metadata: authors: 0: author: Hacihasanoglu, Erk
1: author: Rostom,Ahmed Mohamed Tawfick
2: author: Sensoy, Ahmet
count: Europe
docna: 0: docna: European economic and monetary union
            sovereign debt markets
docty: Policy Research Working Paper
subtopic: Disability,Employment and Unemployment,Macroeconomic Management,Financial Sector Policy
teratopic: International Economics and Trade,Finance and Financial Sector Development
abstracts: cdata!: This paper focuses on developments in
            the European Economic and Monetary Union sovereign debt
            markets in the past decade. The ?rst part analyzes the
            integration and segmentation structure of the bond markets
            of the Economic and Monetary Union before and after the
            sovereign debt crisis, by introducing the novel concept of
            correlation-based stable networks. Accordingly, a fair
            integration is observed between the bond markets during the
            pre-crisis period. However, a strict segmentation emerges,
            separating the members struggling with debt problems and the
            ones with relatively strong ?scal performances during the
            sovereign debt turmoil. The segmentation structure is
            clearly visualized, revealing the potential paths for crisis
            and recovery transmission in the future. In the second part,
            the paper comments on the recent decreasing trend in
            Economic and Monetary Union member bond yields and their
            increasing degree of co-movement. Accordingly, the paper
            argues that these changes do not depend on the ?scal
            performances of the member countries, but depend on the
            illusion of quality that appeared with the Fed (U.S. Federal
            Reserve) tapering signals in early 2013.
repnme: repnme: European economic and monetary union
            sovereign debt markets
display_title: European economic and monetary union
            sovereign debt markets
listing_relative_url: /research/2015/01/23170489/european-economic-monetary-union-sovereign-debt-markets
topicv3: Monetary Policy m1327609 444,Financial Sector Development m1327681 1920,Unemployment m1504051 2747,Blindness m1582294 4218
publishtoextweb_dt: 2015-01-06T00:00:00Z
historic_topic: International Economics and Trade,Finance and Financial Sector Development
no_of_pages: 27
author_keys: 0: author_key: 1092295
model_extractions: WPS7149 Policy Research Working Paper 7149 European Economic and Monetary Union Sovereign Debt Markets _Ahmet Sensoy Erk Hacihasanoglu Ahmed Rostom_ Finance and Markets Global Practice Group January 2015
Policy Research Working Paper 7149

# **Abstract**

This paper focuses on developments in the European Economic and Monetary Union sovereign debt markets in the past decade. The first part analyzes the integration and segmentation structure of the bond markets of the Economic and Monetary Union before and after the sovereign debt crisis, by introducing the novel concept of correlation-based stable networks. Accordingly, a fair integration is observed between the bond markets during the pre-crisis period. However, a strict segmentation emerges, separating the members struggling with debt problems and the ones with relatively strong fiscal performances during the sovereign debt turmoil. The segmentation structure is clearly visualized, revealing the potential paths for crisis and recovery transmission in the future. In the second part, the paper comments on the recent decreasing trend in Economic and Monetary Union member bond yields and their increasing degree of co-movement. Accordingly, the paper argues that these changes do not depend on the fiscal performances of the member countries, but depend on the illusion of quality that appeared with the Fed (U.S. Federal Reserve) tapering signals in early 2013.

This paper is a product of the Finance and Markets Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at arostom@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **European Economic and Monetary Union Sovereign Debt Markets**

## **Ahmet Sensoy (a), Erk Hacihasanoglu (b), Ahmed Rostom (c)**

- _(a) Specialist, Research & Business Development Department, Borsa Istanbul_

- _(b) Group Manager, Research & Business Development Department, Index & Data Department, Borsa Istanbul_

- _(c) Financial Sector Specialist, Finance and Market Global Practice, The World Bank_

## **JEL Classification: D08, E44, G02, G12,**

**Keywords: Sovereign Debt, European Economic and Monetary Union, Network Analysis, Monetary Policy, Tapering.**
# **1. Introduction**

The creation of the European Economic and Monetary Union (EMU) in January 1999 and the launch of the single currency “euro” led to a convergence of government bond yields of eurozone countries, with remaining yield spreads being mainly attributed to differences in the levels of idiosyncratic credit and liquidity risks. A byproduct was that not only the yields converged, but an overall increase in co-movements in euro area bond markets was also observed, suggesting a high degree of integration (Ehrmann et al., 2011). Moreover, unemployment and inflation have fallen below pre-EMU levels in most participating countries (Gruner, 2010). In brief, EMU was a big success story and it performed much better than many economists and academics predicted.<sup>1</sup> However, after 10 years of stability, the collapse of Lehman Brothers triggered severe tensions in financial markets worldwide, including the eurozone bond market. The financial turmoil turned into a global financial crisis which directed attention to the macroeconomic and fiscal imbalances within EMU countries. During the period of stability, markets seemed to turn a blind eye to the possibility that governments might default (Beirne and Fratscher, 2013). As a consequence, the low yield spreads of government bonds issued by the member states of the EMU were no longer observed anymore (Sibbertsen et al., 2014), moreover their co-movement degree was rapidly decreasing. In fact, creditors have started to distinguish clearly between the different member states of the euro area (see Fig. 1). Eventually, the global financial crisis turned into a major sovereign debt crisis. Since then, European policymakers have been consistently taking measurements; although, whether these measurements are

> 1For example, see Gruner and Hefeker (1999); Cukierman and Lippi (1999) for the papers with negative foresights on the performance of EMU prior to its introduction.

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failure or success is a controversial subject.

In this study, we first try to display how investors have distinguished between the different EMU members during the sovereign debt crisis. In particular, by introducing the concept of _“correlation based stable networks”_ , we demonstrate the integration and segmentation structure of EMU bond markets before and after the debt crisis. Then, we reveal the potential paths for crisis/recovery transmission in the future. In the second part, we comment on a recent development in the behavior of EMU sovereign bond yields: Interestingly, since early 2013, bond yields of the struggling EMU members started to experience similar large decreasing trends and all EMU bond yields again tended to co-move in a great harmony. We focus on the co-movement part and ask the following questions: (i) why do we observe such a change? and (ii) what happens next?

In the rest, Section 2 presents the data and the methodologies used in this paper. Section 3 displays the results and finally, Section 4 concludes.

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# **2. Data and Methodology**

In order to carry out our analysis, we use daily 10-year benchmark government bond yields (obtained from Thomson Reuters datastream) for a sample of eleven countries; France, Germany, Italy, Belgium, Netherlands, Ireland, Spain, Portugal, Austria, Finland and Greece. The analysis spans a time period ranging from March 1, 2002 (end of dual circulation period in Greece) until May 2, 2014.

<!-- Start of picture text --> EMU<br>France<br>35<br>Germany<br>Italy<br>30<br>Belgium<br>Netherlands<br>25<br>Ireland<br>Spain<br>20 Portugal<br>Austria<br>15 Finland<br>Greece<br>10<br>5<br>Jun.02 May.04 May.06 May.08 Apr.10 Apr.12 Apr.14<br>10Y Benchmark Yield (points)<br><!-- End of picture text --> Figure 1: EMU sovereign bond yields from March 2002 to May 2014 Bond yield convergence in EMU is validated by Fig. 1 for the first 10 years of the eurozone. However, it does not tell much about the exact timing and the exact amount of yield co-movements. For that purpose, we apply the consistent dynamic conditional correlation model (cDCC) of Aielli (2013) to track the timevarying co-movement degree. The methodology is explained in the following sub-section.

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# _2.1. Consistent dynamic conditional correlation_

The dynamic correlations are estimated by the cDCC. At first, we take the daily changes in bond yields (BY) as the first differences for each member country _i_ i.e.

For a precise model estimation, we take out the days when more than half of the markets are closed. Moreover, the highest and lowest five daily changes of each market are removed from the dataset to get rid of outliers.

To remove the serial correlation effects (which is present in all daily changes) and obtain the zero mean residuals, we first estimate the following mean equation where _rt_ = [ _r_ 1 _,t, ..., rn,t_ ]<sup>_′_</sup> is the vector of _n_ bond yield daily changes, _μ_ is a vector of constants with length _n_ , _φ_ is the coefficient vector corresponding to autoregressive terms and _εt_ = [ _ε_ 1 _,t, ..., εn,t_ ]<sup>_′_</sup> is the vector of residuals.

In the next step, we obtain the conditional volatilities _hi,t_ from the univariate FIAPARCH(1, _d_ ,1) model for an extended flexibility. In particular, we estimate the following where _ω ∈_ (0 _, ∞_ ), _|β|_ and _|φ| <_ 1, 0 _≤ d ≤_ 1, _γ_ is the leverage coefficient and _δ_ is the parameter for the power term that takes finite positive values, while (1 _− L_ )<sup>_d_</sup> is the financial differencing operator expressed in terms of a hypergeometric function (see Conrad et al. (2008) for the expression of this function).

To consider cDCC modeling, we start by reviewing the DCC model of Engle (2002). Assume that _Et−_ 1[ _εt_ ] = 0 and _Et−_ 1[ _εtε_<sup>_′_</sup> _t_<sup>]=</sup><sup>_Ht_,where</sup><sup>_Et_[</sup><sup>_·_]isthe</sup> 4
conditional expectation on _εt, εt−_ 1 _, ..._ . The asset conditional covariance matrix _Ht_ can be written as where _Rt_ = [ _ρij,t_ ] is the asset conditional correlation matrix and the diagonal matrix of the asset conditional variances is given by _Dt_ = _diag_ ( _h_ 1 _,t, ..., hn,t_ ). Engle (2002) models the right hand side of Eq.(3) rather than _Ht_ directly and proposes the dynamic correlation structure where _Qt ≡_ [ _qij,t_ ], _ut_ = [ _u_ 1 _,t, ..., un,t_ ]<sup>_′_</sup> and _ui,t_ is the transformed residuals i.e. _ui,t_ = _εi,t/hi,t_ , _S ≡_ [ _sij_ ] = _E_ [ _utu_<sup>_′_</sup> _t_<sup>]isthe</sup><sup>_n × n_unconditionalcovariancematrix</sup> of _ut_ , _Q_<sup>_∗_</sup> _t_<sup>=</sup><sup>_diag{Qt}_and</sup><sup>_a, b_are non-negative scalars satisfying</sup><sup>_a_+</sup><sup>_b <_1.The</sup> resulting model is called DCC.

However, Aielli (2013) shows that the estimation of _Q_ by this way is inconsistent since _E_ [ _Rt_ ] = _E_ [ _Qt_ ] and he proposes the following consistent model with the correlation driving process where _S_ is the unconditional covariance matrix of _Q_<sup>_∗_</sup> _t_<sup>1</sup><sup>_/_2</sup> _ut_ .

Finally, to consider the time varying group correlation of EMU bond yields, we take the average cDCC i.e.

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# _2.2. Correlation based stable networks_

If we consider our methodological approach as consisting of two main parts, the first one would be obtaining the dynamic conditional correlation matrix _Rt_ . Following that, the second part is the analysis of networks constructed by the time-varying correlations. During recent years, networks have proven to be an efficient way to characterize and investigate a wide range of complex financial systems including stock, bond, commodity, foreign exchange and interbank lending markets.<sup>2</sup> Similarly, a correlation based network could be very useful in understanding the integration and segmentation structure of EMU bond markets in our case. Such an approach has not been used in the relevant literature before and we expect it to provide noteworthy implications regarding the subject. To be able to follow our approach, we first need to give some introductory context: Suppose that an undirected and unweighted network _Nt_ evolves in time and includes at most _k_ nodes from the set _{n_ 1 _, n_ 2 _, . . . , nk}_ on any given time step _t_ . At that time _t_ , let some (or all) of the nodes in the network be connected to each other according to some _t_ -dependent criterion. As easily understood, in this construction, the nodes included in the network and the edges connecting these nodes need not be stable and are subject to change in time. Now, we introduce the following genuine definitions.

**Definition 1:** Let _Nt_ be a dynamic network described as above. Let _eij_ be an edge connecting specific nodes _ni_ and _nj_ and time variable _t_ spans the set _{t_ 1 _, t_ 2 _, . . . , tm}_ . Suppose _eij_ appears in the network _s_ out of _m_ times. Then

> 2For example, see Iori et al. (2008); Tola et al. (2008); Tumminello et al. (2010); Minoiu and Reyes (2013) for some of the noteworthy studies in recent years.

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if 1 _≥ ms_<sup>_≥p>_0,</sup><sup>_eij_iscalleda</sup><sup>_p_</sup><sup>**_-stableedge_**or</sup><sup>_p_</sup><sup>**_-stableconnection_**.3</sup> A network _M_ consisting of only _p_ -stable connections of _Nt_ is called _p_ **_-stable network_** of _Nt_ .

**Definition 2:** Let _Rt_ be the cDCC matrix defined in Section 2.1. At time _t_ , let _<u>ρ</u>_ <u>(</u> _t_ ) be the mean of the lower triangular part of _Rt_ , and _σ_ ( _t_ ) be its standard deviation. A correlation level _ρij_ ( _t_ ) _∈ Rt_ is called _c_ **_-strong_** if _ρij_ ( _t_ ) _≥_ _<u>ρ</u>_ <u>(</u> _t_ ) + _c · σ_ ( _t_ ) where the constant _c ≥_ 0.<sup>4</sup><sup>_,_5</sup>

Then our approach is as follows: For a pre-determined strength level _c_ , we construct a dynamic network _Nt_ consisting of nodes connected by only _c_ -strong correlations at time _t_ , where nodes represent the sample countries. Next, for the considered time period, we construct _M_ ; the _p_ -stable network of _Nt_ . For relatively high _p_ values, we can intuitively state that members connected in _M_ are integrated. As _c_ -level is chosen higher, this integration degree gets stronger.

In the following section, we will study the integration/segmentation structure of the EMU bond markets by analyzing the _p_ -stable connections. For the sake of simplicity in our setup, we introduce the following definition which will be our main focus throughout the rest of this study.

> 3It is clear that every _p_ 1-stable connection is also _p_ 2-stable for any _p_ 2 _≤ p_ 1 _≤_ 1.

> 4It would be naive to choose a fixed threshold level to determine if a correlation value is strong or not. Several studies in the literature have shown that correlations are timevarying and tend to increase in turbulent times. Therefore, a fixed choice would most likely introduce a bias depending on the global conditions. With our model, the threshold level is determined endogenously and updated everyday. Thus, possible bias arising due to changing global conditions is minimized.

> 5When _c <_ 0, _c_ -strong correlation levels become below the average. In order to consider a reasonable strength concept for correlations, minimum _c_ should be taken as 0.

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**Definition 3:** A network _M_ is called **_simply-stable_** , if it only consists of _p_ -stable connections of _Nt_ obtained from _c_ -strong correlations where _c_ = 0 and _p_ = 0 _._ 5.

This last definition associates the concept of stability with the observation of an average event at least half of the time which is, indeed, intuitive. (For future studies, the third definition can later be diversified as **_super-stable_** by imposing stronger connectivity and stability conditions.) 8
# **3. Results**

Fig. 2 displays the average dynamic correlation between euro area bond yields. Accordingly, the early years of the EMU exhibit an almost perfect bond market integration with an average correlation estimate close to one, an indicator of a gain in importance of aggregated European factors over local countryspecific ones. However, things change abruptly in the following years: In Fig. 2, we present two critical dates for the bond market dynamics in EMU. The first one is September 15, 2008, the date when Lehman Brothers collapsed. According to Fig. 1, this event does not have a colossal impact on government bond yields relative to the German Bund, however Fig. 2 clearly shows that the almost perfect correlation among EMU sovereign bond yields starts to deteriorate. The second date is November 5, 2009, the date when the Greek government revealed a revised budget deficit of 12.7% of GDP for 2009, which was double the previous estimate. This time, not only the average correlation deteriorates but also the sovereign yields and spreads relative to the German Bund rise sharply for most of the euro area countries.<sup>6</sup>

# _3.1. Network analysis_

To see the effects of the sovereign debt crisis on the integration/segmentation structure of EMU bond markets, we need to split our time interval as _Phase 1_ and _Phase 2_ . In this step, we use the aforementioned two critical dates as splitters: Phase 1 is taken as the time interval between March 1, 2002 and September 14, 2008 (6.5 years), whereas Phase 2 covers from November 6, 2009 to May 2, 2014 (4.6 years).

In the following, Fig. 3 displays the simply stable networks in Phase 1 and

> 6Estimated model parameters can be found in Appendix A.

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<!-- Start of picture text --> EMU<br>1<br>Greece revised<br>budget deficit<br>(05/11/2009)<br>0.8<br>PHASE 2<br>PHASE 1<br>0.6<br>Lehman<br>collapse<br>(15/09/2008)<br>0.4<br>0.2<br>Jun.02 May.04 May.06 May.08 Apr.10 Apr.12 Apr.14<br>Average cDCC<br><!-- End of picture text --> Figure 2: Average dynamic conditional correlation between EMU sovereign bond yields. Vertical lines denote two specific critical dates for the region.

Phase 2.<sup>7</sup> In Phase 1, a high degree of integration is observed between EMU sovereign debt markets, i.e. almost every member is connected to another. However, strict segmentation is clearly visualized in Phase 2. There are two main clusters where in one of them, the members struggling with debt problems (denoted in red) are present. In the other cluster, we observe the countries with strong fiscal positions during the sovereign debt crisis. Moreover, Belgium, which is one of the two members having relative weak fiscal performances within this cluster (denoted in yellow), serves as a hub in the new segmented structure. Interestingly, Greece is not connected to any other member in Phase 2 which reveals that its debt market no longer belongs to the same asset class with others and currently stands as an outlier in the EMU. Interestingly, simplystable networks show that the decrease in the overall correlation level in Phase 2 is not due to a general sparsity of EMU members. It is more likely to be

> 7The size of the circles in the figures do not have any economic interpretation and are chosen for optimal allocation purposes when constructing networks.

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<!-- Start of picture text --> sub-figure).<br>Ireland Portugal<br>(lower<br>2<br>Greece<br>Phase<br>Spain and<br>Italy Spain<br>sub-figure)<br>Finland<br>Belgium Italy<br>Ireland (upper<br>Austria 1<br>France Belgium Phase<br>in<br>Netherlands<br>Germany France networks<br>Netherlands<br>Portugal<br>Austria<br>Germany Simply-stable<br>Finland<br>3:<br>Figure<br><!-- End of picture text --> 11
caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement.

Fig. 3 shows another important point: In our setup, two main clusters are connected through Belgium in Phase 2. Accordingly, if the sovereign debt crisis worsens in the future, the next target of this crisis is most likely Belgium; i.e., the spillover effect will take place through this country first. However, from another point of view, if a recovery is to be observed, it will most likely happen by the convergence of bond yields of struggling members to the yields of Belgium. Therefore, Belgium’s sovereign debt market plays a crucial role in monitoring the systemic risk in EMU, and should be tracked by investors and policymakers.

# _3.2. Path to recovery?_

In the previous section, we have given two critical dates for the EMU sovereign debt markets. In this part, we present two additional dates. Starting in 2010 and onwards, bond markets of Greece, Ireland and Portugal and then Spain and Italy had been under intense pressure. Accordingly, the first additional critical date is April 7, 2011, the date when Portugal requested immediate assistance from the European Financial Stability Facility (EFSF) and also the date when the European Central Bank (ECB) increased interest rates by 25 basis points; a very controversial decision considering the euro area’s status at that time. After this event, the spreads between the EMU sovereign bonds even expanded and the average correlation hit bottom, hovering around the 0.2 level.

Later, ECB took policy decisions such as lowering interest rates to stimulate economic activities and local policymakers promised structural reforms in these countries. Although these reactions seem to have been effective on spreads (see Fig. 1), Fig. 4 reveals that they are not enough to force EMU bond yields to act as a single asset class as in the pre-Lehman era.

However, starting with 2013, we observe a significant increase in the EMU 12
<!-- Start of picture text --> EMU<br>1<br>Greece<br>revised budget<br>deficit<br>0.8<br>Release of Fed<br>minutes on<br>Dec 2012<br>0.6 meeting<br>Lehman<br>collapse<br>0.4<br>ECB rates<br>up 25 bps<br>+<br>Portugal needs<br>0.2 assistance<br>Jun.02 May.04 May.06 May.08 Apr.10 Apr.12 Apr.14<br>1<br>18.01.2010<br>0.8<br>22.05.2013<br>15.06.2011<br>0.6<br>0.4<br>11.11.2008<br>0.2<br>Jun.02 May.04 May.06 May.08 Apr.10 Apr.12 Apr.14<br>Average cDCC<br><!-- End of picture text --> Figure 4: Average dynamic conditional correlation between EMU sovereign bond yields. Vertical lines in the upper sub-figure denote the dates when noteworthy economic incidents occur. Vertical lines in the lower sub-figure denote the dates when dynamic correlation shifts to another mean level according to penalized contrasts.

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bond market co-movements with a decreasing trend in their yields (see Fig. 1 and 4). Although we can not say it formally, this situation seems to draw a picture of going back to the pre-Lehman structure. Well, the question to be asked here is that do these changes depend on any fundamentals?

Recent studies show that in the eurozone, fiscal rules, debt, deficits, and debt-service ratios play the main roles in determining the sovereign spreads (Schuknecht et al., 2009; Fatas, 2010; von Hagen et al., 2011; Bernoth et al., 2012; Iara and Wolff, 2014). Therefore, if aforementioned significant changes in sovereign bond yield trends and correlation dynamics are to be observed starting with 2013, then we would expect at least a recovery or signs of recovery in terms of fiscal performances of EMU members prior to 2013; however this is not the case here.

Then, we should ask ourselves why we may have observed such a change? The date of change (second additional critical date) corresponds to Jan 3, 2013, the date which the minutes of the Fed meeting on December 12-13, 2012, were released. The importance of the meeting was that for the first time, there was some disagreement on how long the Fed should keep buying bonds.<sup>8</sup> Indeed, the so called “Fed tapering” operation that was formally announced by Bernanke on May 22, 2013, seems to be uttered in this meeting.

Although we do not aim to technically prove it, one of the claims of this study is that the public reveal of Fed tapering possibility has the major impact on the changes we consider in EMU bond markets, and this impact is transmitted through the mechanism of “flight-to-quality”: When differences in government bond yields have sharply increased in the euro area for the first time, the main explanation was that the market players have been shaken through a wake-up

> 8 `http://news.yahoo.com/fed-minutes-show-concerns-bond-190130672.html` .

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call triggered by the global financial crisis and started to distinguish between the different EMU members (Beber et al., 2009). Now, in the case of Fed tapering and the vanishing excess liquidity environment, the flight-to-quality mechanism again seems to be in the works, however this time, the address of quality is the developed markets worldwide, whereas flight is from emerging countries.<sup>9</sup> The problem is that in the previous case, low and high quality addresses were mainly based on solid fiscal performances (Schuknecht et al., 2009; von Hagen et al., 2011; Bernoth et al., 2012; Iara and Wolff, 2014). However in the current case, emerging countries, where the flight takes off, are better off in terms of these fiscal performances in general, compared to the target addresses i.e. EMU members including the problematic ones.<sup>10</sup> Overall, the situation after Fed tapering rumors suggests that during the recent recovery period in EMU, not the quality but the “illusion of quality” (probably due to fiscal history of developed and emerging countries) takes a big role. Of course, one could argue that the main influence was originated from the heightened confidence in distressed governments’ commitment towards sustainable fiscal policies,<sup>11</sup> and the forthcoming macruprodential regulations of ECB. However, it should be kept in mind that commitments from governments and policy responses from ECB

> 9We observe severe capital outflows from emerging markets during this period. Due to these outflows, currencies of Chile, India, Brazil, Turkey, South Africa and Indonesia experienced depreciations ranging between 10% and 26% in 2013. Similarly, several emerging sovereign bond yields shot up and equity markets in some of these countries loss up to 30% of their values.

> 10For example, developed countries worldwide were averaging about 100% debt to GDP ratio while in the emerging countries this ratio was averaging around only about 40% by the end of 2012. Similar considerable differences in other fiscal indicators were also observed.

> 11For example, see Codogno et al. (2003); Barrios et al. (2009) for earlier studies on the subject.

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took place occasionally during 2009-2012 and both did not seem to be enough for a healthy recovery and did not force these yields to act as a single asset class back then.<sup>12</sup> Finally, by using the penalized contrast function methodology of Lavielle (2005), we formally present the dates when the average correlation shifts to different mean levels in Fig.4.<sup>13</sup> We can see that within short time intervals, the aforementioned critical events triggered structural changes in the correlation dynamics.

So, what happens next? Currently, EMU members seem to benefit from the illusion of quality as the upward pressure on their bond yields’ spreads have loosened and the coherency in the bond yield movements has been improving significantly. Therefore, struggling members have time to implement structural reforms until the next excess liquidity environment for whatever the reason. Moreover, a shift in expectations due to this illusion can lead to a further selffulfilling wave of cross-border capital flows, which would definitely support EMU members on this path.<sup>14</sup>

> 12Although it is not the scope of this paper, further research could seek answer to the rhetorical question of “do we need any other central banks then Fed?”. Naturally, the answer would be “yes” but the idea is to stress on the challenges faced with other central banks when one clearly dominates the others.

> 13The main advantage of this methodology is that it does not impose any condition on the dependency and distribution of the data. For details, see Appendix B.

> 14See Masson (1999) for theoretical support to this argument.

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# **4. Conclusion**

This paper focuses on the recent developments observed in the EMU sovereign debt markets. First, we try to clarify the integration and segmentation of the EMU sovereign debt markets before and after the crisis from a novel perspective. Accordingly, an almost perfect integration is observed in the pre-crisis era. However, with the collapse of Lehman Brothers and the following fiscal problems in Greece, the integration structure is severely damaged, yielding an all time low group correlation level in the EMU. By introducing the simply-stable network concept, we show that the decrease in the overall correlation level after the financial crisis is not due to a general sparsity of EMU members. It is caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement. Accordingly, in its new form, EMU sovereign debt markets are segmented into two main parts where fiscal performance seems to be the main reason in this segmentation. Moreover, these two main parts are connected through only one member, Belgium, in our setup. This situation suggests that the path to recovery in a positive future or transmission of crisis in a negative one will take place through this member and its debt market should be monitored by policymakers and investors.

In the second part, we try to shed a light on the recovery signals and in particular, the increased harmony observed in EMU bond markets recently. It is argued that fundamental indicators did not play a role in this case as they were in a much worse situation compared to pre-crisis levels and did not even show promising performances up until the start of this process. Alternatively, transitions were mainly attributed to the Fed tapering rumors originated in early 2013 and the following diminishing excess liquidity environment which eventually created an instinct of “need to flight-to-quality” from emerging countries to developed ones, even though fiscal performances tell that a different story should 17
have been written. The results naturally bring out the following question: If the turmoil observed during 2008-2009 was a self-correction in financial markets then, were EMU bond markets overvalued pre-2008 or are they undervalued post-2009?

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