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Economic Modelling 35 (2013) 35–44
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Economic Modelling
j ourna l homepage: www.e lsev ie r .com/ locate /ecmod
One crisis, two crises…the subprime crisis and the European sovereigndebt problems☆
Loredana Ureche-Rangau a,⁎, Aurore Burietz a,b
a Université de Picardie Jules Verne, CRIISEA, Pôle Universitaire Cathédrale, 10 placette Lafleur, BP 2716, 80027 Amiens Cedex 1, Franceb Department of Finance, IESEG School of Management, 3 rue de la Digue, 59000 Lille, France
☆ We are grateful to Charles Calomiris, Günter Frankemous Referees, and seminar participants at the UnivIESEG School of Management, the Chicago 2012 Global F2012 Groupement de Recherche Européen Conference,Française de Science Economique Conference. All the re⁎ Corresponding author.
E-mail addresses: [email protected] ([email protected] (A. Burietz).
1 E.g. the American S&L crisis in 1980, the Black Monreal estate crisis in Japan in 1989, the currency crisis in Mof 1997, the Russian crisis in 1998, the sovereign debt1999, the dot com bubble in 2000, the sovereign debt cthe subprime crisis in 2007.
0264-9993/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.econmod.2013.06.026
a b s t r a c t
a r t i c l e i n f oArticle history:Accepted 18 June 2013Available online xxxx
JEL classification:E01G01G20G28H12H63
Keywords:Financial crisisGovernment interventionsEuropean sovereign debtDynamic panel
This article explores the link between the subprime crisis and the European sovereign debt crisis. Using a paneldata approach, we estimate the impact of the different government interventions aimed at rescuing financial in-stitutions on the significant increase of the costs of public debts as measured by the interest rate spreads withrespect to Germany. We show evidence on the existence of a statistically significant link between the two crisesembodied by capital injections and government guarantees. More specifically, the two types of government in-terventions have a negative impact on the cost of the sovereign debts under study. This empirical result can ex-plain why the sovereign debt crisis immediately followed the subprime crisis.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
International financial globalization and market integration led tomore intense capital mobility and made financial crises more andmore frequent in nowadays economies1 (Bordo and Eichengreen,1999). The most recent example is the European sovereign debt crisisthat immediately followed the subprime crisis. As a consequence, de-veloped countries' governments were forced to implement variousrescue plans aimed at avoiding market panic and restoring investors'confidence. These financial packages mainly consisted in capital injec-tions, liquidity provisions and guarantees. One major consequencewas that some governments began to face difficulties to refund theirown debt starting with the beginning of 2010.
, Patrick Sevestre, two anony-ersity of Picardie Jules Verne,inance Conference, the Nantesand the Paris 2012 Associationmaining errors are ours.
Ureche-Rangau),
day in 1987, the financial andexico in 1994, the Asian crisis
crises in Brazil and Ecuador inrisis in Argentina in 2001, and
rights reserved.
Several studies tried to characterize the different crisis episodes andthe similarities/differences that can be established between them(Claessens et al., 2010; Hautcoeur, 2011; Kindleberger, 2005; Reinhartand Rogoff, 2008a, 2008b, 2008c; Ureche-Rangau and Burietz, 2010;White, 2009). These comparative and historical approaches shouldallow a better understanding of the main causes that may explain theemergence of a crisis. However, Hautcoeur (2011) argues that even if fi-nancial crises may present numerous common points, they all havetheir own specificities, which make them difficult to forecast.
In this article, we focus on the link between the subprime crisis andits spillover effects in Europe and the recent European sovereign debtcrisis. Our goal is to shed some additional light on the way the differentrescue packages, implemented by the governments of the countries inthe Euro zone following the subprime crisis, impacted the risk andhence the cost of the European sovereign debts. Thus, our objective isto empirically measure the relation between the rescue plans and thecosts of the sovereign debts as measured by the interest rate spreads.We use the capital injections, central bank support and guarantees pro-vided between January 2008 and September 2011 for a sample of elevenEuropean countries as proxies for the various rescue packages.Weman-aged to hand collect a complete and original database using a variety ofavailable sources allowing cross-checks and robustness comparisons.This monthly database is one major contribution of the present work.
Our study provides three important results. First of all, capital in-jections and guarantees significantly contribute to an enlargement
36 L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
of the interest rate spreads, i.e. they increase the cost of the sovereigndebt service. Moreover, the sharp fall of several stock markets and theassociated high volatility translates into higher risks associated tosovereign debts and hence, larger risk premia. Second, the only ex-planatory variable that has no statistical significant impact on thespreads is the central bank support. We explain this result by the dif-ficulty to collect clear-cut relevant information on this variable in theEuro zone. Finally, this article shows direct empirical evidence on thelink between the banking crisis and the European sovereign debtcrisis.
The paper is structured as follows. Section 2 sketches a brief liter-ature review on banking and sovereign debt crises and underlines thepotential link between them. Section 3 is dedicated to the descriptionof our data andmethodology. Sections 4 and 5 present our results andassociated robustness checks while Section 6 concludes.
2. Literature review: from the subprime (private) debt crisis to thesovereign debt crisis
Financial institution crises can be illustrated by a series of boom andbust cycles (Reinhart and Rogoff, 2010). The bust phase is dominated bybank runs and bankruptcies in the banking industry (e.g. Northern Rockor Lehman Brothers in 2008), in addition to the numerous governmentinterventions that aim at restoring confidence on the markets. Regard-ing government interventions, Helwege (2010) distinguishes betweenthe two types of risks that may condition the “too-big-to-fail” policies:the risk of counterparty contagion (e.g. the AIG case) and the informa-tion contagion risk (e.g. the Lehman Brothers case). The former typeof risks should induce a government intervention, while the latter notnecessarily.
While the literature is almost consensual when it comes to theboom and bust phases of a private debt crisis, there is much less con-sensus on the definition of a sovereign debt crisis. Among the differ-ent proposals advanced by the literature (see for example Beim andCalomiris, 2001; Detragiache and Spilimbergo, 2001; Manasse et al.,2003; Pescatori and Sy, 2007), we consider in this article that a coun-try enters a sovereign debt crisis period when it experiences difficul-ties in funding its debt. One first indicator would be an increase in thecost of the debt, as measured by the interest rate spread.
A whole strand of literature has been devoted to describing andexplaining the links between financial institution crises and sovereigndebt crises. Pescatori and Sy (2007) for example, study emergingbond markets between 1994 and 2002, and show evidence that thebanking crisis determinants identified in the 1980s can be used to an-ticipate sovereign debt crises one to two decades later. In the contextof the subprime crisis, Wehinger (2010) describes how governmentshave tried to limit the consequences of the bust phase by providingpublic financing to financial institutions like Fannie Mae, FreddieMac and other Government-Sponsored Enterprises (GSEs) in the USor Dexia in Europe. More generally, the different governments pro-moted rescue packages created by the IMF and the EU to supporttheir banking industry, to avoid a global panic on the developed mar-kets and, by domino effects, on the emerging markets. As such, gov-ernment debts recorded noteworthy increases as illustrated byFig. 1 for a sample of eleven developed countries starting with 1995.As shown by Fig. 1, the rise of the debt-to-GDP ratio is more pro-nounced for countries like Greece and Ireland; it is however also im-portant for countries like the UK or Portugal, which present verysimilar patterns. This evolution goes in line with the hypothesis thatthe subprime crisis starting in 2007/8 may indeed explain the in-crease of the cost of the European sovereign debts (Wehinger,2010). In order to support banks experiencing important financial dif-ficulties, governments had to inject huge amounts of money(Gennaioli et al., 2010) which in turn exacerbated their own debts.
Two theoretical arguments are generally used to explain how thetwo types of crises can be linked. First of all, sovereigns are nowadays
considered as being the last “rung of the scale” and henceforth aslenders-of-last-resort (Kindleberger, 2005). Therefore, as argued byBordo and Eichengreen (1999), the government has to be responsiblefor maintaining confidence and avoiding bank runs (Laeven andValencia, 2008). Second, sovereign bonds often play the role of therisk-free asset. Whenever banks are in trouble, financial markets expe-rience the flight-to-quality phenomenon (Fostel and Geanakoplos,2008; Furfine, 2001; Gagnon and Hinterschweiger, 2011; Holmströmand Tirole, 1993), i.e. a switch between risky and riskless assets in inves-tors' portfolios. Therefore, governmentsmust be able to issue debtwith-out increasing their own risk too much.
Moreover, Bordo and Eichengreen (1999) insist on the fact thatthe government has to use all its resources to limit the credit crunchphenomenon. Therefore, the government may implement significantrescue packages, including central bank interventions, proceed tocapital injections to limit liquidity shortage and provide guaranteesto avoid a credit crunch (Candelon and Palm, 2010; Laeven andValencia, 2008). More specifically, in the context of a financial crisis,the central bank may inject liquidity by direct interventions on themoney market (Kindleberger, 2005; Von Hagen and Ho, 2007), estab-lish a government-owned company to buy back bad loans from banksand assist bank property transfer to new owners (Laeven andValencia, 2008). However, this desire to maintain confidence maylead to a significant increase of the sovereign debt burden as well asof the sovereign risk perception (Diaz-Alejandro, 1985; Reinhartand Rogoff, 2011; Velasco, 1986). This type of argument can be tracedback to Krugman (1999) who has already discussed the level of guar-antees a government can engage to restore confidence in the market,pointing out that guarantees, even if they are not as tangible as capitalinjections, may also damage the sovereign debt position of a country(Corsetti et al., 1999; Krugman, 1999; McKinnon and Pill, 1996) viamoral hazard behavior (Lindert and Morton, 1989). Finally,Detragiache (1996) develops the idea that guarantees might influ-ence the risk perception of investors with respect to sovereign debt.A significant amount of guarantees might for example positively im-pact the cost of debt.
Empirical studies also deal with the link between a financial institu-tions crisis and a sovereign debt crisis. Reinhart and Rogoff (2008c) pro-pose a historical analysis of financial crises in 66 countries, including thesovereign default event, between 1350 and 2006. Their findings confirmthe existence of a link throughwhich a banking crisis leads to a sovereigndebt crisis (Arellano and Kocherlakota, 2008). Themain argument is thatfollowing a banking crisis, the public budget ismodified due to a decreasein tax revenues (less consumption, need to support the economy and in-vestment activities) combined with an increase in governmental ex-penses through rescue packages (Eichengreen and Portes, 1989; Lahiriand Vegh, 2003). In the three years following the banking crisis,Reinhart and Rogoff (2008c) report an increase of the public debt levelby 86%. Acharya et al. (2011) focus on the recent subprime crisis and itsconsequences on the sovereign debt level. Based on banks and sovereignCDS fluctuations, Acharya et al. (2011) succeed in identifying three timeperiods. The first one, from January 2007 to September 2008, is dominat-ed by the banking crisis with a huge increase in bank CDSwhereas sover-eign CDS remain stable. From September 2008 to October 2008, becauseof the rescue packages implemented by almost all developed countries,they point out a transfer of default risk from banks, whose CDS decline,to sovereigns. Finally, from October 2008 until December 2010, the situ-ation for banks gets worse while government debts remain risky too(Acharya et al., 2011; Ejsing and Lemke, 2011). Over this last period, wemay conclude that the increase in the government debt level not onlyfails in solving the leverage problem in the banking sector but it evenfuels it (Dieckmann and Plank, 2012; McKee, 2010). Based on a graphicalapproach, Candelon and Palm (2010) use six different variables in orderto check the link between the 2007/8 banking crisis and the Euro zonesovereign debt crisis. These variables are government guarantees, capitalinjections, purchase of assets and lending by Treasury, central bank
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96 98 00 02 04 06 08 10 12 14 16
_AUSTRIA
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96 98 00 02 04 06 08 10 12 14 16
_BELGIUM
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96 98 00 02 04 06 08 10 12 14 16
_FRANCE
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96 98 00 02 04 06 08 10 12 14 16
_GERMANY
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96 98 00 02 04 06 08 10 12 14 16
_GREECE
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96 98 00 02 04 06 08 10 12 14 16
_IRELAND
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96 98 00 02 04 06 08 10 12 14 16
_ITALY
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96 98 00 02 04 06 08 10 12 14 16
_NETHERLANDS
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96 98 00 02 04 06 08 10 12 14 16
_PORTUGAL
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96 98 00 02 04 06 08 10 12 14 16
_SPAIN
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96 98 00 02 04 06 08 10 12 14 16
_UK
DEBT
Fig. 1. Government debt as a percentage of GDP. Notes: Figures after 2009 are IMF estimations.Source: International Monetary Fund, World Economic Outlook Database, September 2011.
37L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
support providedwith orwithout Treasury backing (Cottarelli, 2009) andstock market indices. They show evidence of a negative correlation be-tween stock market indices and the level of the debt-to-GDP ratio, aswell as between stock market indices and the level of government bondspreads. Finally, Arezki et al. (2011) use sovereign rating news and therisk of contagion across different markets and show that rating down-grades deeply increase the spillover effects as many regulationsimplemented to monitor financial markets and transactions are basedon these ratings (Sy, 2010).
2 To save space, the results of these preliminary tests are not reported here. They arehowever available upon request.
3 Ireland's breaking point is situated in the second quarter of 2008, previously tothose identified for the other countries in the sample. Before the crisis, the Irish econ-omy, mainly based on financial services, was very prosperous, attracting large financialinvestments especially from the US. The country benefited from a significant economicgrowth combined with an increase in both the household indebtedness by more than120% in six years, until 2007, and the housing prices. In the Euro zone, the Irish bankingsystem is the largest one, so the most sensitive to the international aggregate risk(Gerlach et al., 2010). Therefore, when the subprime crisis occurred in the US, thecountry was among the first economies suffering from spillover effects. This particularsituation may explain why the debt-to-GDP ratio in Ireland started to skyrocket beforethose of the other countries in our sample.
3. Data and methodology
3.1. Data
This article aims at investigating the link between the private debtcrisis, i.e. the subprime crisis, and the sovereign debt crisis, by analyz-ing the impact of government interventions on the cost of sovereigndebts. Our sample is composed of eleven European countries, namelyAustria (AU), Belgium (BE), France (FR), Greece (GR), Ireland (IR),Italy (IT), Netherlands (NL), Portugal (PT), Spain (SP), the UnitedKingdom (UK) and Germany (GE) which stands as our benchmark.The period covered by our study goes from January 2008 untilSeptember 2011, mainly due to data availability. Laeven and Valencia(2010) date the beginning of the banking crisis in 2008, except for theUK and theUS, forwhich the crisis started in 2007. Over this time period,we use variables that proxy government interventions to support theeconomy.
Fig. 1 shows a large increase in the sovereign debt-to-GDP ratioduring a very short time period. The earliest government interventionannouncements were made at the end of 2008/beginning of 2009(Laeven and Valencia, 2010). We use the Quandt–Andrews (QA)and the Chow breaking point tests to identify the exact timing ofour analysis. The QA test allows us to detect the significant breakingpoints for each country in our sample. We first identify the year,
then the quarter. Once the quarter fixed for each country, we usethe Chow test to assess the significance of the result associated to aspecific date. Our findings2 confirm that the breaking point in theevolution of the government debt level occurs in the last quarter of2008 for 6 countries out of eleven (i.e. AU, GR, NL, PL, SP, UK) andin the first quarter of 2009 for the other countries in our sample(BE, FR, GE, IT), except for Ireland.3 These breaking points coincidewith government announcements of rescue packages aimed atmaintaining confidence on the markets following the banking crisis.In order to include all the relevant information, we fix our startingpoint in January 2008.
We proxy the sovereign debt cost by the spread between thelong-term interest rate of each country in our sample with respectto the German 10-year interest rate. For all the countries, the increaseof the spread with respect to Germany is due to an increase in thelong-term interest rate of each country combined with a decrease ofthe German sovereign interest rate. We choose the interest rates in-stead of the classical debt-to-GDP ratio following Wehinger (2010)who argues that the sovereign risk of a country recording a highdebt-to-GDP ratio and a low interest rate is not equivalent to the
4 In order to assess our choice of considering together CB, PAL and LP, we alsoproceeded to a different grouping by including stimulus packages in the CI variableas stimulus packages represent direct loans. Moreover, purchases of asset and lendingwere included in the G variable as they can also be considered as collateral for bankloans. The results obtained with these new groupings remain completely unchanged.For space reasons, they are only available upon request. We thank an anonymous ref-eree for this suggestion.
38 L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
risk a country faces when its debt-to-GDP ratio is low but the cost ofdebt is high. Moreover, Wehinger (2010) also underlines the superiorityof the interest rate spreads with respect to CDS spreads in apprehendingsovereign risks as the formers are better in encompassing all the signifi-cant variables that characterize a country (e.g. politics, institutions, etc.counterparty risks). He also argues that CDS spreads may be illiquidand very volatile. To conclude the interest rate spreads appear to beone of the best proxies for the cost of sovereign debts. We collect the in-terest rates on a monthly basis from Bloomberg.
Our explanatory variables are similar to those used by Candelon andPalm (2010) and Laeven and Valencia (2010). For each country, we firstcollect themonthly prices of the representative stockmarket index fromBloomberg, namely the WBI for Austria, the BEL20 for Belgium, theCAC40 for France, the ASE for Greece, the ISEQ for Ireland, theFTSE-MIB for Italy, the AEX for the Netherlands, the PSI20 for Portugal,the IBEX35 for Spain and the FTSE100 for the UK. We then computethe monthly returns of these indices. This allows us to include in ouranalysis the general economic environment of each country by takinginto account investors' anticipation of the industrial production fluctu-ations and the increase in the fiscal costs induced by the rescuepackages.
The other explanatory variables used in the analysis are the CapitalInjections (CI), the Purchase of Asset and Lending by the Treasuryand/or the Central Bank (PAL), the Central Bank support with Treasurybacking (CB), the Liquidity Provisions (LP) and the Guarantees (G).The exact meaning and use of these variables follow the IMF definition,provided by the IMF Fiscal Affairs Department and validated byCottarelli (2009). According to IMF definitions, CI refers to bank recapi-talizations. It consists in either capital injections or nationalizations. InOctober 2008, euro 40 billion for example were provided by the Frenchgovernment to financial institutions which were facing difficulties, outof which euro 21 billion were used by the top six French banks. Directloans made by governments, as well as their purchase of toxic assetsfrom banks through government-owned enterprises or by the Treasuryand/or the Central Bank are contained by the PAL variable. In April 2009,Ireland announced its project to create the National Asset ManagementAgency (NAMA), with the objective of buying risky loans from banks.Transfers to the NAMA started in September 2009 for a total of euro90 billion and involved several big size banks. In addition, followingthe IMF categorization, we have decided to include stimulus packagesin the PAL variable too, as they are direct loans to businesses providedby governments outside the banking system, aimed at sustaining eco-nomic activity. Stimulus packages include support to households, busi-nesses, the labormarket and public investment. Taking into account thepermanent effects of these packages is important as their implementa-tion following a banking crisis may increase the sovereign debt leveldue to an increase in government's expenditures and a deteriorationof its structural balance (Cottarelli, 2009; Van Riet, 2010). BetweenNovember 2008 and June 2009 for example, Italy decided to implementthree different stimulus packages to support the economic activity: onein November 2008 for a total of euro 80 billion consisting in transfers tolow-income households and relief measures for enterprises; a secondone in February 2009, of euro 2 billion providing the car industry witha scrapping incentive; and a final one, in June 2009, of euro 4.5 billionto strengthen the social safety net and provide incentives for purchasesof machinery. CB and LP will proxy central bank interventions such asdirect loans and purchase of Asset-Backed Securities (ABS). However,CB implies Treasury backing while LP does not. In this variable, we in-clude for example the special liquidity scheme implemented by theUK between April 2008 and October 2008 to swap banks' risky mort-gage assets for a total of pounds 200 billion. Finally, G summarizes allthe government guarantees on financial sector liabilities. In October2008, Germany for example approved the Financial Market Stabiliza-tion Act for a total of euro 400 billion to boost interbank loans and toavoid a credit crunch due to the potential spillover effects followingthe collapse of Lehman Brothers.
The first and the most important rule we apply consists in takinginto account new and isolated government interventions aimed atdealing with the subprime crisis only. We thus exclude from our da-tabase regular central bank activities like the main refinancing opera-tions with a maturity of one week, the long-term refinancingoperations with a maturity between three and six months and thefine-tuning operations to manage the reserves of the interbank mar-ket. For each intervention, we consider the month of its official an-nouncement and the maximum amount dedicated to each plan,independent on whether the whole amount was used or not. Avalue equal to zero is attributed to a month where no interventionwas recorded. Moreover, when a country decides to implement twocapital injections within the same month, the two amounts areadded.
Data was gathered by scanning a variety of official daily newspaperslike the Financial Times, theWall Street Journal, the Dow Jones Interna-tional News, Reuters, OECD and central banks' websites and the MayerBrown summaries of government interventions. We mainly use theFactiva search engine that provides a direct access to the majority ofthese newspapers. The objective is to build up a complete and correctdatabase both in terms of amounts and timing for each country andeach bank; to do so, one has to cross information and make compari-sons. Table 1 is an illustration of the data collected for the UK. For allthe other countries, the information is organized following the samestructure. Thus, we are able to identify the same five periodsmentionedby Panetta et al. (2009), i.e. September 2008— standalone interventionsaddressed to specific financial institutions experiencing difficulties,October 2008— government rescue packages launched for the financialsector in general, November and December 2008 — a decrease in thetiming and amount of interventions, January 2009 to April 2009 —
new vague of interventionsmainly focused on financial assets and final-ly, May 2009 to June 2009 — the end of some plans whereas new oneswere launched.
In the next step we build our monthly database as a balancedpanel with the same number of observations for each entity andeach time period. We had to deal with data availability issues, espe-cially regarding the interventions of the European Central Bank(ECB), and decided to merge in the same category three variables,namely CB, PAL and LP following Laeven and Valencia (2010).4 Thefinal variable will hereafter be CB.
We must also explain another specificity of the Euro zone, relatedto the ECB architecture, which affects the content of the variablecalled G. First, the ECB proceeded to four capital injections duringthe period under study. For each capital injection, we have decidedto increase the level of G for the countries in our sample as they allmade capital subscriptions to fund the ECB. The amount is deter-mined based on the different allotment keys established in January2007 and revised in January 2009. In addition, the ECB promotedtwo different types of plans to help the European economies. Thefirst one, i.e. the European Financial Stability Facility (EFSF), was cre-ated in May 2010 with a reserve of euro 440 billion, extended byaround euro 560 billion in October 2011. The EFSF is programmedto last until June 2013. Following this date, it will be replaced by theEuropean Stability Mechanism (ESM). Until now, the fund was usedby three countries, namely Ireland in November 2010 for an amountof euro 85 billion, Portugal in April 2011 for euro 78 billion andGreece in July 2011 for euro 109 billion. This implies that the Europeanmembers are indirectly involved in these loans as guarantors (e.g. theEFSF Agreement). The second type of plan, called the Covered-Bond
Table 1UK government interventions between January 2008 and October 2011.Sources: Official daily newspapers, Bank of England website, Mayer Brown summary.
Date Operation Company Amount(£ bn)
Plan
Feb. 08 PAL Northern Rock Plc 00.7Mar.08 CI Banks 5Apr. 08 CB Banks 50 Special liquidity schemeMay.08 CI Bradford &
Bingley0.4
Aug.08 CI Northern Rock Plc 3Sept.08 CI Financial system 5Sept.08 CB Banks 40 Special liquidity schemeSept.08 PAL Bradford &
Bingley50
Oct. 08 CB Banks 110 Special liquidity schemeOct. 08 G Banks 250 Credit Guarantee schemeOct. 08 CI Banks 50 Bank recapitalization schemeOct. 08 CI RBS 36.6 Emergency Liquidity
AssistanceNov.08 CI HBOS 25.4 Emergency Liquidity
AssistanceNov.08 CI Stimulus package 20Jan. 09 PAL High quality
assets50 Asset Purchase scheme
Jan. 09 CI Homeowners 285 Mortgage rescue schemeFeb. 09 G RBS 325 Asset Protection schemeMar.09 PAL High quality
assets100 Asset Purchase scheme
Mar.09 G Lloyds 260 Asset Protection schemeAug.09 PAL High quality
assets25 Asset Purchase scheme
Nov.09 PAL High qualityassets
25 Asset Purchase scheme
Nov.09 CI RBS 25.5Nov.09 CI Lloyds 5.7Dec.09 G Banks 50 Credit Guarantee schemeJan. 10 CI Northern Rock Plc 8.5Oct. 11 PAL High quality
assets75 Asset Purchase scheme
5 We must however underline that the relevance of these unit root tests is ratherlow given that we use a limited number of observations over a rather short time-period and that breaks are present in our series. We thank an anonymous referee forpointing it out.
6 FMOLS take advantage of the long-run covariance information; hence, they are as-ymptotically efficient, contrary to OLS. Moreover, FMOLS were preferred to DynamicOLS (DOLS) because of the limited number of observations over a rather short-time ho-rizon; indeed, a high optimal number of leads and lags selected by the different infor-mation criteria in the case of DOLS may affect the relevance of the results when only alimited number of observations is available and/or exceed the time period under study.
7 Supported by the Johansen cointegration test on the ten countries.8 Results are available in the Appendix A.
39L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
Purchase Program (CBPP)was established inMay 2009,with a durationof 1 year and a total amount of euro 60 billion. In terms of G, it had thesame implications for the European countries than the EFSF. The differ-ence comes from its objective, which was to support the funding for fi-nancial institutions and individuals deeply impacted by the crisis bypurchasing long-termassets. This planwas extended toowith the intro-duction of the Securities Market Program (SMP).
Once we have our monthly data, the last step is to express it as apercentage of the GDP as a rescue plan involving huge amounts inthe context of a high GDP has absolutely not the same impact as fora low level of GDP. The challenge is to compute monthly GDP figuresfrom quarterly reported GDP statistics that we collected fromBloomberg. We begin by describing the GDP based on monthly mac-roeconomic variables that best explain its evolution. These monthlyvariables were defined following Cüche and Hess (1999) amongothers and an ECB study (Gerdesmeier and Roffia, 2003), alsodepending on data availability on Bloomberg. In order to insure ho-mogeneity, for each country in our sample we selected seven vari-ables which can explain the evolution of the GDP. The series werecollected over a longer time period than the one under study inorder to insure relevant coefficients for the description of the evolu-tion of the GDP. The variables we use are the Industrial ProductionEX Construction (IPEXC) Index, the Industrial Production for Con-struction (IPC) Index, the New CAr REGgistration (NCAREG) Index,the Retail Sales for Food (RSF) Index, and Non-Food (RSNF) Index,the Trade Balance (TB) and the Unemployment Rate (UR).
First, we run Augmented Dickey–Fuller (ADF) stationarity tests forthe eight series, quarterly GDP included. For all these series, we were
unable to reject the presence of a unit root.5 Thenwe compute quarterlyseries from the monthly ones by taking the average value of the threemonthly data of each quarter. We have now quarterly series both forthe GDP and the other seven variables. We are able to run a Fully Mod-ified Ordinary Least Squares (Phillips and Hansen, 1990)6 as in Eq. (1),which takes into account the cointegration7 of our series, i.e.
GDPQt ¼ α þ β IPEXCQt þ γ IPCQ
t þ δ NCAREGQt þ θ RSFQt
þ ρ RSNFQt þ σ TBQt þ φ URQ
t þ εt : ð1Þ
For each country, we only conserve the significant coefficients pro-vided by the FMOLS regressions associated to a R2 higher than 0.95;they are not systematically identical from one country to the other.8
Based on these coefficients we then compute our monthly GDP. Finally,we adjust eachmonthly GDPbyminimizing the difference between twoconsecutive months under the constraint that the sum of the threemonthly GDPs should be equal to the associated quarterly GDP.
Table 2 presents the basic descriptive statistics of our six differentvariables: the interest rate spread with respect to Germany, the stockmarket returns and the four intervention variables expressed as per-centage of the country's GDP, e.g. capital injections, purchase of assetsand lending, central bank support and guarantees.
Between January 2008 and September 2011, the interest ratespreads record a continuous increase with a maximum spread equalto 15.95% for Greece. On the opposite, the stock market returns de-crease, as a direct consequence of the crisis. Regarding the interven-tion variables, they all have a positive mean with high volatility asrescue packages are implemented occasionally, depending of the eco-nomic context, the evolution of the crisis and the needs of each coun-try. The figures reveal that the most significant amounts as apercentage of GDP are dedicated to guarantees. Indeed, governmentsmay be more inclined to provide banks with guarantees rather thancapital injections and hence limit their financial involvement. Thethree other variables show a relatively small mean but with highdiscrepancies between countries from 0.4228% (for UK) to 802%(for Ireland) for the purchases of asset and lending for example.
3.2. Theoretical background
In order to empirically assess the link between the subprime crisis andthe European sovereign debt problems, we analyze the impact of rescuepackages implemented after the banking crisis on the cost of long-termsovereign debt. More specifically, we test the following model:
si;t ¼ αi þ γ si;t−1 þ β′xi;t þ εi;t ð2Þ
where s stands for the spread with respect to the German long-term in-terest rate, x is a vector of explanatory variables, e.g. CI for capital injec-tions, CB for central bank interventions, G for guarantees, SK for returnsof the representative stock market index, and ε is the residual for eachcountry i in t.
The potential effects of the different explanatory variables on theinterest rate spread are rather complex. As such, Acharya et al.(2011) for example establish a positive wealth transfer of the
Table 2Descriptive statistics.
Variable Mean(%)
Standard deviation(%)
Minimuma
(%)Maximum(%)
S 1.5230 2.3073 0.1000 15.9500CIb 3.8404 20.2920 0.2449 211.7376CBb 7.1062 67.5781 8.5847 781.3849PALb 2.9827 38.4754 0.4228 802.1389Gb 20.0673 200.6863 0.0474 4136.4400SK −1.4861 7.5229 −28.3829 21.9293
a For the intervention variables, i.e. CI, CB, PAL and G, the minimum value is the firstlowest value different from 0.
b As a percentage of GDP.
40 L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
different governmental interventions aiming at reducing the risk ofcredit crunch in the banking system and underline a decrease in theCDS of banks. However, this decrease in the CDS of banks was verytemporary, as it only lasted for two months between September andOctober 2008. Following this moment, the CDS of banks startedexperiencing an upward trend accompanied by an increase of sover-eign CDS too. In the same line, Van Riet (2010) confirms that the con-sequences of the rescue packages on sovereign solvency were onlyvisible on the medium- and long-run (King, 2009). Thus, despite abrief positive outcome, the negative impact of the different publicmeasures on the sovereign interest rates seemed to overtake theirshort-term success in restoring confidence in the banking systems(Acharya et al., 2011; Hirakata et al., 2009; Van Riet, 2010). Moreover,the phenomenon was emphasized in the Euro zone by very high mar-ket volatility as well as by significant political and contagion riskswithin the zone (De Santis, 2012; Di Cesare et al., 2012; Metiu, 2012).
The fiscal transfers resulting from these interventions are also veryimportant as they impacted the public sector deficits and the publicdebt level (Blundell-Wignall and Slovik, 2011; European Commission,2009b, 2009c; Laubach, 2003). In the 2009 annual report, the EuropeanCommission (2009c) establishes that, in addition to the budgetary issueslinked to the financial and economic crisis per se, rescue measures likebanks' recapitalization may have contributed to the deterioration ofthe public financial position of several countries. Another EuropeanCommission report (2009a) also describes the way capital injectionsas well as guarantees and purchase of assets are recorded, in order toexplain the mechanisms by which these interventions may impact thedebt level of a country. First of all, the government may either issuedebt or use its stock of other financial assets like deposits at the ECBto finance banks' recapitalization. If the government chooses the firstsolution, its debt level should increase directly. The second choicewould have an indirect effect; by reducing the overall liquidity thepublic debt needs for future projects would increase. According to theEuropean Commission, governmental guarantees should also have adirect impact on public debt as they represent a contingent liability.The European commission considers that guarantees have to be takeninto account due to the risk they represent if the debtor finally endsup in default. Moreover, the European commission also emphasizesthe risk of guarantees for public finances linked to the increase ofmoral hazard they may induce (Corsetti et al., 1999). Finally, the reportalso underlines the fact that, in the specific context of a financial andeconomic crisis, governments have to account for losses linked totheir purchases of banks' assets whenever the value of these assetsgoes significantly below the purchase price. These potential lossesmost likely increase the sovereign debt level. Overall, these technicalreports emphasize the link throughwhich government rescue packagesmay damage the debt level of a country. As such, investors may changetheir perception of the sovereign creditworthiness and ask for higherrisk premia because they already anticipate that the sovereign willhave difficulties to refund its debt in the future. This may lead to anultimate increase in long-term sovereign yields (Ardagna et al., 2007;
Attinasi et al., 2009; ECB, 2009; European Commission, 2009b; Hirakataet al., 2009; Van Riet, 2010). Finally, Gerlach et al. (2010) show evidencethat the size of the banking sector is positively correlated with the in-crease of sovereign yields during a crisis. In a countrywith an importantbanking system, the government may be required to provide financialsupport to a larger number of banks, damaging even more its publicfinances. On the other side, in countries like Greece, characterized byan underdeveloped banking system, the problems may come from alack of transparency and a high level of corruption which may explainthe increase in their sovereign yields.
Last but not least, when analyzing ECB's interventions, the conclu-sions should be interpreted very carefully. First of all, the opacity aroundthese different interventions leads to important difficulties when tryingto collect the exact data. In his analysis of central banks' transparency,Diamondopoulos (2012) shows that the ECB, in comparison to theFed, the Bank of England and the Bank of Japan, is viewed by themedia and the financial markets as being the less transparent. One ofthe main arguments set forward is based on the role of institutions.The decision making process on issues of monetary policy in the Eurozone is long due to the differences between itsmembers and the impor-tance of national banks which makes the ECB highly decentralized(de Haan et al., 2005). Therefore, assessing the exact timing andsize of the ECB interventions is difficult (Diamondopoulos, 2012).Second, in terms of objective, the Fed and the ECB do not follow thesame path. As such, applying the IMF methodology that involves in-cluding the ECB interventions in our modeling framework may beless relevant; indeed, the ECB follows an inflation objective whilethe Fed aims at supporting employment, managing prices as wellas long-term interest rates (Diamondopoulos, 2012).
3.3. Panel data regressions
Our methodology is based on panel data analysis. Preliminary testswere performed to assess the stationarity of our series and the appro-priateness of a panel data approach. Two generations of testswere iden-tified based on their assumptions (Barbieri, 2006; Hurlin and Mignon,2005). The first generation of tests includes the Levin et al. (2002)(LLC) test, the Im et al. (2003) (IPS) test and the Fisher-type tests(Choi, 2001; Maddala and Wu, 1999) derived from the classical Aug-mented Dickey & Fuller (1981) and Phillips and Perron (1988) tests.They all consider that cross-sections are independent, i.e. no link be-tween movements in one country and the others. The second genera-tion of tests, including the Pesaran (2007) test, takes into account thepossibility of dependence between cross entities (Barbieri, 2006;Hurlin and Mignon, 2005). The independence assumption is the mainlimit of the first generation tests as the literature underlines the poten-tial interaction between the different economic variables (Banerjee etal., 2005).
Once the stationarity of our data assessed based on the two gener-ations of tests, we check for the possibility to use a panel approachgiven our sample. First we analyze whether the use of a panel meth-odology is appropriate and if so, which type of model best describesour dataset. We follow the methodology suggested by Hsiao (2003)and start by performing the specification tests on the homogeneityof our data.
We finally conduct our tests using the Generalized Method of Mo-ments (GMM) approach, based on the Arellano and Bond (1991)model, first introduced by Holtz-Eakin et al. (1988) and check forthe robustness of our result by running the Pooled Mean Group(PMG) estimation method.
4. Empirical evidence
We start our analysis by running unit root tests as stated previously.Table 3 presents the results of the unit root tests for panel data.
Table 4Homogeneity tests.
Model 1homogenous panel
Model 2 panel withspecific effects
Model 3 no specificeffects
Fisherstatistic
2.4291 1.3715 5.4734(0.0000)a (0.104648) (0.0000)a
Notes: The figures in parentheses are the probabilities associated to the Fisher statistic.a Significant at the 1% conventional risk level.
Table 3Unit root tests.
Variables IPS test ADF testb PP testb LLC test Pesaran test
Sa −7.035 93.638 185.950 −3.765 −3.992(0.0000)*** (0.0000)*** (0.0000)*** (0.0001)*** (0.0000)***
SK −10.340 140.432 182.672 −10.859 −4.693(0.0000)*** (0.0000)*** (0.0000)*** (0.0000)*** (0.0000)***
Notes: The figures in parentheses are the probabilities associated to the test; ***Significant at the 1% conventional risk level; null hypothesis of unit root.
a First-difference of the series.b Chi-squared distribution.
41L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
The dependent variable is integrated of order 1, while the stockmarket return series is stationary in level.9
Based on these results, we include a lagged value for the depen-dent variable in our model and keep the intervention variables inlevel as follows:
si;t ¼ αi þ ρ si;t−1 þ β CIi;t þ γ CBi;t þ δ Gi;t þ θ SKi;t þ εi;t ð3Þ
where s stands for the spread with respect to the German long-terminterest rate, CI for capital injections, CB for central bank interven-tions, G for guarantees, SK for returns of the representative stock mar-ket index and ε for the residual of each country i in t. We then run thehomogeneity tests required for comparing the performance of indi-vidual regressions to the performance of a pooled regression includ-ing the whole sample of ten countries. The Fisher statistics and theircorresponding p-values are displayed in Table 4.
The results in Table 4 allow rejecting the null hypothesis of a ho-mogenous panel data structure. We can thus conclude that we haveheterogeneity either with the constant or with the coefficients orwith both. To address this issue we run the second test on coefficientsand find that the p-value associated with the Fisher statistic is abovethe three main critical thresholds, i.e. 1%, 5%, 10%. Therefore, we can-not reject the null that we have a panel with specific effects. Whilethe estimators of our regression seem identical for all the countriesin our sample, the constant is different, which could be linked tosome countries' specifics such as the fiscal regime or the credibilityof government interventions. The last test assesses whether thesespecific effects are significant. The p-value associated to this test isbelow the 1% conventional risk level which implies that we can rejectthe null hypothesis of no specific effects. Thus, we can use panel dataanalysis in a model with specific effects in order to assess the influ-ence of government interventions, i.e. CI, CB, G, as well as the move-ments of stock market returns on the interest rate spreads of eachcountry with respect to the German long-term interest rate.10
As we have a panel with cross-section specific effects, we take theorthogonal deviation model in order to eliminate these specific ef-fects as well as potential correlations between the dependent and ex-planatory variables and the error term. Regarding the instruments wehave selected, the best regression (in terms of AIC/BIC criteria) wasobtained with four lags of the exogenous explanatory variables andthe second lag of the dependent variable in addition to a time trend.
9 We do not run unit root tests for the three intervention variables, CI, CB and G. Asthey represent government or central bank interventions, they are similar to dummyvariables in which case unit root tests may seem irrelevant. For each intervention,we specified the exact month of the announcement and the amount the governmentor the central bank decided to inject in order to rescue the banking system. A valueequal to zero stands for no intervention. Due to the extraordinary nature of these res-cue packages, we may consider the absence of an intervention as being the normalsituation.10 The results of a Hausman test to compare the fixed and random effects estimates ofcoefficients favor the fixed effects approach at a conventional risk level of 5% (p-value = 0.026).
Integrating a time trend is justified by the timing European govern-ments chose to intervene to rescue the banking system. Moreover,Arezki et al. (2011) highlight that spillover effects between Europeanmembers are statistically significant. Thus, implemented in a Seem-ingly Unrelated Regression (SUR) framework, cross-section depen-dence of the error terms can be modeled and tested statistically(Pesaran, 2004). The test of Cross Dependence (CD) introduced byPesaran (2004) consists in computing simple averages of pair-wisecorrelation coefficients of OLS residuals from individual regressions.This test is valid under general conditions regarding the number ofcross-sections and the length of the time period. The CD test appliedon the error term of our model shows that we succeeded in removingthe cross-section dependence without deteriorating the performanceof our estimation (the statistic associated to the CD test is 0.5593 witha p-value of 0.5760).
Table 5 depicts the results of our GMM panel estimations. We testwhether government interventions after the subprime crisis have animpact on sovereign debt cost and if so, we analyze the link betweenthe different variables.
Before interpreting our results we have to underline that the valueof the Hansen J-statistic associated to our panel GMM estimations isequal to 34.6340, with an associated p-value of 0.9999 at the 5% con-fidence level. It implies that the number of instruments selected forthe regression does not deteriorate the estimations. We can thus con-clude that our estimations are robust.11
Table 5 shows that all the explanatory variables are significant ex-cept for CB. More specifically, there seems to be a positive correlationbetween the level of interest rate spreads and their retarded valueswhich is equivalent to saying that investors build their anticipationson the long-term perspective of sovereign risk by taking into accountthe risk premium of the preceding month. The second significant var-iable is the return of stock market index which seems to negativelyimpact the interest rate spread.12 When the return of the stock mar-ket decreases by 1%, the risk premium on sovereign bonds increasesby 3.30%. Indeed, a bear market sends negative signals about the eco-nomic environment. This in turn impacts the sovereign debt risk; ifthe growth perspectives are negative, the public budget will neces-sarily be impacted. As such, revenues from taxes will decrease, ex-penses to boost the economy will explode, with a final impact onthe level of the public debt (Panetta et al., 2009). Moreover, Panettaet al. (2009) also set forward the hypothesis that, based on the CDSpremia and stock prices comparison, there was a wealth transferfrom shareholders to creditors due to the introduction of the differentrescue plans. The reasoning behind this argument is that banks'recapitalization is a synonym for the dilution of shareholder poweras the government becomes a larger/the largest shareholder of therescued banks.
11 Given the fact that UK is the country in our sample outside the Euro zone thereforeimplementing a monetary policy that may present some specificities, we perform thesame estimations on a panel that excludes the UK. The results remain strictly the same.12 A clear conclusion about the impact of SK on yield spreads would require a test toassess the direction of the causality between the two variables. This is the subject of afurther development of this work. We thank an anonymous referee for pointing outthis issue.
Table 5Panel GMM results.
S(−1) CI CB G SK
Coefficients 1.0358 0.0015 −0.0003 0.0005 −0.0330(0.0000)a (0.0037)a (0.3907) (0.0296)b (0.0000)a
Notes: The figures in parentheses are the probabilities associated to the t-statistics;S states for the interest rate spreads, CI for capital injections, CB for central bankinterventions, G for guarantees and SK for stock market returns.
a Significant at the 1% conventional risk level.b Significant at the 5% conventional risk level.
42 L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
In order to deepen our analysis on our main question concerningthe impact of rescue plans on the cost of sovereign debt, we use thearguments set forward by Calomiris et al. (2004) when sketchingthe main categories of mechanisms which can be used by a govern-ment to solve a crisis. Thus, in our model, the first variable which ispositively related to the spread is the level of capital injections decid-ed by the governments to recapitalize banks following the subprimecrisis. This first mechanism aims at consolidating financial institutionsexperiencing a confidence crisis (Calomiris et al., 2004). Our resultsshow evidence that a 1% increase in the amount of CI translates intoan increase by 0.15% of the spread. Said differently, the different in-terventions aimed at rescuing banks and the European banking sys-tem as a whole, were interpreted with a lot of caution by marketoperators (both investors and depositors); they even had a perverteffect as they contributed to an increase of the cost of sovereigndebts when a decrease was expected. Calomiris et al. (2004) identifythree potential issues to this mechanism. The first possible explana-tion of capital injections' failure is a misallocation of capital andhence a lack of credibility (Panetta et al., 2009). Capital injectionswere decided immediately after the crisis, in October 2008, in orderto preserve investors' confidence which in turn could have been con-sidered as not allowing enough time to inspect all their consequences.Moreover, they represented huge amounts of capital: euro 124 billionin Germany and sterling 132 billion in the UK. Another issue withcapital injections is the distortion to incentives. Calomiris et al.(2004) argue that banks could be tempted to exploit governmentalhelp the wrong way, by accepting too much risk – as they did beforethe crisis – instead of building up a stronger position. The financialbackup provided by the government thus leads to moral hazard. Fi-nally, Calomiris et al. (2004) study the impact of this measure on tax-payers. When the capital injection is mainly financed by thegovernment, it means an increase in expenses. Therefore, if there isno increase in revenues or a decrease in another expense, the budgetwill be deteriorated. Thus, investors may anticipate that the govern-ment will experience financial difficulties in the future, in terms of li-quidity, and might be forced to borrow money to compensate. This issynonym of sovereign debt increase.
The conclusion is identical for the impact of guarantees provided bythe government to restore confidence and avoid the “credit crunch”phenomenon due to a lack of liquidity. Banks were enforced in theircredit capacities as they benefited from government guarantees againstdefault risk. Our evidence points out that G is positively related to thespread. Said differently, when guarantees are increased by the govern-ment, investors integrate this news as a bad signal regarding the sover-eign debt risk. Guarantees do not seem to have been successful inrestoring confidence among investors. This result is in line withKrugman (1999) who underlines an increase in the moral hazard atti-tude due to guarantees provided by the government. Calomiris et al.(2004) explain that the efficiency of such a measure depends on the in-stitutional environment, its authority, its credibility and its enforcementcapacity. The EU suffers from a lack of credibility. Investors seem to pe-nalize the lack of homogeneity and the presence of huge discrepancieswithin the union. Because of the heterogeneity within the group and acertain lack of coordination, investors may fear that these guarantees
will be embodied in the future debt. We can therefore say that the useof this system of guarantees did not provide an efficient tool againstthe negative effects of the crisis and that their use did not help govern-ments in achieving their objective of “calming down” the market pres-sure, restoring confidence and strengthening the banking system.
The last variable which seems to negatively impact the spread, albeitnot significantly, is the central bank support. The insignificant resultmaybe attributed to the architecture of the Euro system and the difficulty tohave precise data on ECB actions and their targets. The objective of theECB is to deal with liquidity risk and not with solvency risk, which iswhat we test in our model. The majority of the measures implementedby the ECB remain strictly confidential. The opacity surrounding thistype of data may be explained by ECB's attempts to avoid anotherpanic as well as the speculation on several European country sovereigndebts. However, if we were to interpret this negative impact on the sov-ereign risk, we can say that CB includes stimulus economic packageswhich aim at supporting corporate restructuring and sustaining growth.The consequence should be an increase in future revenueswhich in turnis good news for investors. We also estimated our model without thevariable CB and the conclusions about CI and G remain unchanged. Wemay therefore state that CB is not significant in explaining the impactof rescue packages on the cost of sovereign debt.
A general conclusion can be drawn about the different rescue plans.Calomiris et al. (2004) use the concept of “forbearance”when describingthese plans, as their main objective is to provide financial institutionswith time to solve the crisis. This argument may also explain the factthat a lot of rescue plans have been extended. One example is the CBPP,created in July 2009 for a total of euro 60 billion for one year. The aimof this plan was to support the financial market segment responsiblefor funding the banks particularly affected by the crisis. This planwas ex-tendedwith no limitation regarding its amount or timing. It thus becamethe SMP and aimed at dealing with investors' fear of a possible contagionof the Greek crisis to the other Euro members. However, these plans arenot an answer to the issue of providing insolvent institutions with assis-tance because of a potential moral hazard risk. In Europe, countries likeGermany and France did not need to use the whole amount of the differ-ent rescue packages after the crisis. In France for example, from the euro40 billion dedicated to capital injections, only 50% have been borrowedby banks and these loans have already been refunded. On the contrary,countries like Greece, Portugal and Ireland respectively, were in desper-ate need for financial assistance through the EFSF in addition to their na-tional rescue plans. However, instead of using these cash provisions tosolve the crisis, they used it for regular operations in a highly illiquid en-vironment. Our results also highlight another issue linked to the institu-tional environment. As Calomiris et al. (2004) clearly point out, withouta transparent, credible anduncorrupted government, rescue plans cannotbe efficient as there is no authority with an enforcement capacity, able tomonitor the respect of the different established rules. Calomiris andMason (2003) show that a structural reformwith strict rules, market dis-cipline and bank capital regulations may reduce the cost of supporting afinancial system after a crisis. Such reforms could help the Europeancountries experiencing financial difficulties to improve their situationand benefit from forbearance.
The results we get from our dynamic panel estimations confirm thelink between the financial institutions (subprime) crisis and the sover-eign debt crisis. The European governments' interventions aiming atsupporting bankrupt/on the verge of bankruptcy banks had a statisticallysignificant impact on the cost of their debts. The main channels of trans-mission were capital injections, guarantees and stock markets. They allcontributed to an increase in the cost of the European sovereign debt.
5. Robustness check
In order to check further the robustness of our results we performthe same analysis with the Pooled Mean Group (PMG) estimationmethod. As highlighted by the literature, the GMM estimation may
Table 6PMG results.
CI CB G SK
Coefficients 0.00180 0.00029 0.00004 −0.00765(0.0270)a (0.2854) (0.6926) (0.0361)a
Notes: The figures in parentheses are the probabilities associated to the t-statistics.a Significant at the 5% conventional risk level.
AU BE FR GR IR IT NL PT SP UK
IPC X X X XIPEXC X X X X X X XNCAREGRSF X X X X X X X XRSNF X X X X X X X XTB X X X X X XUR X X X X X X XC X X X X X X X X
43L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
suffer from potential correlation between the dependent variable andthe error term even with the first-difference model (Pesaran andSmith, 1995; Sevestre, 2002). Traditional estimation procedures in thecase of pooled data may provide inconsistent estimates of parametersin dynamic panel models except if the slope coefficients are indeedidentical. Moreover, the number of instruments selected to run themodel may influence the results and lead to a model with poor perfor-mances. Finally, having a coefficient above one for the lagged depen-dent variable may be the sign that the model fails in estimatingcoefficients which converge to the true value (Roodman, 2009). There-fore, to assess the efficiency of the estimates discussed above, we ex-press the interest rate spread taken as first difference as a function ofthe three intervention variables and the stock market return seriesusing the PMG estimator. Introduced by Pesaran et al. (1999), thePMG estimator overcomes the limits of the traditional estimationmethods in that it allows the intercepts, short-run coefficients and var-iances to be different between groups while long-run coefficients areconstrained to be the same.13 It is indeed reasonable to expect thatwhile the short-run evolution of the sovereign risk perception for thecountries in our samplemay be different, the long-run equilibrium rela-tionships between the variables under study are similar due to solvencyconstrains, common monetary and/or economic conditions.14 Table 6displays the results of this regression.
Except for G, the results are quite similar to the GMM estimation.CI's coefficient is positive and statistically significant while the stockmarket returns are negatively correlated to the spread difference. Asin the GMM estimation, the variable CB is not statistically significant.The major difference between the results of the two estimation ap-proaches is that the variable G becomes statistically insignificant inthe PMG estimation. The huge credibility issue that affects the major-ity of the countries compositing the European Union linked to the factthat guarantees are not direct liquidity injections may explain thisdifference. For investors, the use of these guarantees by the bankingsystemmay seem unclear and so may also be their maturity and max-imum amount. Therefore, investors might have difficulties arbitragingbetween this insurance against credit crunch and the additional riskfor the sovereign debt induced by this mechanism of guarantees.
An additional feature of the PMG estimation is the group specificestimates which provide information on country-by-country basis.Indeed, while by definition the long-run coefficients are the samefor all the countries, the short-run coefficients may differ. In ourcase, we notice some group specific effects as proxied by the interceptwhile the coefficients for the intervention variables and the stockmarket are similar across countries and on the long- and short-runs.These country-specific effects allow distinguishing countries forwhich the intercept is negative and statistically significant (Austria,Belgium, France, Netherlands and UK) from countries with a positiveand significant intercept (Greece and Portugal) and finally countriesfor which the intercept is statistically insignificant (Ireland, Spainand Italy). There are therefore some specificities in the risk-aversionbehavior when dealing with the public debt of the core EuropeanUnion countries compared to those at the periphery which werealso those experiencing high indebtedness ratios even before thesubprime crisis.
6. Conclusion
The objective of this article is to provide a deeper analysis of thelink between a financial institutions crisis, namely the subprime
13 This is not allowed for example by another traditional estimation procedure, i.e.the Mean Group (MG) estimator that ignores the possibility for certain parameters tobe equal across the groups.14 The LR statistic for equal long run parameters confirms the null (p-value = 0.3799).The error-correction coefficients are all negative, significant andwithin the unit circlewhichconfirms that thedynamic stability conditionworks for all the countries in the sample.Moredetailed results are available upon request.
crisis, and a sovereign debt crisis, namely the European sovereigndebt problems. Following this goal, we built up an extensive samplecontaining the European government interventions in a panel of elev-en countries over the period 2008 to 2011 and then computed amonthly GDP allowing us to express these government interventionsas a percentage of each country's GDP. Finally, we conducted GMMand PMG estimations in a dynamic panel data approach. Our resultsshow a statistically significant link between the amount of capital in-jections provided by the different European governments followingthe subprime crisis to rescue banks and maintain confidence andthe level of long-term sovereign interest rate spreads. Furthermore,ECB interventions do not seem to produce statistically significant re-sults, while the outcome of guarantees is rather inconclusive,depending on the estimation method that is used (GMM or PMG). Afurther development of the paper would be to consider a modelallowing correcting government bond yields for different factors,namely liquidity risk, and then performing our analysis on the sover-eign credit risk premia alone.15
Other contributing factors to the actual uncertainties characteriz-ing the European sovereign debts and implications for the current ac-tions and status of the ECB can be discussed in light of these results.Are the numerous “last chance” meetings and councils organized bythe European leaders to manage the sovereign debt problem an effi-cient answer to the crisis? The reaction of the financial markets tothe different proposals emerging from these meetings can be consid-ered as being at least mitigated, if not worse. Our results underlinethe direct link between stock market returns evolution and sovereignrisk premia, hence the importance of restoring market confidence andinstitutional credibility. Will the different proposed reforms of theECB status and actions change the perceived risk of the Europeandebts? Moreover, we show evidence that direct liquidity injections,and to a certain extent guarantees, deteriorate the risk appreciationof the public debts. We thus argue that a real debate on the role andcontents of such instruments should be opened.
Appendix A. Fully Modified Ordinary Least Squares results
Notes: variables selected by the FMOLS regression to compute the monthly GDP of eachcountry.
15 We thank an anonymous referee for this suggestion.
44 L. Ureche-Rangau, A. Burietz / Economic Modelling 35 (2013) 35–44
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