Eurobond Correlation Network: a case history

Eurobond Correlation Network: a case history

During the last two decades, the European sovereign bond market has been driven not only by the converging mechanism of its common currency, but by the centrifugal forces of trade imbalances and unequal sovereign credit capacity also. The Eurozone government bond spreads experienced “euro convergence” at the end of the 90’s in expectation of the benefits of the common currency. Up until 2008, bond spreads only reflected the different liquidity between the issuers and not the redenomination risk into a weaker currency or even sovereign credit risk.

As the American “subprime” crisis triggered the burst of a credit bubble in the European banking system, privately held debt was transferred to public balance sheets in order to prevent a chain reaction of defaults. This risk transfer and the repricing of perceived sovereign risk led to increased spreads of “peripheral” versus “central” government bonds within the Eurozone.

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Graph 1

Similar to our first post, we will continue to study correlation networks in order to understand financial market dynamics. As before, we will use correlation networks analysis and visualization techniques provided by FNA.

If we look at the correlation structure of Minimum Spanning Tree between Eurozone government bonds around 2005, we see one tight cluster where all Eurozone countries exhibit similar characteristics. Throughout the Eurozone, the sovereign bonds offer “safe haven” status and act as “risk off” assets.

GovNetwork2005_cropped

GovNetwork2005_outliersWe can use outliers from the range predicted by normal distribution of returns as signals to changes in volatility regime and forecasters of expected larger moves.

Around the start of 2005 positive outliers in bond returns far exceed the negatives, signalling higher returns and lower volatility ahead. At this point it seemed as though the introduction of the euro and the regulated fiscal policy of the Eurozone were working well.

In 2010, following the sub-prime crisis in the US, credit problems began to affect the Eurozone and the expanding financial crisis found a new theatre in Eurozone sovereign credit risk.

With the looming possibility of Greece defaulting on its debt, the credit spreads of peripheral countries started to diverge with Greece, Ireland and Portugal rising sharply.

 

Graph 2

Due to the repricing of sovereign credit risk, the periphery bonds lost their “safe haven” status and changed their correlation pattern from “risk off” towards “risk on” assets.

Portugal, Spain, Italy and Greece exhibited many negative outliers in returns and moved in the opposite phase with northern European countries’ debt.

GovNetwork2010_outliersGovNetwork2010_cropped

Looking at the bond correlation structure at this time, we see the “core” and “periphery” blocks already have formed. Finland, Germany, France, Austria and Belgium are on one end of the spectrum while Italy, Spain, Portugal and Greece are on the other. The closer to the correlation tree extremes a node appears in the visualisation, the sharper the characteristics: high quality debt on one end and risky unsafe investment on the other.

From 2010 to 2012, negative correlations between the two blocks of “core” and “periphery” bonds appeared, signalling capital flights at the long end of the yield curve. Negative correlations in the figure below are shown in red. Investors returned to the safer fiscal havens of Northern and Central European countries.

GermanyCorr2011_cropped

Note also how spread apart the correlation structure between European sovereign bonds became at this point. The uniformity that the euro brought about in the first half of 2000’s appears long gone. The correlation tree and spreads both cover a much larger spectrum at this stage, showing economical differences between Eurozone countries.

In 2012, even within the core block of the Eurozone, a two-tier substructure appeared; France, Belgium and Austria decoupled somewhat from the core block of countries after a rating downgrade (see the figure below).

GovNetwork2012_cropped

As a countermeasure, the non-bailout clause of the Maastricht treaty and the strict ECB monetary policy were both quickly reversed.

In 2011, the European Financial Stability Facility (EFSF) was founded. It raised capital for countries in a bailout programme in the private capital market in the form of bonds issues guaranteed by the whole Eurozone countries.

Capital markets accepted the EFSF bonds as part of the Eurozone core from the beginning. These bonds trade between the German and French yielded levels and are thus more expensive as a “Eurozone basket”, reflecting a high level of investor’s confidence in the guarantee structure.

In parallel, the ECB not only lowered short term interest rates and essentially replaced the interbank money market with unlimited liquidity, but also supported the long end of the yield curve indirectly and directly.

In 2013, these measures started to take effect. Yield spreads decreased substantially and correlation patterns between the bond yields normalised. All the correlations between Eurozone government notes are now positive. Greek and Portugal correlations with Germany do not have statistical significance any longer.

Graph 3GermanyCorr2013_cropped

Those countries in compliance with the restructuring conditions of the Eurozone rescue packages like Ireland, Portugal and Spain have also managed to improve their competitiveness and economic fundamentals.

The only exception is – was – Greece, which decoupled from the other peripheral countries. In the autumn of 2014, Greek yield spreads increased again. The 2015 elections and subsequent difficult negotiations between Greece and the creditors dominated the news until the referendum on July 5th.

pschwender Peter Schwendner
Guest writer Peter Schwendner is a Senior Lecturer at the Institute for Wealth and Asset Management at ZHAW School of Management and Law, Zurich, Switzerland. His research interests are financial markets, asset management and network analytics. Peter received a PhD in Physics in 1998 for his research at Max Planck Institute in Goettingen. He has 15 years’ work experience in the financial industry as a head of quantitative research at Sal. Oppenheim and as a partner at Fortinbras Asset Management, developing investment products.

 

Eugene Neduv

Eugene Neduv

Risk Researcher at Centre for Risk Studies
Eugene Neduv is an expert in quantitative methods in finance and a risk management professional. His interests include network analytics, volatility trading and portfolio optimisation. Eugene graduated from Columbia University in 2002 with a PhD degree in Mathematics and continued as a postdoctoral researcher at the Brazil Institute for Pure and Applied Mathematics and Humboldt University in Germany. Eugene has 10 years' work experience in financial analytics and risk management software and as an independent consultant for several hedge funds in New York and Sao Paulo.

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