, the level of dispersion across statements was rather high, pointing to a very heated public debate.
The paper also shows that fundamentals and the public discourse have generally very little explanatory power in describing the volatility of the euro exchange rate e all but two potential de- terminants appear unimportant. The ECB's actions have had dampening effects on exchange rate volatility; by contrast, public statements by politicians in AAA-rated countries are consistently found to have increased volatility. Interestingly, this increase was strongest if their statements expressed rather homogeneous (and negative) views, whereas it was less pronounced, the more dispersed their communication was.
Splitting the statements in terms of their content, effects on the euro's volatility were primarily, if not exclusively, triggered by comments about rescue packages to euro area countries and their like- lihood and conditions, about a possible default of a country, or about private sector involvement. None of the other types of statements that we distinguish, namely those about the ECB's monetary policy, the EU's policy response to the crisis, structural measures or fiscal policy measures to be taken by countries under stress, are found to have affected the exchange rate or its volatility in a systematic fashion.
The main conclusions from the paper are therefore that the euro exchange rate was mainly dancing to its own tune, and that financial markets might have priced assets more independently from the public debate than previously feared. However, politicians' statements have had some effects on ex- change rate volatility, suggesting that communication strategies by policy makers at times of crisis should be particularly cautious about triggering undesired financial market reactions.
1 These figures are based on daily implied volatilities for the years 2002e2006 and 2010e2011. 2 See Financial Times Deutschland, 18 July 2011.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339 321
The paper proceeds as follows: Section 2 overviews the related literature. The data and the econometric methodology are explained in Section 3. Section 4 describes the evolution of the public debate on the European sovereign debt crisis, and Section 5 presents the results on the determinants of the euro exchange rate volatility during the European sovereign debt crisis. These results are subjected to several robustness tests in Section 6. Section 7 concludes.
2. Literature review
This paper relates to several strands of literature. The first focuses on the effects of scheduled and unscheduled macroeconomic announcements on exchange rates. Andersen et al. (2003) show that exchange rates tend to react quickly to news, that timeliness of the news matters, and that U.S. macroeconomic announcements tend to be more influential than German and European ones. In a similar vein, Faust et al. (2007) argue that the effect of macro announcements on exchange rates and other asset prices depends on the source of the shock and on how it changes the public perception of the state of the economy. These findings from studies with high-frequency data are broadly confirmed by studies using daily data, such as Ehrmann and Fratzscher (2005), Johnson and Schneeweis (1994), Kim (1998) or Kim (1999).
A second strand of the literature analyses effects of communication by policy makers on exchange rate returns and volatility. There is ample evidence that communication by central banks about monetary policy affects exchange rates: Sager and Taylor (2004) as well as Conrad and Lamla (2010) find this to be the case for the ECB, and Melvin et al. (2009) for the Bank of England. Furthermore, several studies have documented how oral exchange rate interventions affect exchange rate returns and volatilities. Whereas Jansen and de Haan (2005) only find effects of ECB interventions on the euro's conditional volatility, Fratzscher (2006) finds substantial effects of ECB communications on both the spot and forward euro- dollar exchange rate returns. More recently, Dewachter and Erdemlioglu (2014) expanded this litera- ture to show that communications trigger large jumps in the euro-dollar rate, and raise its volatility.
A third strand of the literature that is highly relevant for the current paper analyses the effects of news and statements by politicians during the European sovereign debt crisis. These papers construct databases containing public statements like we do in this paper, but follow different paths. Beetsma et al. (2013) construct a news variable using the Eurointelligence daily newsflash, and code the con- tent in a very similar fashion to ours. They find that the quantity of news matters, as more news tend to increase government bond spreads of the peripheral countries. Also the content of news is found to be important, with bad news explaining upward pressure on spreads. Similarly, Mohl and Sondermann (2013) construct variables related to politicians' statements based on the frequency of statements reported by news agencies, without differentiating their content. They find that more statements are correlated with increasing spreads and heightened conditional volatility, particularly when made by politicians from AAA-rated countries. However, as we will argue later, public statements may be endogenous to the developments in government bond yields, such that these papers are more likely to identify correlation rather than causality.
Mink and de Haan (2013) identify news about the European sovereign debt crisis by looking up the news on days that saw large changes in Greek government bond yields. The paper finds that news about financial support measures for Greece affect bank stocks, even for banks without exposure to Greece or other peripheral euro zone countries. Finally, Kilponen et al. (2012) and Smeets and Zimmermann (2013) focus on policy initiatives related to the resolution of the European sovereign debt crisis and EU summits in particular. Whereas Kilponen et al. (2012) identify an effect of policy measures on government bond spreads (for instance, decisions on support packages and the EFSF typically decreased spreads), Smeets and Zimmermann (2013) find little repercussions of EU summits on financial markets.
This paper contributes to the literature in four ways. First, the paper can serve as a cross-check of the earlier literature on communication by central bankers and politicians, because the collection of statements by policy makers is consistent with the earlier studies by Ehrmann and Fratzscher (2007, 2011). Second, in contrast to the previous literature, it focuses on the second moment of financial market returns and tests a different hypothesis, namely whether policy makers' communication (and the controversy of the debate in particular) have increased financial market volatility. Third, while most papers study the effect of crisis communication on government bond yields, this paper extends the
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339322
literature by looking at the effect on the euro exchange rate. Finally, it does so in a novel way, namely by modelling the first principal component of the euro exchange rate returns against the major currencies.
3. Data and methodology
3.1. The volatility of the euro exchange rate
We are interested in explaining the evolution of the euro exchange rate volatility during the Eu- ropean sovereign debt crisis. Our dataset therefore starts October 1st, 2009,3 and ends on November 30th, 2011. The frequency of the data is daily, as it is not possible to identify the exact timing when news of public statements reach the financial markets at intra-day frequency. Even though newswire reports typically have a precise time stamp when issued, this cannot be recovered ex post, such that we can only identify the day of a given statement.
We measure exchange rate volatility based on the conditional volatility of spot exchange rates (provided by Bloomberg), resulting from the estimation of an EGARCH model.
Due to the bilateral nature of exchange rates, an analysis of the exchange rate of a given currency pair requires modelling all potential determinants in both economies. To avoid this complication, and to give a more robust effect of the events in the euro area, we model the first principal component of the euro exchange rate returns against the major currencies, namely the U.S. dollar, the British pound, the Swiss franc and the Japanese yen.4
The first principal component of the exchange rate returns explains 56.9% of their overall variance, whereas the second and third principal components explain another 21.5% and 15.3% of the overall variance of the spot exchange rates. In addition, while all spot exchange rates load with similar magnitudes on the first principal components,5 this is no longer the case for the subsequent principal components. The second principal component, for instance, is clearly dominated by the Swiss franc.
As to the potential determinants of the euro exchange rate and its volatility, we differentiate three types e i) public statements by policy makers; ii) actions at the EU level and by the ECB, as well as rating announcements by the three largest rating agencies (Fitch, Moody's and Standard&Poor's), and iii) announcements of macroeconomic data.
3.2. Potential determinant 1: the public debate about the European sovereign debt crisis
For the first group of potential determinants, public statements by policy makers, we first assembled a list of potential speakers: i) the presidents, prime ministers, finance ministers and economy ministers of all euro area countries, as well as the leaders of the parliamentary opposition; ii) the Managing Di- rector of the IMF; iii) the presidents of the European Council and the European Commission, as well as the Commissioners for Economic and Monetary Affairs; iv) all members of the ECB's Governing Council (i.e. the members of the ECB's Executive Board and all Governors of the National Central Banks of the euro area countries); and v) a group of other speakers that might affect markets and have been relatively vocal during the European sovereign debt crisis, namely George Soros, Warren Buffett and Mohamed El- Erian, who at the time was the CEO of PIMCO, one of the world's largest bond investors.6
To identify the relevant statements by these speakers, we used reports by Reuters News as con- tained in Factiva, and extracted all database entries containing a reference to the name of the speaker
3 The start date is selected to lie two weeks before Greek Prime Minister George Papandreou in his first parliamentary speech disclosed the country's severe fiscal problems on October 16th, 2009. It also coincides with end-September 2009 when the original Irish blanket guarantee (CIFS) has been extended from 1 year to 2 years.
4 Other possibilities would have been to use a trade-weighted exchange rate or the euro exchange rate against the IMF's Special Drawing Rights. Both options are similar to our approach, as they also simply take a weighted average of spot exchange rates. For a related approach see Engel et al. (2014), who extract common factors from a cross-section of exchange rates against the U.S. dollar and show that the factors can be usefully employed in forecasting the exchange rate.
5 The factor loadings of the first component are as follows: U.S. dollar 0.59; Swiss franc 0.55; British pound 0.38; Japanese yen 0.45.
6 For the complete list of speakers, see Table A1 in the annex.
Table 1 Summary statistics for the statements database.
Country measures
ECB policies
EU policies
Financial support
Fiscal reform
Total
By country/speaker group Austria 0 0 5 11 2 18 Belgium 1 0 6 3 1 11 Cyprus 0 0 0 1 3 4 Estonia 0 0 2 1 1 4 Finland 3 0 10 14 1 28 France 13 0 19 24 12 68 Germany 13 2 74 81 19 189 Greece 15 0 5 30 22 72 Ireland 8 0 4 25 3 40 Italy 7 0 5 3 17 32 Luxembourg 1 0 20 27 5 53 Netherlands 1 0 8 8 0 17 Portugal 15 0 0 21 26 62 Slovakia 0 0 12 15 3 30 Spain 10 0 2 17 11 40 European Central Bank (ECB) Executive Board 22 34 30 64 21 157a
National Central Bank (NCB) Governors 43 19 38 59 20 178a
European Union (EU) officials 17 2 26 56 22 123 International Monetary Fund (IMF) 2 0 4 11 3 20 Other 8 1 1 9 0 19 By coding Positive 83 20 122 292 120 626a
Negative 79 33 131 157 51 448a
Neutral 17 5 18 31 21 91a
Total 179 58 271 480 192 1165a
Note: The table shows the number of public statements contained in the database and a breakdown by speaker groups, coding and topics.
a Some statements were classified into several topics, such that the sum of statements by topic exceeds the total number of statements.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339 323
and a broad set of keywords.7 From all hits, we extracted those containing statements by the relevant speakers that are related to the European sovereign debt crisis, carefully avoiding double counting and making sure that we include only the first report about a given statement.
We classified the statements into five different topics. The first group, labelled “financial support”, contains statements about rescue packages to euro area countries and their likelihood and conditions, about a possible default of a country, or about private sector involvement. The second one, labelled “ECB policies”, relates to the ECB's standard and non-standard policies, including its Securities Market Program (SMP) and changes in its collateral rules. The third group is called “EU policies” and includes statements about EU initiatives aimed at solving the sovereign debt crisis, such as the establishment of the ESM, the EFSF, the fiscal compact, issuance of Eurobonds, etc. It also includes statements about the possible euro area exit of a country under stress. Another category, labelled “country measures”, dis- cusses country-level measures, such as structural reforms (excluding fiscal policy measures), as well as statements about the severity of the crisis for single countries. The last group collects all statements about setting and achieving public budget goals, and is called “fiscal reform”.
In total, our database includes 1165 statements (see Table 1). The breakdown into speaker groups shows that most statements were made by politicians from Germany (189), the central banks (157 by members of the ECB's Executive Board, and 178 by Governors of the National Central Banks of the euro
7 The search words were (in alphabetical order): aid; austerity; bailout; bank involvement; bond purchases; debt crisis; debt reduction; default; downgrade; European Financial Stability Facility (EFSF); European Financial Stability Mechanism (EFSM); European Stability Mechanism (ESM); euro bonds; euro zone bond; fiscal consolidation; government bonds; government debt; guarantee; haircut; investor involvement; negative outlook; private sector involvement; programme; private sector involve- ment (PSI); rating; reform; reforms; reprofile; reprofiling; rescue; restructure; restructuring; restructuring; securities market programme (SMP); sovereign debt; support programme; troika.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339324
area) and by EU officials (123). A breakdown by topic reveals that the bulk of all statements falls into the “Financial support” category (480), followed by comments about “EU policies” (271). Statements about “Fiscal reform” and “Country measures” are roughly equally represented, with 192 and 179 occurrences each, whereas there are only 58 statements about the ECB's policies. Looking at the breakdown of statements by speaker groups and by topic, comments about the ECB policies are nearly exclusively made by central bankers, whereas all other topics are covered by most speaker groups.
Beyond the occurrence of a statement (which can be measured by a dummy variable), we are also interested inwhether its effect on the exchange rate volatility depends on its content. To also test this, we classified each statement depending on whether it contains “positive” (coded þ1) or “negative” (coded �1) news about the European sovereign debt crisis. For example, when a finance minister confirms that agreed budget cuts will be achieved, this is coded as positive news. Support for further EUpolicies, such as the fiscal compact, would also be coded as positive news, whereas a statement suggesting the exit of a country from monetary union is classified as negative news. Of course, there are also neutral statements (coded 0). The Annex contains examples of statements with their respective category and coding.
Table 1 also provides an overview of the statement content. Of the 1165 statements in the database, we coded the majority, namely 626, to be positive, 448 as negative, and 91 as neutral.
A number of issues are worth noting about this data extraction exercise and the subsequent coding. First, due to the use of Reuters News to extract the statements, we clearly take a financial market perspective. Public statements that are not reported by the newswires would not necessarily reach financial markets, but for the purposes of the analysis in this paper this is not an issue, as we are only interested in the reaction of financial markets to the public debate.
Second, as the search was only conducted in English, we might not have discovered all statements. However, as financial markets tend to follow newswires in English, which cover this topic very extensively, this should not be a problematic issue.
Third, a key difficulty is how to ensure a correct classification of statements. It is important to stress that the classification is based on our own judgment and reading of the reports and thus prone to error. In line with the techniques of content analysis (e.g. Holsti, 1969), we had different individuals classify the statements independently and discarded the non-unanimous ones. However, in the vast majority of cases the wording of statements was very clear and a unanimous classification was generally ach- ieved. The appendix provides a number of statements contained in our database along with our classification, allowing the reader to cross-check our classification.
After identifying and classifying all relevant statements, we aggregated them into various groups of speakers because if we were to include one variable for each of the 95 speakers, the econometric model would lack degrees of freedom. Of course, there are several ways of aggregating these types of data, each with advantages and disadvantages. For instance, when aggregating the statements by politicians to the country level, one could give larger weights to the head of government than to ministers, or to ministers than to the leader of the opposition. However, any weighting scheme requires much judg- ment, and reasonable weights would differ across countries and time, given that the influence of a particular person might not only depend on her position on the job and in the debate, but also on how the debate evolves, etc.
We therefore decided for an unweighted aggregation of all speakers within a given group of speakers by just taking the sum of all sspeaker,t on each given day t, for all speakers that are part of the group. We have done such aggregation for all EU officials, for the members of the Executive Board of the ECB and of the remaining group of National Central Bank Governors, for the other speakers (el-Erian, Buffett and Soros), and for politicians (excluding the National Central Bank Governors) at the country level as well as at the level of country groups.
Note that this aggregation implies that if there are two statements on a given day, one coded as þ1, one as �1, the aggregation is equal to zero. We will also use a summation of the number of statements in a given speaker group (in the example above, there were two statements, such that this variable would equal two). For robustness, we will also use {�1,0,þ1}-variables that report the sign of the aggregated views, and a dummy variable that is equal to one on days when there was at least one statement by speakers of the group.
Finally, we ensured that no statement was recorded on days of policy actions by the EU or the ECB (as described below). On such days, there are typically a large number of statements by politicians or
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339 325
central bankers that comment the decision. In order not to confuse the effect of the two types of variables, we decided not to include any statement made on a decision day.
The approach adopted here follows the construction of datasets on communications by central bankers and politicians in Ehrmann and Fratzscher (2007, 2011), and it is consistent with that earlier work. As discussed previously, the related studies by Beetsma et al. (2013), Mink and de Haan (2013) and Mohl and Sondermann (2013) each use different approaches to construct their news variables during the European sovereign debt crisis. We will highlight the most relevant similarities and dif- ferences between our results and those of such other studies.
3.3. Potential determinant 2: actions and decisions by policy makers and rating agencies
The euro exchange rate and its volatility are affected not only by the public debate, but also (and in particular) by decisions and actions by policy makers, as well as by the rating announcements by rating agencies. To cover these, we have constructed the following variables.
A variable covering decisions, actions and events that had large repercussions at the European level. As with the communications variable, we have generated one dummy variable that measures whether an action has taken place on a given day, as well as a “signed” variable, where actions that might have helped overcome the European sovereign debt crisis are coded as þ1, and actions that might have complicated the crisis coded as �1.8 In a similar vein, wecollected and coded all decisions by the ECB.9 All euro area sovereign rating and outlook changes byeach of the three major rating agencies are also part of our database. The coded version takes the value þ1 for improvements in the rating or the outlook, and �1 for deteriorations. The data come from the websites of Standard&Poor's, Moody's and Fitch Ratings.
3.4. Potential determinant 3: macroeconomic news
We also examine the response of the exchange rate to major macroeconomic data releases. How- ever, financial markets should not respond to the component of these announcements that is expected (Kuttner, 2001)10. We therefore construct the unexpected component of macroeconomic data releases as the realized value of the macroeconomic data release on the day of the announcement less the financial market expectation for that value. The data on financial market expectations are the median response in respective polls by Money Market Services among financial market participants. This approach is standard in the literature, the data have been shown to pass standard tests of forecast rationality and provide a reasonable measure of ex-ante expectations of the data release (among others, see Andersen et al., 2003).
Our dataset includes a large set of macroeconomic announcements, including releases of unem- ployment, industrial production, inflation, PMI, trade balance and retail sales for the large countries of the euro area and for the euro area itself, as well as a few other releases that are known to move financial markets, such as the Ifo index for Germany. Of this large battery of announcements, only two turned out to be statistically significant, and remain in our econometric model, namely the releases for German and Italian industrial production data.11
We estimated our models including macroeconomic surprises for the United States, as well as the first principal component of the interest rate differential of the United States, Japan, the United
8 For a detailed exposition of the various EU actions that are covered, see Table A2 in the annex. There are three events that are coded as �1. As these are very different in nature than the other EU decisions, we have tested for robustness of our results to using these three events independently. We find that results are robust.
9 For a detailed exposition of the various ECB actions that are covered, see Table A2 in the annex. 10 Note that we cannot calculate corresponding surprise measures for the actions and the statements, as naturally there are no market surveys for these types of variables. Especially with regard to statements, we would expect that the largest part of them are surprising to markets e even if the view of a certain speaker is known to the public, the mere fact that a speaker feels compelled to make a(nother) statement about the European sovereign debt crisis might be news to the public. 11 This is a common finding in the literature using European macroeconomic announcement data, see e.g. Ehrmann et al. (2011). Even more, Egert and Kocenda (2014) show that financial markets react to fewer announcements during the finan- cial crisis than previously.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339326
Kingdom and Switzerland relative to the euro area, to control for macroeconomic developments abroad. These were not statistically significant, and we therefore dropped them.
3.5. The econometric methodology
As mentioned above, we are interested in the evolution of the exchange rate volatility. A natural econometric methodology for this purpose is to use an ARCH-type model. In more detail, we estimate an exponential GARCH (EGARCH) model, following Nelson (1991). An EGARCH(1,1) model is sufficient to address the non-normality of the data, in particular the serial correlation and heteroskedasticity of the daily exchange rate changes.
The conditional mean equation is formulated as
rt ¼ c1 þ a1 rt�1 þ X i
bs;i si;t þ X j
ba;j aj;t þ X k
bm;k mk;t þ mt; (1)
with rt as the first principal component of the change in the euro exchange rate against the four major currencies and rt-1 as its lagged value.
12 Variables s, a, and m denote the coded variables for statements, actions and macro news surprises, respectively, where s is estimated for i speaker groups (which vary over different estimated models), a is estimated for the EU, the ECB and the rating agencies, and m for German and Italian industrial production. The a and m variables are included in all models. Conditioned on the information set of last period (It�1), we assume the distribution of the disturbance to be mt ��It�1eð0; htÞ. Hence, we express the conditional variance, ht, as
logðhtÞ ¼c2 þ k1 ����� mt�1ffiffiffiffiffiffiffiffiffiffiht�1p
����� � ffiffiffiffiffiffiffiffiffi2=pp !
þ k2
mt�1ffiffiffiffiffiffiffiffiffiffi ht�1
p ! þ k3 logðht�1Þ þ
X i
gs;i s * i;t
þ X j
ga;j a * j;t þ
X k
gm;k m * k;t
(2)
Here, statements, actions and macro news surprises are entered as dummy variables that take the value one on the days of actions or macro releases, and zero otherwise (hence the different notation with the stars). With regard to the statement variables, we work with two variants e the first is the sum of all statements by a certain speaker group on a given day, the second takes the value of one on days when anyone belonging to the respective speaker group made a statement, and zero otherwise. The sum of statements is our preferred measure, as it accounts not only for the occurrence of statements, but also for the intensity of the debate.
The model is estimated via maximum likelihood, using the BHHH and BFGS algorithms for opti- mization. Note that the model is estimated for all business days in the sample, i.e. also for days when neither a statement is recorded, nor a decision is taken, nor macroeconomic news is released. The corresponding variables are equal to zero on such days.
What hypotheses do we entertain? We are interested in the coefficients in the variance equation. The idea that the public debate has instilled volatility in financial markets translates to finding positive g-coefficients. This test can then be extended in various ways, e.g., to see whether certain subsets of statements (depending on their topic, their tone or the speakers) exert different effects, or whether the effects depend on the dispersion of the views expressed.
4. The evolution of the public debate during the European sovereign debt crisis
A first interesting question that can be answered with the help of our database is how the public debate has evolved over time. Fig. 1 plots 20-days moving averages of the number of statements on a
12 Adding further controls, such as day of the week effects, does not affect our results.
-4 -2
0 2
4
0 1
2 3
4 5
01oct2009 01apr2010 01oct2010 01apr2011 01oct2011
Euro exchange rate
-2 0
2 4
0 1
2 3
4 5
01oct2009 01apr2010 01oct2010 01apr2011 01oct2011
Government bond spreads
Fig. 1. The number of statements and financial market developments.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339 327
given day against the first principal component of the euro exchange rate and of the government bond spreads of Greece, Ireland and Portugal against Germany.
Several interesting insights emerge. First, starting with few recorded public statements, there is a clear upward trend as the European sovereign debt crisis unfolds, with a first intensification in spring 2010, when the first rescue package for Greece was agreed, an agreement about the ESM was reached and the ECB started its SMP. After some cooling off, the debate intensified substantially at the end of 2010 and in 2011, coinciding with the rescue package for Portugal and the negotiation of the private sector involvement in the Greek debt restructuring.
The intensity of the debate closely mirrors the severity of the crisis. As shown in Fig. 1, when comparing government bond spreads with the number of statements, there are more statements when spreads are large. At a daily frequency, the correlation coefficient stands at 0.37. At such a high fre- quency, this is a substantial correlation: the correlation coefficient at the monthly frequency increases to 0.71. This suggests that the public debate surrounding the European sovereign debt crisis has been endogenous to its evolution as mirrored by increasing government bond spreads. This, in turn, implies that event studies that measure the effect of public statements on yields or spreads might suffer from problems of endogeneity.
Less of an endogeneity problem arises when studying the reaction of the euro exchange rate and its volatility, as shown in the charts of Fig. 1. The correlation between the euro exchange rate and the number of statements is �0.17, i.e. substantially below the one for government bond spreads. Although we cannot exclude that some statements reacted to exchange rate movements, we consider this rather unlikely, given that the focus of the debate at the time clearly was the evolution of government bond yields.13
Beyond measuring the intensity of the debate via the number of statements, an important dimension is how controversial the debate was. To get at this, we calculate a dispersion measure borrowed from Jansen and de Haan (2006) and Ehrmann and Fratzscher (2007):
Ut ¼ PN�1
i¼1 PN
j¼iþ1 ��si;t � sj;t��
1 =2$ � N2 � D
� (3)
13 We are also comforted by the way results come out. For instance, we find that negative statements by politicians in AAA- rated countries increased exchange rate volatility (without systematically generating exchange rate changes as such). If these communications had been endogenous to exchange rate developments, politicians should have provided positive rather than negative statements in order to calm down markets.
-4 -2
0 2
4
0 .2
.4 .6
.8
01oct2009 01apr2010 01oct2010 01apr2011 01oct2011
Euro exchange rate
-2 0
2 4
0 .2
.4 .6
.8
01oct2009 01apr2010 01oct2010 01apr2011 01oct2011
Government bond spreads
Fig. 2. Dispersion among statements and financial market development.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339328
with N as the number of statements in a given day t, s the statements classified as {�1,0,þ1}, and a dummy D with D ¼ 0 if N is an even number and D ¼ 1 if it is odd. This normalization allows us to obtain a dispersion measure that lies strictly between zero and one, with Ut ¼ 0 if no dispersion is present (i.e., all statements share the same tone) and Ut ¼ 1 if there is a maximum degree of dispersion across statements (for instance a case of two statements, one coded as þ1, the other as �1). Fig. 2 shows that with increasing severity of the crisis, especially as measured by the government bond spreads, dispersion increases substantially e only towards the end of the sample it came down somewhat, from a peak in early 2011.
Finally, it is also revealing to look at the level of dispersion. For the 304 days in our sample when there was more than one statement, the average level of dispersion is 0.52, pointing to a rather contentious debate.
5. Determinants of the euro exchange rate volatility during the European sovereign debt crisis
Having seen how intense and controversial the public debate about the European sovereign debt crisis has been, let us now turn to studying its effects on the euro exchange rate volatility. The results of our first EGARCH estimations are provided in Table 2. For brevity, we only report the results for the variance equation, and refer the interested reader to the working paper version (Ehrmann et al., 2013) for results for the mean equation.
The table contains results from 5 different models, which differ only with regard to the variables on the public statements. Model (1) enters these variables at a rather aggregated level. It differentiates politicians of three country groups, namely those that were AAA-rated throughout our sample (Austria, Finland, France, Germany, Luxembourg and the Netherlands), those that were under stress at some point of our sample (Spain, Ireland, Italy, Greece and Portugal), and all remaining countries (“Other countries”).14
We find that ECB actions have led to a reduction in exchange rate volatility, which we interpret as a sign that ECB actions have helped removing uncertainty and calming markets.15 These results are in
14 The group of AAA-rated countries includes Luxembourg, the prime minister of which was also president of the Eurogroup during the sample studied. As he might have made statements in either one of the two capacities, we have tested whether our results are robust to excluding his statements, and found this to be the case. 15 The economic interpretation is as follows: For example a negative coefficient of �1.4 for the ECB actions means that the residual decreases by 0.5 units (
ffiffiffiffiffiffiffiffiffiffiffi e�1:4
p ) by using the variance equation of the EGARCH model (equation (2) above). This is a
sizable drop given the standard deviation of the mean equation of about 0.4. In addition there are dynamic effects through the autoregressive lags in the variance equation.
Table 2 The effect of statements and actions on the euro exchange rate.
Variance equation (equation (2)) (1) (2) (3) (4) (5)
Main results Split of AAA-rated countries Split of AAA-rated, large vs. small Split of stressed countries Dummy (0,1) version
coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev.
Statements (gs,t) European Central Bank (ECB) �0.063 (0.143) �0.116 (0.160) �0.112 (0.148) �0.056 (0.149) �0.110 (0.176) National Central Bank (NCB) 0.081 (0.133) 0.098 (0.140) 0.100 (0.135) 0.076 (0.134) 0.177 (0.194) all AAA rated countries 0.185** (0.081) e e 0.185** (0.082) 0.378** (0.152) Of which: Austria, Finland,
Luxembourg, Netherlands e 0.307* (0.177) 0.307* (0.175) e e
France e 0.170 (0.219) e e e Germany e 0.128 (0.114) e e e France, Germany e e 0.138 (0.095) e e
Greece, Ireland, Italy, Portugal, Spain �0.194* (0.116) �0.173 (0.127) �0.175 (0.121) e �0.181 (0.116) Of which: Greece, Ireland, Portugal e e e �0.221* (0.133) e
Italy, Spain e e e �0.136 (0.194) e European Union (EU) �0.003 (0.144) �0.014 (0.151) �0.016 (0.148) �0.005 (0.149) �0.034 (0.160) International Monetary Fund (IMF) �0.332 (0.549) �0.373 (0.502) �0.372 (0.502) �0.318 (0.546) �0.307 (0.517) Other countries 0.217 (0.258) 0.217 (0.265) 0.217 (0.263) 0.227 (0.259) 0.278 (0.254) Other speakers �0.520 (0.357) �0.670* (0.368) �0.659* (0.357) �0.528 (0.358) �0.915** (0.454) Actions (ga,j) European Union (EU) 0.710 (0.453) 0.675 (0.441) 0.678 (0.440) 0.712 (0.451) 0.784* (0.456) European Central Bank (ECB) �1.398** (0.711) �1.392* (0.717) �1.393* (0.716) �1.418** (0.710) �1.353* (0.713) Rating agencies �0.288 (0.216) �0.325 (0.223) �0.325 (0.220) �0.277 (0.217) �0.328 (0.216) Macro news (gm,k) Industrial Production, Germany �0.297 (0.442) �0.292 (0.454) �0.283 (0.453) �0.269 (0.445) �0.221 (0.429) Industrial Production, Italy �0.396 (0.330) �0.407 (0.349) �0.401 (0.351) �0.412 (0.352) �0.362 (0.329) EGARCH Constant (c2) 0.878*** (0.190) 0.880*** (0.187) 0.888*** (0.186) 0.884*** (0.189) 0.876*** (0.187) Earch (k2) �0.091 (0.072) �0.104 (0.077) �0.102 (0.076) �0.086 (0.072) �0.092 (0.070) Egarch_a (k1) 0.083 (0.108) 0.103 (0.113) 0.103 (0.112) 0.084 (0.108) 0.081 (0.105) Egarch (k3) �0.216 (0.217) �0.211 (0.208) �0.224 (0.206) �0.229 (0.217) �0.266 (0.202) Log-likelihood �916.0 �914.6 �914.6 �915.9 �915.0 Observations 519 519 519 519 519
Note: The table shows results from EGARCH models for the variance equation (equation (2) of the paper). Benchmark model (1) contains all statements aggregated by speaker groups. Model (2) splits the statements by the politicians from AAA-rated countries into France, Germany and the remaining countries, model (3) into France and Germany on the one hand, and the remaining countries on the other hand. Model (4) splits the politicians from countries under stress. Model (5) re-estimates the benchmark model, aggregating the statement variables into dummy variables (�1, 0,þ1) indicating the balance of views in the mean equation, and {0,1} indicating the occurrence of at least one statement by the speaker group in the variance equation. ***/**/* denote statistical significance at the 1%/5%/10% level.
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line with Kilponen et al. (2012), who find that the ECB's SMP has been successful in stabilizing bond yields, whereas decisions related to the new European economic governance have not been very successful in reducing uncertainty.
Of the various statement variables, there is some volatility-reducing effect triggered by statements of politicians in the euro area countries under stress, but this effect is only weakly statistically sig- nificant, and not consistent across models. What is intriguing, however, is that statements by politi- cians in AAA-rated countries have actually increased the euro exchange rate volatility. The importance of statements by this particular speaker group might be due to market perceptions that politicians of these countries are pivotal to overcoming the crisis, as crisis resolution measures lack credibility without their endorsement. These findings are also in line with Mohl and Sondermann (2013), who show that statements by politicians in AAA-rated countries had a particularly strong impact on gov- ernment bond spreads.
This result raises a number of questions, e.g. whether it is driven by particular speaker groups or whether it depends on the content or the tone of the statement. Of course, the aggregate of AAA-rated countries contains a set of very heterogeneous nations, especially with regard to their size. For that reason, model (2) takes the larger countries of this group, France and Germany, out of the aggregate and includes them separately. Model (3) combines France and Germany in one group, and the remaining countries in another group. Interestingly, the results of model (1) disappear e politicians from France and Germany in isolation or as a country group do not exert the same effects as politicians from all AAA-rated countries together, suggesting that the contributions of the entire country group have mattered for financial markets, rather than those of the large countries within that group.
Model (4) re-groups the AAA-rated countries into one block, and splits the group of countries under stress into those under an EU/IMF adjustment program, namely Greece, Portugal and Ireland, thus leaving Italy and Spain as a separate country group. It turns out that the previous finding of some volatility reduction was due to statements by politicians from the program countries, with statements by Spanish and Italian politicians not being influential on average.
The last model in Table 2, model (5), re-estimates model (1), but replaces the statement variables by a dummy variable (as described above, providing the balance of views expressed in the statements as a {-1, 0, þ1}-variable in the mean equation, and a {0, 1}-dummy indicating whether there has been at least one statement by a speaker in a given group in the variance equation). Most effects are robust, especially the volatility-increasing effect of statements by politicians from AAA-rated countries. One interesting change is that statements by the “other” speaker group now seem to have contributed to lowering volatility, and with large effects.
All models are econometrically well specified. The EGARCH model takes account of hetero- skedasticity in the data. We tested all models for autocorrelation using Cumby and Huizinga (1992) tests and rejected the null hypothesis of autocorrelation in the residuals. The results are also robust to the inclusion of more lags in both the mean and the variance equations (the additional lags were statistically insignificant at conventional confidence levels). We could not reject the null hypothesis that the residuals are normally distributed.
The evidence so far has pointed to an influential role of politicians in AAA-rated countries in affecting the euro exchange rate volatility: unfortunately, these effects have, on average, been increasing volatility, suggesting that they did not contribute to easing market tensions and removing uncertainty. Given the large number of topics that was talked about, it is interesting to split the pre- vious evidence by topic, in order to understand better which parts of the debate have triggered these effects. The corresponding evidence is reported in Table 3.
For parsimony, we only include the statements originating from politicians in the AAA-rated countries. To test whether results are robust to excluding all other statements, model (1) in Table 3 repeats the benchmark model, but without all other speaker groups. Results are extremely robust, justifying further analysis that splits the statements according to topics, as done in model (2) for the standard definition of the statement variables and in model (3) for their dummy version. The volatility- enhancing effects were primarily triggered by statements of the “financial support” category, i.e. by statements about rescue packages to euro area countries and their likelihood and conditions, about a possible default of a country, or about private sector involvement. No other category appears to have exerted significant effects.
Table 3 The effect of statements by AAA-rated countries on the euro exchange rate, by topic.
Variance equation (equation (2)) (1) (2) (3) (4) (5) (6)
All statements By topics By topics, dummies (�1, 0, 1) Positive vs. negative With dispersion Unanimously negative coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev.
Statements (gs,t) All 0.170** (0.066) e e e 0.246*** (0.081) e Of which: Positive e e e 0.151 (0.105) e e
Negative e e e 0.234** (0.094) e e Of which: Positive or disputed
negative e e e e e 0.249** (0.117)
Unanimously negative
e e e e e 1.007*** (0.354)
Of which: Country measures e �0.129 (0.346) �0.020 (0.308) e e e ECB policies e �0.177 (18.416) �0.103 (5.326) e e e EU policies e 0.146 (0.116) 0.157 (0.156) e e e Financial Support e 0.246* (0.128) 0.512*** (0.191) e e e Fiscal reform e 0.051 (0.222) �0.123 (0.252) e e e
All comments e dispersion e e e e �0.548** (0.251) e Actions (ga,j) European Union (EU) 0.719 (0.456) 0.688 (0.449) 0.715 (0.436) 0.731* (0.444) 0.759* (0.449) 0.757** (0.384) European Central Bank (ECB) �1.346* (0.708) �1.412** (0.697) �1.354** (0.674) �1.350** (0.684) �1.264* (0.705) �1.041* (0.630) Rating agencies �0.246 (0.205) �0.229 (0.220) �0.251 (0.203) �0.280 (0.202) �0.261 (0.207) �0.268 (0.168) Macro news (gm,k) Industrial Production, Germany �0.384 (0.414) �0.361 (0.415) �0.175 (0.388) �0.331 (0.415) �0.245 (0.417) �0.124 (0.338) Industrial Production, Italy �0.253 (0.281) �0.309 (0.290) �0.266 (0.288) �0.273 (0.298) �0.301 (0.290) �0.318 (0.292) EGARCH Constant (c2) 0.725*** (0.201) 0.788*** (0.197) 0.933*** (0.171) 0.814*** (0.194) 0.818*** (0.208) 1.051*** (0.151) Egarch (k1) 0.029 (0.102) 0.026 (0.102) 0.054 (0.098) 0.041 (0.100) 0.026 (0.102) 0.095 (0.087) Earch (k2) �0.124* (0.068) �0.106 (0.069) �0.091 (0.065) �0.115* (0.066) �0.107 (0.068) �0.072 (0.062) Egarch (k3) �0.090 (0.251) �0.161 (0.234) �0.414** (0.196) �0.222 (0.235) �0.247 (0.257) �0.575*** (0.130) Log-likelihood �922.9 �922.0 �920.5 �922.0 �921.4 �920.5 Observations 519 519 519 519 519 519
Note: The table shows results from EGARCH models for the variance equation (equation (2) of the paper). Model (1) contains all statements by politicians from AAA-rated countries. Model (2) splits the statements according to topics. Model (3) re-estimates this model, aggregating the statement variables into dummy variables (�1, 0,þ1) indicating the balance of views in the mean equation, and {0, 1} indicating the occurrence of at least one statement by the speaker group in the variance equation. Model (4) splits the statements into positive and negative statements. Model (5) contains all statements by politicians from AAA-rated countries and their dispersion, measured according to equation (4). Model (6) separates days where all speakers agreed on a negative message (“Unanimously negative”) from all other days with statements (“Positive or disputed negative”). ***/**/* denote statistical significance at the 1%/5%/10% level.
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We also test hypotheses related to the direction of the statements and the dispersion among speakers. Model(4)of Table3 splitsthestatementvariableintoonethatcounts the numberofpositive statementson a given day, and another one counting the negative statements. While this does not affect results in the mean equation, the results in the variance equation show that the volatility increase found for statements by politicians in AAA-rated countries stems from negative statements, whereas the sum of positive statements does not generate volatility in a statistically significant fashion. A similar result has been ob- tained by Beetsma et al. (2013) e in their estimations, “bad news” exert upward pressure on domestic and foreign bond yield spreads, and even lead to spillover effects on the yields of the non-stressed countries.
Another hypothesis is tested in model (5) of Table 3, namely whether the disagreement among politicians of the AAA group mattered in how strongly volatility was affected. The effects could poten- tially go both ways: On the one hand, if these speakers are perceived to be the ones that can come to rescue, their agreement should lower uncertainty. On the other hand, given that this group of politicians has often been rather critical of the possible rescue packages, their being unanimously against a certain solution could increase uncertainty about the future course of the European sovereign debt crisis, which in itself could increase volatility. To test this hypothesis, we included the aggregate of all statements by politicians from AAA-rated countries as well as the dispersion measure introduced in Section 4. The interpretation of the coefficients is now straightforward: if dispersion is zero, each statement increases volatility by 0.25, and significantly so; with increasing dispersion, the effect on volatility declines, up to a negative, but insignificant coefficient of �0.30 in the case of complete dispersion.
A natural question that arises is whether agreement among the speakers is generally volatility- enhancing, or whether this is only the case if there is agreement on negative positions. This ques- tion is taken up in model (6), which differentiates days when all speakers agreed on a negative message (by means of a dummy variable that is equal to one when there were at least two statements on a given day, all negative) from all other days with statements (with a dummy variable for days with mixed statements, or with only positive statements). The results are remarkable: on days with unanimously negative statements, volatility is substantially larger than on days without statements, as well as than on days with positive or mixed statements.
To summarize these findings, it is evident that the euro exchange rate volatility is very hard to explain during the crisis. Of the few important factors, decisions and actions at the EU level and by the ECB stand out as having affected exchange rate volatility. In particular the ECB actions have helped reducing volatility. With regard to the public debate, despite the large coverage of our database, it is difficult to find a consistent pattern as to how statements have affected financial markets. The main exception is statements by politicians in AAA-rated countries, which tended to increase volatility, implying that they were not helpful in lowering uncertainty and calming financial markets. For this effect to show up, it is important to take into account the statements by politicians from all AAA-rated countries, which were more influential if they were expressing similar views across speakers, and especially if these views were negative. In particular statements about rescue packages to euro area countries, about a possible default of a country, or about private sector involvement have triggered financial market reactions.
6. Robustness
We have subjected our results to a large battery of robustness tests (see Table 4). The bulk of the tests replace the dependent variable. Models (1) to (3) replace the principal component of the changes in the euro exchange rate against the four major currencies by the spot exchange rate of the euro against the U.S. dollar, the Japanese yen and the British pound, respectively. As can be seen, the major finding that the statements by politicians from AAA-rated countries tend to increase volatility is not necessarily robust e it is present for the Japanese yen, but not for the other two currency pairs. This is not overly surprising, however e as we noted at the outset, when modelling a bilateral exchange rate, it is important to properly account for developments in both economies, even when including “foreign” variables like macroeconomic news and interest rates. The EGARCH model for the Swiss franc had convergence problems, such that results are not provided here.
By contrast, results obtained by computing the principal component also including the exchange rates against the Australian dollar, the Canadian dollar, the Swedish krona and the Norwegian krone are robust (Model 4).
Table 4 Robustness tests.
Variance equation (equation (2))
(1) (2) (3) (4) (5) (6) (7) (8)
US$ JPY GBP PC broad Panel Panel broad Fin. support statements
Adding macro controls
coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev.
Statements (gs,t) European Central Bank (ECB)
�0.047 (0.144) �0.163 (0.127) �0.144 (0.132) �0.140 (0.143) �0.110* (0.060) �0.022 (0.017) �0.190 (0.184) 0.044 (0.156)
National Central Banks (NCB)
0.053 (0.133) 0.105 (0.123) �0.092 (0.141) 0.068 (0.130) 0.052 (0.061) 0.002 (0.021) 0.119 (0.172) 0.003 (0.142)
all AAA-rated countries
0.096 (0.081) 0.169** (0.070) �0.010 (0.086) 0.208** (0.088) 0.105*** (0.033) 0.025*** (0.009) 0.276** (0.137) 0.231** (0.091)
Greece, Ireland, Italy, Portugal, Spain
�0.100 (0.115) �0.353*** (0.114) �0.419*** (0.117) �0.127 (0.098) �0.243*** (0.047) �0.022** (0.010) �0.269** (0.118) �0.279** (0.128)
European Union (EU)
0.086 (0.165) 0.150 (0.137) 0.063 (0.200) �0.008 (0.141) 0.092 (0.069) 0.055** (0.023) 0.086 (0.198) 0.139 (0.166)
International Monetary Fund (IMF)
�0.012 (0.498) �0.283 (0.549) �0.535 (0.537) �0.300 (0.487) �0.089 (0.227) 0.058 (0.050) �0.450 (0.561) �0.337 (0.572)
Other countries 0.084 (0.271) �0.049 (0.249) 0.137 (0.290) 0.320 (0.238) �0.026 (0.123) �0.071** (0.030) 0.050 (0.349) 0.300 (0.290) Others �0.742* (0.407) �0.239 (0.393) 0.264 (0.366) 0.282 (0.353) �0.152 (0.156) �0.030 (0.052) �0.886 (0.627) �0.383 (0.362) Actions (ga,j) European Union (EU)
0.772* (0.445) 0.341 (0.565) 0.621 (0.567) 0.353 (0.387) 0.538** (0.268) 0.171*** (0.062) 0.410 (0.338) 0.767 (0.515)
European Central Bank (ECB)
�0.938 (0.720) �0.477 (0.849) �1.227 (0.867) �1.401** (0.623) �0.387 (0.360) 0.082 (0.096) �1.096* (0.648) �1.502 (0.999)
Rating agencies �0.099 (0.207) �0.042 (0.218) 0.192 (0.201) 0.331** (0.169) �0.025 (0.107) �0.044 (0.029) �0.279 (0.172) �0.224 (0.221) Macro news (gm,k) Industrial Production, Germany
�0.269 (0.392) 0.094 (0.368) 0.040 (0.379) �0.259 (0.318) �0.116 (0.172) �0.132* (0.071) �0.171 (0.342) 0.018 (0.531)
Industrial Production. Italy
�0.395 (0.342) �0.514 (0.358) 0.255 (0.380) 0.283 (0.363) �0.259 (0.206) 0.150** (0.076) 0.015 (0.295) �0.213 (0.440)
Additional macro controls
No No No No No No No Yes
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Table 4 (continued )
Variance equation (equation (2))
(1) (2) (3) (4) (5) (6) (7) (8)
US$ JPY GBP PC broad Panel Panel broad Fin. support statements
Adding macro controls
coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev. coeff. st.dev.
EGARCH Constant (c2) �11.197*** (2.809) �8.211*** (1.523) �14.630*** (1.836) 1.091*** (0.214) �7.363*** (1.084) �0.260*** (0.061) 1.160*** (0.147) 0.610** (0.276) Egarch (k1) �0.076 (0.120) 0.061 (0.136) �0.106 (0.118) 0.256** (0.105) 0.071 (0.061) 0.175*** (0.016) 0.154* (0.092) 0.253** (0.118) Earch (k2) �0.064 (0.074) �0.249*** (0.081) 0.030 (0.078) 0.178*** (0.068) �0.161*** (0.036) �0.019** (0.009) �0.090 (0.066) �0.151* (0.086) Egarch (k3) �0.118 (0.281) 0.140 (0.160) �0.417** (0.175) �0.344** (0.158) 0.255** (0.110) 0.975*** (0.006) �0.596*** (0.130) �0.323*** (0.117) Observations 519 519 519 519 1557 4152 519 515
Note: The table shows results from EGARCH models for the variance equation (2), testing for the robustness of the results of the benchmark model in Table 2. Models (1) to (3) replace the dependent variable by the bilateral spot exchange rate against the U.S. dollar (1), the Japanese yen (2), the British pound (3), the principal component of the changes in the euro exchange rate against the U.S. dollar, the Swiss franc, the Japanese yen, the British pound, the Australian dollar, the Canadian dollar, the Swedish krona and the Norwegian krone (4). Model (5) estimates a panel EGARCH model of the changes in the euro exchange rate against the U.S. dollar, the Swiss franc and the British pound, model (6) against the U.S. dollar, the Swiss franc, the Japanese yen, the British pound, the Australian dollar, the Canadian dollar, the Swedish krona and the Norwegian krone. Model (7) only includes statements in the “financial support” category. Model (8) contains additional macroeconomic news (the mean equation includes also the surprise component contained in the release of industrial production of Spain, France, euro area and the US; gross domestic product of Germany, Italy, and the US; purchasing manager indexes of the euro area, Germany, Spain, France and the US; and for the US trade balance, consumer confidence, non-farm payrolls and housing starts. The variance equation includes those for industrial production of the euro area, France, Spain and the US; gross domestic product of Italy, Spain and the US; purchasing managers indexes of the euro area, Spain, Italy and the US; trade balances of the euro area and France; inflation of the euro area, Germany and the US; unemployment of the euro area; M3 of the euro area; ZEW index of Germany; monetary policy decisions of the euro area; and for the US consumer confidence, retail sales, housing starts and the trade balance). ***/**/* denote statistical significance at the 1%/5%/10% level.
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Models (5) and (6) run panel EGARCH models, where we first include the U.S. dollar, the British pound and the Japanese yen,16 and then expand the set to also include the other currencies. This in- creases the number of observations substantially. While the volatility-dampening effect of ECB actions disappears, all other results are robust e only in the case of the panel EGARCH model with eight currencies do we find other speaker groups to matter, namely the EU officials and the group of poli- ticians from “other” countries.
The next model is estimated using the original dependent variable, but varies the explanatory variables. As we found that statements collected in our “financial support” category affected exchange rate volatility most, model (7) re-estimates the benchmark model, but only including this type of statements. Also here, as usual, we find only the statements by politicians in the AAA-rated countries and in the countries under stress to matter.
The results from a final robustness test are reported in column (8). We included the surprise component contained in the release of many more macroeconomic announcements in the model, both in the mean and in the variance equations. Inclusion of all announcements in our database led to problems in convergence because combinations of variables can lead to multicollinearity problems which make numerical optimization infeasible. In addition to the industrial production of Italy and Germany we included 19 additional variables in the mean and 24 additional variables in the variance equation.17 Importantly, these additional macro controls are statistically insignificant, while our main parameter estimates remain effectively unchanged vis-�a-vis the benchmark results.
As an alternative to studying exchange rate volatility in an EGARCH framework, in the working paper version (Ehrmann et al., 2013), we also report results for 3-month implied volatilities, and show that our main results are robust. In particular, using quantile regressions, we show that statements by politicians in AAA-rated countries tended to increase volatility at times when generally volatility was already high.
These robustness tests confirm the difficulty in explaining the euro exchange rate volatility during the European sovereign debt crisis, which was in large part unaffected by the public debate.
7. Conclusions
The euro exchange rate has been very volatile during the European sovereign debt crisis, and several commentators have argued that part of this volatility has been due to an uncontrolled public debate led primarily by policy makers. In the light of this, the current paper has tested which factors have affected the euro exchange rate volatility over the years 2009e2011, allowing a role for macroeconomic fun- damentals, for policy actions and for the public debate by policy makers.
The paper finds that the euro exchange rate volatility is extremely difficult to explain. Of a large battery of macroeconomic fundamentals, only very few seem to have had an influence on the exchange rate. Actions at the EU level and by the ECB, however, have affected the exchange rate itself as well as its volatility (even though, of course, it should be clear that these actions had not been targeting a change in the exchange rate). In particular ECB actions have contributed to lowering the euro's volatility, suggesting that they have helped reducing economic uncertainty and calming markets.
In order to measure the effects of the public debate, we constructed a unique dataset covering more than 1100 statements by nearly 100 potentially relevant speakers, at the country as well as at the international level. The paper documents how the public debate has intensified and become more controversial in line with the severity of the crisis. Of the various speaker groups, only few are found to have affected the exchange rate volatility. Statements by politicians from AAA-rated countries have in general increased volatility, especially their statements about rescue packages to euro area countries and their likelihood and conditions, about a possible default of a country, or about private sector involvement in case of a default.
The findings of the paper suggest that financial markets might have been less reactive to the public debate by policy makers than previously feared. Still, there are instances where markets reacted with
16 Including also the Swiss franc once again led to convergence problems. 17 The additional variables are listed in the notes to Table 4.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339336
increased volatility, such as on days when several politicians from AAA-rated countries went public with negative statements, suggesting that communication by policy makers in crises times should be cautious about triggering unwanted financial market reactions.
Acknowledgements
We thank Paolo Gambetti for research assistance, an anonymous referee as well as Michael Melvin, Louis Raes and participants at seminars at the ECB, the Bank of Estonia, the International Conference on the Global Financial Crisis: European Financial Markets and Institutions, the 2013 Meeting of the Eu- ropean Public Choice Society, the 2013 EEA-ESEM congress, the 2013 Money, Macro and Finance Research Group Annual Conference, the 2014 ASSA meetings and a workshop at the Bank of Italy for useful comments.
Annex. Examples of statements and their coding
Date: 28.01.2011. Speaker: M. Kiviniemi, PM Finland. Finland PM: not ready for any more European bailouts DAVOS, Switzerland, Jan 28 (Reuters) e
Finland is not ready to join a bailout of any more European countries, Prime Minister Mari Kiviniemi said on Friday, adding that the euro zone's bailout facility had sufficient funds. “We are not ready for any bailouts of the other European countries,” Kiviniemi told Reuters Insider at the World Economic Forum in Davos.
Statement category: EU policies. Coding: �1. Date: 18.06.2010. Speaker: G. Tumpel-Gugerell, ECB Executive Board Member. ECB's Tumpel-Gugerell: Bond buying results good VIENNA, June 18 (Reuters) e The European
Central Bank's bond-buying programme has had good results, Executive Board member Gertrude Tumpel-Gugerell said on Friday.
Statement category: ECB policies. Coding: þ1. Date: 18.06.2010. Speaker: J.-M. Gonzalez-Paramo, ECB Executive Board Member. ECB crisis measures are only temporary- Gonzalez-Paramo FRANKFURT, June 18 (Reuters) e The
European Central Bank's extra crisis-fighting measures cannot remain in place for too long because of the risk to inflation, Executive Board member Jose Manuel Gonzalez-Paramo said on Friday.
Statement category: ECB policies. Coding: �1. Date: 10.12.2009. Speaker: G. Soros. Soros sure Greece won't be allowed to default -Sky LONDON, Dec 10 (Reuters) e Billionaire investor
and philanthropist George Soros said on Thursday he was sure the Greek government would not be allowed to default on its debts despite growing budgetary difficulties and market concerns. “There has to be pressure on Greece to put its house in order but I'm sure that Greece will not be allowed to default. The same applies to the United Kingdom,” Soros told Sky News television.
Statement category: Financial support. Coding: þ1. Date: 30.12.2009. Speaker: W. Sch€auble, Finance Minister, Germany. German FinMin: EU aid for Greece would be misplaced BERLIN, Dec 30 (Reuters) e European Union
countries would show “misplaced solidarity” if they gave financial aid to fellow bloc member Greece, German Finance Minister Wolfgang Schaeuble said in a newspaper interview released on Wednesday. “It would be misplaced solidarity if we were to support Greece with financial help,” Schaeuble told Germany's Boersen Zeitung in an early release of an interview to run in its Thursday edition.
Statement category: Financial support. Coding: �1. Date: 10.06.2010. Speaker: J. L. R. Zapatero, PM Spain. Spain PM sees wide parliamentary support for job reform MADRID, June 10 (Reuters) e Spain's
Prime Minister Jose Luis Rodriguez Zapatero said on Thursday that he was confident that a labour reform would receive majority backing in parliament. “It's going to be a substantial labour reform for
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339 337
our market, and I'm confident it will have majority support in parliament,” Zapatero told reporters on an official visit to Italy.
Statement category: Country measures. Coding: þ1. Date: 12.08.2011. Speaker: M. Rutte, PM the Netherlands. Dutch PM: Greece, Italy economic reform too slow AMSTERDAM, Aug 12 (Reuters) e The Dutch
Prime Minister Mark Rutte said on Friday euro zone countries such as Greece and Italy have not reformed their economies quickly enough to boost growth. “There are too many countries where debt and deficits have run too high. There are too many countries, such as Greece and Italy, where there was either no implementation of reforms to strengthen the growth engines, or it was too late,” Rutte told reporters.
Statement category: Country measures. Coding: �1. Date: 29.12.2009. Speaker: C. Stavrakis, Finance Minister, Cyprus. Cyprus unveiled a fiscal consolidation package NICOSIA, Dec 29 (Reuters) e Cyprus unveiled a fiscal
consolidation package on Tuesday aimed at generating savings and additional revenue of 500 million euros annually to curtail growing deficits, Finance Minister Charilaos Stavrakis said on Tuesday. The package includes changing valuations used to tax real estate – unchanged since 1980, stamping out tax evasion and closer monitoring of a civil service payroll. Authorities will also pursue changes to pension contributions in the public sector, Stavrakis said.
Statement category: Fiscal reform. Coding: þ1. Date: 03.03.2010. Speaker: G. Papandreu, PM Greece. Greek PM says extra measures needed for survival ATHENS, March 3 (Reuters) e Greek Prime
Minister George Papandreou said on Wednesday an extra set of austerity measures decided by the cabinet earlier in the day had been necessary for the debt laden country's survival. “The decisions were not just a choice but a necessity for the survival of our country and our economy,” Papandreou told reporters without giving any details on the measures. (Reporting by Harry Papchristou; Writing by Ingrid Melander).
Statement category: Fiscal reform. Coding: �1.
Table A1 Complete list of included speakers.
Names of speakers
Almunia (EU) Frattini (IT) Merkel (DE) Sarkozy (FR) Ansip (EE) Frieden (LU) Mersch (NCB) Schaeuble (DE) Baroin (FR) Gabriel (DE) Miklos (SK) Silva Pereira (PT) Barroso (EU) Gaspar (PT) Mitterlehner (AT) Socrates (PT) Berlusconi (IT) Honohan (NCB) Napolitano (IT) Soini (FI) Bini Smaghi (ECB) Juncker (EU) Noonan (IE) Soros Bonello (NCB) Katainen (FI) Nowotny (NCB) Stark (ECB) Bos (NL) Kazamias (CY) Noyer (NCB) Stavrakis (GR) Bruederle (DE) Kees de Jager (NL) Ordonez (NCB) Strauss-Kahn (IMF) Buffett Kenny (IE) Orphanides (NCB) Teixeira (PT) Campa (ES) Kiviniemi (FI) Papaconstantinou (GR) Tremonti (IT) Cavaco Silva (PT) Knot (NCB) Papademos (ECB) Trichet (ECB) Coelho (PT) Koehler (DE) Papandreou (GR) Tumpel (ECB) Coene (NCB) Kranjec (NCB) Paramo (ECB) Urpilainen (FI) Constancio (ECB) Lagarde (FR) Provopoulos (NCB) van Rompuy (EU) Costa (NCB) Lagarde (IMF) Quaden (NCB) Vanhanen (FI) Cowen (IE) Leite (PT) Radicova (SK) Venizelos (GR) da Silva (PT) Lenihan (IE) Rajoy (ES) Weber (NCB) Draghi (NCB) Leterme (BE) Rehn (EU) Weidmann (NCB) El Erian (PIMCO) Ligi (EE) Reynders (BE) Wellink (NCB) Faymann (AT) Liikanen (NCB) Roesler (DE) Westerwelle (DE) Fekter (AT) Lipsky (IMF) Rutte (NL) Wulff (DE) Fico (SK) Lipstok (NCB) Salgado (ES) Zapatero (ES) Fillon (FR) Makuch (NCB) Samaras (GR)
Note: The table shows the names of the speakers covered in our dataset, along with their affiliation in brackets.
Table A2 Overview of EU actions and events and ECB actions.
Date Description Coded
EU actions and events 25/03/2010 Euro area Heads of State agree to offer, together with the IMF,
financial support to Greece in the form of coordinated bilateral loans 1
03/05/2010 Announcement of an economic adjustment programme for Greece 1 10/05/2010 Agreement on the European Stabilisation Mechanism (ESM) 1 29/11/2010 Announcement of an economic adjustment programme for Ireland 1 07/12/2010 Decision on financial assistance to Ireland 1 14/02/2011 Agreement about ESM lending capacity of V500 bn 1 24/03/2011 European Council agrees on the Euro Plus pact 1 16/05/2011 Official approval of the V78 bn bailout package for Portugal 1 17/06/2011 Increase in effective lending capacity of EFSF to V440 bn 1 20/06/2011 Finance ministers agree to broaden the EFSF mandate 1 04/07/2011 Decision to disburse the fifth tranche of the Greek rescue package (V12 bn) 1 22/07/2011 Agreement about V109 bn of new funds for the Greek
package and a private sector involvement of 21% 1
02/09/2011 The 5th EU/IMF/ECB Review Mission to Greece has left Athens unexpectedly �1 09/09/2011 Jürgen Stark resigns from the ECB's Executive Board �1 27/10/2011 Restructuring of the second rescue package for Greece: increase
in financing to V130 bn and in private sector involvement to 50% 1
01/11/2011 Greek PM Papandreou announced his intention to hold a referendum over the rescue package, including the 50% private haircut
�1
08/11/2011 “Six-pack” approved by the European Council 1 ECB actions 27/01/2010 Discontinuation of temporary swap lines with the Federal Reserve �1 10/05/2010 Measures to address tensions in financial markets, including the
Securities Market Programme, fixed-rate tender procedure with full allotment in the regular 3-months LTROs, a 6-month LTRO with full allotment, reactivation of temporary swap lines with the Federal Reserve
1
17/12/2010 Swap facility agreement with the Bank of England 1 21/12/2010 Extension of the swap agreements with the Federal Reserve until 1 August 2011 1 07/04/2011 Increase in policy interest rates by 25 bps �1 29/06/2011 Extension of the swap agreements with the Federal Reserve until 1 August 2012 1 07/07/2011 Increase in policy interest rates by 25 bps and suspension of the minimum
credit rating threshold for collateral eligibility applied to instruments issued or guaranteed by the Portuguese government
�1
25/08/2011 Extension of liquidity swap arrangement with the Bank of England up to 28 Sep 2012 1 15/09/2011 Decision to conduct three additional operations providing USD liquidity in
the form of fixed-rate tenders with full allotment 1
Note: The table shows the EU actions and events and the ECB actions covered in the corresponding variable, along with their coding.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339338
References
Andersen, T., Bollerslev, T., Diebold, F., Vega, C., 2003. Micro effects of macro announcements: real-time price discovery in foreign exchange. Am. Econ. Rev. 391, 38e62.
Beetsma, R., Giuliodori, M., de Jong, F., Widijanto, D., 2013. Spread the news: the impact of news on the European sovereign bond markets during the crisis. J. Int. Money Finance 34, 83e101.
Conrad, C., Lamla, M., 2010. The high-frequency response of the EUR-USD exchange rate to ECB communication. J. Money Credit Bank. 42 (7), 1391e1417.
Cumby, R.E., Huizinga, J., 1992. Testing the autocorrelation structure of disturbances in ordinary least squares and instrumental variables regressions. Econometrica 60 (1), 185e195.
Dewachter, H., Erdemlioglu, D., Gnabo, J.-Y., Lecourt, C., 2014. The intra-day impact of communication on euro-dollar volatility and jumps. J. Int. Money Finance 43, 131e154.
Egert, B., Kocenda, E., 2014. The impact of macro news and central bank communication on emerging European forex markets. Econ. Syst. 38 (1), 73e88.
Ehrmann, M., Fratzscher, M., 2005. Exchange rates and fundamentals: new evidence from real-time data. J. Int. Money Finance 24 (2), 317e341.
Ehrmann, M., Fratzscher, M., 2007. Communication and decision-making by central bank committees: different strategies, same effectiveness? J. Money, Credit Bank. 39, 509e541.
Ehrmann, M., Fratzscher, M., 2011. Politics and monetary policy. Rev. Econ. Stat. 93, 941e960. Ehrmann, M., Fratzscher, M., Gürkaynak, R.S., Swanson, E.T., 2011. Convergence and anchoring of yield curves in the euro area.
Rev. Econ. Stat. 93 (1), 350e364.
M. Ehrmann et al. / Journal of International Money and Finance 49 (2014) 319e339 339
Ehrmann, M., Osbat, C., Str�aský, J., Uusküla, L., 2013. The Euro Exchange Rate during the European Sovereign Debt Crisis e Dancing to its Own Tune?. ECB Working Paper No. 1532.
Engel, C., Mark, N.C., West, K.D., 2014. Factor model forecasts of exchange rates. Econometric Reviews, in press. Faust, J., Rogers, J.H., Wang, S.B., Wright, J.H., 2007. The high-frequency response of exchange rates and interest rates to
macroeconomic announcements. J. Monetary Econ. 54 (4), 1051e1068. Fratzscher, M., 2006. On the long-term effectiveness of exchange rate communication and interventions. J. Int. Money Finance
25 (1), 146e167. Fratzscher, M., 2009. What explains global exchange rate movements during the financial crisis? J. Int. Money Finance 28 (8),
1390e1407. Holsti, O.R., 1969. Content Analysis for Social Sciences and Humanities. Addison-Wesley, Reading. Jansen, D.-J., de Haan, J., 2005. Talking heads: the effects of ECB statements on the euro-dollar exchange rate. J. Int. Money
Finance 24 (2), 343e361. Jansen, D.-J., de Haan, J., 2006. Look who’s talking: ECB communication during the first years of EMU. Int. J. Finance Econ. 11,
219e228. Johnson, G., Schneeweis, T., 1994. Jump-diffusion processes in the foreign exchange rate markets and the release of macro-
economic news. Comput. Econ. 7 (4), 309e329. Kilponen, J., Laakkonen, H., Vilmunen, J., 2012. Sovereign Risk, European Crisis Resolution Policies and Bond Yields. Bank of
Finland Working Paper No. 22. Kim, S.-J., 1998. Do Australian and the U.S. macroeconomic news announcements affect the USD/AUD exchange rate? Some
evidence from E-GARCH estimations. J. Multinatl. Financial Manag. 8 (2e3), 233e248. Kim, S.-J., 1999. Do macro-economic news announcements affect the volatility of foreign exchange rates? Some evidence from
Australia. Appl. Econ. 31 (12), 1511e1521. Kuttner, K., 2001. Monetary policy surprises and interest rates: evidence from the Fed funds futures market. J. Monetary Econ.
47 (3), 523e544. Melvin, M., Saborowski, C., Sager, M., Taylor, M.P., 2009. Bank of England interest rate announcements and the foreign exchange
market. Int. J. Central Bank. 6 (3), 211e247. Mink, M., de Haan, J., 2013. Contagion during the Greek sovereign debt crisis. J. Int. Money Finance 34, 102e113. Mohl, P., Sondermann, D., 2013. Has political communication during the crisis impacted sovereign bond spreads in the euro
area? Appl. Econ. Lett. 20 (1), 48e61. Nelson, D.B., 1991. Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59 (2), 347e370. Sager, M.J., Taylor, M.P., 2004. The impact of ECB Governing Council announcements on the foreign exchange market: a
microstructural analysis. J. Int. Money Finance 23 (7e8), 1043e1051. Smeets, D., Zimmermann, M., 2013. Did the EU summits succeed in convincing the markets during the recent crisis? J. Common
Mark. Stud. 51 (6), 1158e1177.
- The euro exchange rate during the European sovereign debt crisis – Dancing to its own tune?
- 1. Introduction
- 2. Literature review
- 3. Data and methodology
- 3.1. The volatility of the euro exchange rate
- 3.2. Potential determinant 1: the public debate about the European sovereign debt crisis
- 3.3. Potential determinant 2: actions and decisions by policy makers and rating agencies
- 3.4. Potential determinant 3: macroeconomic news
- 3.5. The econometric methodology
- 4. The evolution of the public debate during the European sovereign debt crisis
- 5. Determinants of the euro exchange rate volatility during the European sovereign debt crisis
- 6. Robustness
- 7. Conclusions
- Acknowledgements
- Annex. Examples of statements and their coding
- References
