Public finance, banking sector and sovereign bond risk premiums in the Eurozone

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Public finance, banking sector and sovereign bond risk premiums in the Eurozone

Nicolas Huchet, Bénédicte Serbini

To cite this version:

Nicolas Huchet, Bénédicte Serbini. Public finance, banking sector and sovereign bond risk premiums in

the Eurozone. GDRE Monnaie, Banque, Finance (30ème), Jun 2013, Poitiers, France. �hal-03124168�

1

Public finance, banking sector and sovereign bond risk premiums in the

Eurozone

Nicolas Huchet

¹

Bénédicte Serbini

²

Based on a panel of Member States of the Eurozone between 2008 and 2013, we focus on the effect of both public expenditure and banking on ten-year sovereign spreads. By using fixed- effects’ estimations and cluster-robust standard errors with a monthly frequency, we highlight that investors kept on scrutinizing fundamentals about debt sustainability during the sovereign debt crisis, despite the importance of risk aversion. In addition to budgetary balance and debt, distances to the stabilizing primary balance and public investment indicate that markets are sensitive to specific items of public expenditures and not only their level, which put the interest of austerity measures into proportion. Then, our findings support the hypothesis of a risk transfer between private and public sectors, which legitimates long-term refinancing operations by the central bank and put the emphasis on the need to go further as regards European integration, especially the single resolution mechanism.

JEL Classifications: F34, G12, E43, E62

Keywords: Interest rate premium, Public investment, Primary Balance, Sovereign debt, Bank credit risk, Liquidity.

1

Nicolas Huchet (Corresponding Author), LEAD, University of Toulon, Campus porte d’Italie, Avenue Roger Decouvoux, 83000 Toulon, Tel : (33) 04 83 36 63 50, Email huchet@univ-tln.fr

2

Bénédicte Serbini, IREGE, University of Savoie, IAE Savoie Mont-Blanc, 4 chemin de Bellevue, BP 80439,

74944 Annecy-le-Vieux Cedex, France, Tel : (33) 04 50 09 24 02, Email benedicte.serbini@univ-savoie.fr

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I. Introduction

The markets for public debt establish the cost of debt and so the price of government bonds.

Following standard theory, asset prices reflect all available information and depend on monetary drivers (including official interest rates and future inflation) and counterparty risk premium. Borrowing rates also include a liquidity premium linked to trade volumes on secondary markets. However, in case of crisis, assets prices may be disconnected from fundamentals. If so, risk aversion and uncertainty become main drivers of sovereign spreads.

So the question we answer is the remaining role of public expenditure and interbank troubles for long-term interest rates during the sovereign debt crisis.

Even though a common monetary policy offers the advantage of ignoring exchange rates matters, the European debt crisis is an interesting topic for empirical research mainly because it may result in the death of redistributive models, or by contrast it may only mean that markets need credible and reassuring information about consistency between public finance management and chosen growth models. The question is all the more important that Member States at the same time reduce (and streamline) debt and deficit ratios and implement measures aimed at enhancing economic growth.

From late 2008 and especially 2010, long-term interest rate premiums (relative to German

benchmark bonds) increase, sometimes dramatically. We explain it from 2008 to 2013 for ten

Member States of the Eurozone, by taking into account both banking sector and public

finance features. Section II depicts related literature and usual findings. Section III presents

monthly data and estimation methods. Section IV highlights interesting results: first, investors

are sensitive to the composition of public finance in times of crisis, by considering the

distance to the stabilizing primary balance, public investment, government balance and

(squared) debt, so that resources reallocations are as important as debt levels. Second,

3

liquidity providing to banks reduces bond market stress but bank credit risk remains negatively related to sovereign spreads, which supports the hypothesis of a risk transfer.

Indeed, the central bank may reduce risk aversion but resolving solvency troubles is necessary to restore confidence, hence the need to go further as regards the single resolution mechanism.

Section V concludes.

II. Related Literature

Debt sustainability is achieved when government finance is not supposed to be drastically adjusted in the near future. So two issues at least derive from the inter-temporal budget constraint: short-term refinancing (liquidity) and long-term repayment ability (solvency), which in turn define the duration of sovereign debt (Hatchondo and Martinez, 2013).

Regardless the reason, a rise in debt burden that is superior to the rise in GDP implies a decrease in primary balance as there is a ceiling for public deficit (i.e. 3 per cent in the Eurozone), hence a lower deal of control in public spending. That is why the later structural fiscal adjustments

³

are, the more painful they get. Fiscal consolidation is thus necessary, but at the same time austerity measures may have a severe impact in terms of unemployment, especially if fiscal multiplier is underestimated (Blanchard and Leigh, 2013). That is also why it is important to better understand determinants of long-term bond risk premiums, assuming that they bear no more relation to the fundamentals if confidence is lost.

Many indicators aim at stabilize debt-to-GDP ratio, usually through primary balance and tax rate (Blanchard, 1990). That is why Borgy et al. (2011) use expected changes in debt-to-GDP

3

Structural adjustments refer to reforms imposed to countries under assistance (Greece, Portugal and Ireland) by their institutional creditors (European Commission, European Central Bank and International Monetary Fund).

Basically, it is recommended to restructure fiscal policy and to reduce public spending, to increase markets’

flexibility and to strengthen international competitiveness.

4

ratios for each country in order to assess fiscal sustainability and then sovereign bond yields.

On the basis of projections of public debt ratios including age-related spending, Cecchetti et al. (2010) find that “the path pursued by fiscal authorities in a number of industrial countries is unsustainable” and advocate drastic measures both to support monetary stability and long- term growth. Indeed demographic ageing put additional pressure on public finances in many countries, as shown by Rother (2012).

Barrios et al. (2009) and Manganelli et al. (2009) explain that the high degree of risk aversion in the Eurozone also appears as a key factor of bond yield spreads. A refinement is yet necessary because the lack of confidence can be interpreted as a cause of the crisis and at the same time as a result. The work carried out by Attinasi et al. (2010) is thus helpful, by including dummies related to support measures to the banking sector. The model’s ability to forecast is successful, but the study ends in 2009 and cannot encompass recent developments in the European debt crisis.

Over a longer period of time, Seremetis and Pappas (2013) point that the counterparty risk associated to each country is similar in good times, but huge differences appear in bad times as individual macroeconomic fundamentals become closely scrutinized by investors. Still over a long period, Gerlach et al. (2010) show that an aggregate risk factor is a main driver of spreads. In case of increase, the level of equity ratios and the size of the banking sector also play a major role in widening yields spreads, as financial markets expect for bank bailouts.

According to Assman and Boysen-Hogrefe (2012) and Bernoth and Erdogan (2012), such a

(long) study period reveals the need of time-varying coefficient models. So face to

heterogeneous results in the literature (except for global environment), Maltritz (2012) applies

Bayesian Model Averaging to annual panel data over the period 1999-2009, and highlight the

role of fiscal variables (budget balance and government debt).

5

Despite risk and liquidity premium, Dötz and Fischer (2010) find that the third component of spreads, namely the expected loss component, rises during the recent financial crisis, such as sovereign spreads rise too. Alexopoulou et al. (2010) use a dynamic panel error correction model for new EU countries over the period 2001-2008 and highlight the role of fundamentals for the assessment of creditworthiness (by including exchange rates or even trade openness).

Domestic fundamentals but also swings in market perception of sovereign risks are more relevant when countries are characterised by large external imbalances and historically high levels of spreads. Sgherri and Zoli (2009) show that sovereign risk premium differentials tend to co-move over time as they are mainly driven by global risk; however, markets are more and more concerned about specific criteria for countries in the Euro area, including liquidity risk.

The aim of this paper is to provide a similar analysis over the period 2008 to 2013, by

focusing on banks’ involvement and public finance on a monthly basis. Indeed both are

supposed to affect sovereign bond yields and bond risk premiums. In particular, we compute

for each country the distance to the stabilizing primary balance as the level of budget balance

for which debt-to-GDP ratio is constant over time. In order to take into account the role of

public spending for sovereign spreads, we also estimate the share of public investment in

government spending. Indeed the relative shares of investment expenditure and operating

costs may be taken into account by investors during any tender, because it tends to matter for

future economic growth. We also use monetary and banking data to assess the relationship

between banking sector and sovereign spreads.

6

III. Data and Methodology

A. Data

After the financial crisis triggered in 2007 and mostly 2008, economic theory predicts a flight to quality, usually in favour of public claims. At the same time, sovereign bonds are no more considered as risk-free assets (Schuknecht et al., 2011), what may be consistent with market sanction (bail-in) and accountability of Member States. Up to the end of 2009, sovereign spreads increase in the same proportions for all countries and then come back to a low level:

the investors are more risk adverse but they still do not operate huge discrimination among countries. Then, Greece is the first country really concerned by the debt crisis in December 2009, as soon as its budget deficit is updated, and sovereign bond risk premiums sharply increase in the Euro area, especially for Greece, Portugal and Ireland (Table 1). Spanish and Italian premiums move in the same direction, and then this trend is reversed between July 2011 (Ireland) and July 2012 (Greece), though Greek troubles last until summer 2015, and probably more.

Insert Table 1 here

From 2010 to 2012, Greece, Portugal and Ireland accessed International Monetary Fund, European Union (EU) and European Central Bank (ECB) resources. Premiums react strongly when Greece asks for a financial rescue, and after first debates about ways to restructure Greek sovereign debt in July 2011 (Figure 1). Then the decrease in spreads is not coordinated and they remain at a higher level in 2013 than at the beginning of period.

Insert Figure 1 here

So, the dependent variable is the monthly spread between national and German long-term

interest rates. The sample consists of the first countries that adopt the euro on 1st January

7

1999 (except Luxembourg

⁴

): Germany, Austria, Belgium, Spain, Finland, France, Greece (2001), Ireland, Italy, the Netherlands and Portugal. As indicated in the summary table of variables (Annex 1), each (monthly) explicative variable is defined in difference against Germany, the referent country, from January 2008 to December 2013. Indeed, although there is no sign of a debt crisis until 2009, the year 2008 presents a major financial crisis that weakens banks and may play an important role in determining government spreads.

Firstly, depending on the risk profile of each country, a credit risk premium is required. To measure counterparty risk, we use public debt (debt) and budgetary balance (bal). We also calculate the balance that stabilizes debt, in other words the proceeds of previous gross public debt and current growth rate of Gross domestic product. By subtracting the interest burden from this stabilizing balance (i.e. the difference between budgetary and primary balances), we obtain the stabilizing primary balance. Then, the (widespread) gap to the stabilizing balance (denoted effort) is obtained by difference between theoretical and observed balances, i.e. the difference between the stabilizing primary balance and the current primary balance. Another interest variable complements the evaluation of counterparty risk, since we recover (on a quarterly basis) the share of public investment in public spending (gfcf).

Banking variables are also part of sovereign risk in the literature. In case of a decline in the value of public securities, the value of banks’ balance sheets declines too, due to financial reporting standards. In this case, a credit crunch lowers growth, hence a negative feedback effect on future tax revenues and a self-sustaining spiral of higher long-term interest rates. In the extreme case, financial losses reduce levels of capital positions and a recapitalisation scheme reinforces public over-indebtedness

⁵

. We take into account recapitalization and

4

Luxembourg is excluded of the sample as dataset on sovereign spreads is not significant before 2010 (too small government bond market, especially for long-term debt securities with a residual maturity of close to ten years).

5

This is all the more likely, that prudential rules will keep on tightening and especially solvency ratios will be

higher, as EU promised to adopt next recommendations of the Basel Committee on Banking Supervision. So one

can expect a decrease in excess demand for (downgraded) public securities, which is all the more likely when the

8

guarantee schemes over the studied period through a binary variable (recap). We also use time series through the spread between EURIBOR and EUREPO three-month interest rates (borrepo). The maturity is the same but the risk is different because REPO markets are collateralized, so that the variable borrepo indicates banking credit risk. On the opposite, we have not included the yield curve of money markets (i.e. the difference between three-month and one-month or one-day interest rate on unsecured markets) because of excessive correlation with other control or interest variables that could bias our findings.

Secondly, as regards monetary factors, we take into account liquidity provision by the ECB.

As unconventional monetary policy is implemented over the period, main refinancing operations are excluded from dataset in order to take into account long-term refinancing operations. Moreover, assets purchase must be considered as an important part of liquidity providing, so that the best indicator of the monetary policy stance seems to be the excess liquidity (excliq) in the Eurozone. Because of the specific Inflation-targeting framework (given by the 2% threshold), the break-even inflation rate (bir)

⁶

may also inform about the anticipation of a monetary policy tightening or easing. The variable is associated with an ambiguous expected sign as a lower anticipation of inflation may increase spreads, by indicating a recessive scenario or a liquidity trap, but it also may decrease spreads, by implying a lower inflation premium in long-term interest rates or by reassuring market’s participants as regards further monetary policy easing.

Sovereign credit risk, banking and monetary data constitute interest variables of estimations, which are then completed by control variables. So, another component of the determination of

central bank has reached the “zero lower bound”: a possible rise in official interest rates would also reduce the value of securities on secondary markets, notably in banks’ balance sheets.

6

The break-even inflation rate corresponds to the spread between nominal and inflation-linked bonds. It

represents an indicator of inflation expectations as it reflects the overall inflation compensation requested to hold

nominal bonds (the spread includes both the expected level of inflation and a premium to compensate for

inflation risks).

9

spreads is the bond liquidity risk. We calculate a liquidity premium (liq) for each country characterized by a higher borrowing cost than Germany. We do not retain the bid-ask spread, neither the amount of issuances, but we prefer the share of outstanding (fixed-rate denominated) long-term securities issued by each central government, within total outstanding at the end of the month for the whole sample, as do Bernoth et al. (2012). More specifically, we use data on gross public debt, which include the first bilateral loan to Greece (May 2010) and then financial assistance from the European financial stability fund (EFSF)

⁷

to supported countries.

Next, global risk is an important component in works focused on sovereign spreads. As for D’Agostino and Ehrmann (2013), it is measured through the yield differential between BAA and AAA American corporate bonds (spreadba). Indeed, contrary to Haugh et al. (2009) we do not retain the yield spread between high rated short-term securities issued by the non- financial sector and securities with worse signature, which also comes from the Fed, because it is more interesting to take into account foreign financial sector ratings. Alternatively, and as in the literature, we also use the S&P 500 (sp500) index to control the international environment.

As indicators of risk aversion, we do not use the implied volatility given by Vstoxx or VIX indexes, as both are based on equity markets (and excessively correlated with many other variables). Futures contracts listed on the Chicago Board of Trade measures the implied volatility of bond markets, but do not give any significant result compared to the implied volatility of the Eurozone bond market (eurex), which is therefore used to control risk aversion. In the same way, because of the features of the debt crisis, a binary variable (break)

7

The European Financial Stability Facility (EFSF) was the first European temporary crisis resolution

mechanism. The EFSF provided financial assistance to Ireland, Portugal and Greece, through the issuance of

bonds and other debt instruments on financial markets. Then on October 2012 a permanent rescue mechanism

was created and provided loans to Spain and Cyprus: the European Stability Mechanism (ESM), which is

currently the sole mechanism responding to new requests for financial assistance by euro area Member States.

10

is introduced in order to consider the breakdown from which sovereign spreads’ dynamics changes.

B. Methodology

X

1

represents the liquidity risk, X

2

the sovereign risk, X

3

the global risk and the economic environment, X

4

the risk aversion, X

5

monetary variables and X

6

is a breakdown. We estimate the following equation:

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Our sample confirms a partly heterogeneous specification, as intra-individual variability (“within”) is superior to inter-individual variability (“between”), and as the number of periods exceeds the number of observations. A Breusch-Pagan test rejects OLS method, and statistical properties of data do not imply random-effects estimations, so our coefficients are unbiased when using a fixed-effect model, which takes into account the features of the countries in the sample.

⁸

The equation is estimated with a monthly frequency

⁹

from January 2008 to December 2013. Besides common variables, each country-specific variable is defined in difference against Germany, and stationary transformations (growth rates) of all variables (except break) are implemented in order to avoid spurious regressions. As modified Wald tests reveal heteroscedasticity, all specifications of the equation are estimated by robust standard errors. Moreover, each estimate successfully passes the Wooldridge test, which systematically implies the absence of first order autocorrelation.

8

Besides economic analysis, fixed-effects are preferred as the number of periods exceeds the number of individual, even if Hausman tests do not systematically reject random-effects estimations.

9

Variables collected on a quarterly basis (debt, budgetary balance and public investment) are treated by cubic

spline interpolation.

11

With a fixed effects model, the standard errors are only adjusted for clustering at the level of country (in our case, standard errors are adjusted for 10 clusters by country). But with panel data, the residuals may be correlated both for countries and time (Peterson, 2009; Thompson, 2011). Thus, it is useful to conduct additional regressions with double clustered standard errors (by country and by month). Finally, as Greece is excluded from financial markets from May 2010, we implement regressions for the full sample (10 countries) but also for all countries without Greece (9 countries).

IV. Results

A. Baseline model

Initially we do not retain the qualitative aspect of public finances or the banking sector: the purpose is to define the baseline model and to select the better indicator of monetary conditions (Table 2), either inflation concerns (bir) or financial stability issues (excliq).

Not surprisingly, liquidity risk (liq) measured by the share of outstanding amounts of long-

term government securities slightly affects sovereign spreads. International conditions

(measured by the spread between different classes of corporate bonds, by including the

financial sector: spreadba) are strongly and positively associated with European government

spreads, by using fixed effects models but also double clustered standard errors, and with or

without including Greece in the sample. A usual indicator of the international environment

such as the S&P 500 index (sp500) gives the same result, but is not presented because of

excessive correlation together with interest variables (Annex 2) such as interbank spreads

(borrepo) and the breakeven inflation rate (bir).

12

Risk aversion is measured by the implied bond volatility of the Eurozone (eurex), and highlights a statistically significant positive relationship with the dependent variable, as expected. Other indexes based on equity markets, such as the VIX index and the Vstoxx index, are less interesting as regards the need to highlight the functioning of bond markets.

Moreover, the VIW index would be excessively correlated with other variables such as borrepo and spreadba. Additional tests show that the implied volatility of U.S. bond markets (i.e. given by futures contracts listed on the Chicago Board of Trade) is not significant, which confirms the European specificity of the crisis (besides the role for international environment) and the interest to focus on Eurex indexes to assess the changes in risk aversion. Again, this finding is robust, as it appears whatever the method is (even when removing Greece from the sample).

We pay particular attention to monetary conditions as interest variables. The monetary policy

stance is introduced through the breakeven inflation rate of the Eurozone (bir): sovereign

spreads decrease when this variable rises, but the relationship only appears when using fixed

effects method. Such a result is in line with the literature, as expectations of further increases

in consumer prices mean that growth is also likely to increase in the next future, hence a

decrease in sovereign spreads. However, unconventional monetary policies are implemented

over the studied period and the liquidity provision by the ECB includes both higher

refinancing operations (longer term and larger amounts) and assets buyouts. In this context,

the excess liquidity (excliq) given by the ECB is chosen for next estimations: excess reserves

of credit institutions are systematically significant with the expected sign. In a context of debt

deleveraging, liquidity provision is officially used to rescue banks but also directly reduces

strains on sovereign bond markets, giving that the ECB is not allowed to directly help

Member States and that banks are important players on debt markets (including collaterals

faced to credit lines).

13

Finally, a “breakdown” is implemented for each country over the period from May 2010 to December 2011. So, whatever the country risk profile is, investors considerably differentiate the studied countries from May 2010. Despite the aid plan for Greece, this date also corresponds to the massive European recovery plan, with the implementation of the EFSF, and to an important asset-purchase program from the ECB (as in December 2011, with a massive liquidity provision towards banks for an extended period of time of three years).

B. Composition of public finance and banking sector

A new set of tests allows introducing qualitative features of public finances and interbank troubles, still by using both fixed effects and double clustered standard errors (Table 3).

Insert Table 3 here

As regards banking, the three-month Euribor-Eurepo spread (borrepo) represents bank credit

risks as Repo markets are secured by guarantees. We could have expected for a positive sign,

as disturbance on interbank markets indicates strains and then unwillingness or unability to

lend, hence the absence of recovery. On the opposite, the (systematically) negative sign

supports the hypothesis of a risk transfer from banks to public sector, as shown by Attinasi et

al. (2010). Indeed, in spite of the common policy interest rate, money interest rates and long-

term interest rates move in the opposite direction in case of recapitalization scheme. We note

that Euribor-Eurepo spreads are just weakly significant (and only through fixed-effects

models) because of the use of monthly variables and so a loss in information when computing

monthly averages. This pivotal variable complements the excess liquidity (excliq) and

highlights that sovereign spreads are likely to decrease even in a context of banking troubles,

if the central bank eases its policy. Further estimations highlight the absence of significance

for recapitalizations schemes (recap) in any case, as for Barrios et al. (2009). The absence of

14

instruments for weighting rescue packages (with a binary variable) can explain this feature.

Mostly, it is uneasy to assess the effect of rescue packages that clean balance sheets of banks, as the recovery of bank activity may be offset by the increase in sovereign debts. Here, the absence of significance also tends to validate the hypothesis of a risk transfer from banks to Member States.

Additional results related to government spending are obtained for the entire sample but also by excluding the Greek case, whose difficulties are still topical at the summer 2015 (Table 4).

Insert Table 4 here

First, debt levels are weakly significant with the expected sign, but this finding remains true whatever the estimation method and the sample are, which points out robustness of the relationship. Moreover, the squared debt is highly significant. Even if coefficients are low, this remains true by implementing double-clustered standard errors, and when excluding Greece from the sample. In other words, results highlight non-linear reactions face to changes in the risk perception by market operators (Filbien et al., 2013). Budgetary balance (bal) is less interesting, as the positive relationship with sovereign spreads is very weakly significant.

Then, public effort (effort) is measured by the changes in the distance to stabilizing primary balances, and indicates whether markets are interested or not in the composition of public spending, including the debt burden. Despite weak coefficients, estimations confirm that public efforts are granted by lower long-term interest rates on sovereign bond markets, which in turn confirms the existence of thorough debt sustainability assessments even when risk aversion supports high risk premiums.

Finally, the share of public investment over total public spending (gfcf) is supposed to denote

if bond yields consider or not the allocation of expenditure, especially towards public

investment: if so, a higher debt could result in a decrease in spreads, by supporting future

15

economic growth. On the opposite, as indicated by Table 3, public investment is not significant for the whole sample. However, when removing Greece from the sample (Table 4), the relationship becomes significant with unexpected positive sign. First, it is essential to remove Greece from the sample, because of the very specific context of crisis: over the period, the changes in investment expenditure do not reflect choices with regard to public expenditure management, as they depend on ongoing adjustment programs. For the same reason, we note that budgetary balance is not significant when considering Greece in the sample. Second, additional public expenditure systematically increases yield spreads, even by considering only investment expenses: two opposite views can be put forward to account for the positive sign of gfcf. A first reading leads to conclude that any expenditure (operating but also investment expenditure) increases sovereign spreads, which tends to validate the hypothesis of a very short-term horizon of financial markets, contrary to the possibility of exhaustive debt sustainability assessments. However, when subjected to closer scrutiny, rises in public investment rises correspond to economic downturns and to subsequent decreases in public expenditure. In other words, a rise in gfcf does not indicate an increase in public spending but, on the contrary, a recession resulting in a decrease in operating expenditure.

Though this is the most likely scenario, the question whether sovereign spreads are due to recession or austerity cannot be answered.

Overall, this significant variable mainly confirms that the structure of public spending remains

scrutinized by investors during a sovereign debt crisis, except for countries under assistance

that are excluded from financial markets: even in a context of sovereign debt crisis associated

with huge risk aversion, sovereign spreads remain partly driven by fundamentals, and debt

sustainability cannot be summarized as a threshold expressed in percent of GDP. Indeed,

removing Ireland or Portugal from the sample does not change above-mentioned results. It is

16

also possible to conclude that governments seems to have restrain spending in an orderly way, by decreasing unproductive spending faster than public investment.

V. Conclusions

Over the period 2008-2013, we implement a panel model with country fixed-effects to highlight the main drivers of the European debt crisis. Stationary variables prevent from taking into account base effects, for example on government debt or balance. As a consequence, the explanatory power is limited to the main drivers in changes in sovereign spreads, on a monthly basis. Except for public investment, the removal of Greece and the double clustered method act as robustness tests and validate our findings.

As for previous studies on this topic, the increase in risk aversion is the most important driver

of spreads, especially if based on bond markets. Unsurprisingly, the international environment

also explains the crisis, especially if assessed through U.S. corporate bond spreads. Then

unconventional monetary policies decrease sovereign spreads by providing liquidity to

interbank markets through collateral enlargements. Yet, even though impairments on bond

markets are supposed to weigh on banking health, interbank spreads (bank credit risk) are

negatively related to the dependent variable (sovereign credit risk), which is due to a risk

transfer with public sectors. So, the well-functioning of public bond markets depends on

banking, which highlights the role for the central bank face to a sovereign debt crisis (partly

due to recovery plans following the 2007-2008 banking and financial crisis). Giving that

recapitalization schemes do not give any helpful information in our study, such a relationship

between bank and sovereign credit risk also put the emphasis on the importance of the second

key element of the European banking union, namely the Single Resolution Mechanism.

17

As regard public finances, the study reveals that fiscal efforts (measured by the changes in the distance to the stabilizing primary balance) and debt (especially when squared) are highly significant, even in the short-term horizon of financial markets. In other words, despite the need to decrease the level of debt, it is necessary to take into account its composition in terms of primary balance. This finding is interesting as it highlights that such fundamentals keeps on being scrutinized by investors in a context of crisis with strong risk aversion. Here, usual mechanisms are distorted as Greek debt sustainability is also assessed through the ability to satisfy creditors’ requirements. By removing this country from the sample, a positive relationship appears between public investment and sovereign spreads, but the explanation is that downturns and cuts in expenditure (that mechanically result in an increase in gfcf) are accompanied by an increase in sovereign spreads. Overall, we show that financial markets take into account qualitative features of public expenditure and public efforts, and that austerity measures are far from an obvious solution.

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20

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21

Annex 1: List of dependent and explicative variables

Variables (group) Source Frequency Comments

Endogenous variable: government spread relative to German benchmark bonds

spread Thomson

Reuters Daily In basis points. Also see: http://sdw.ecb.europa.eu/reports.do?node=10000049 X1: Liquidity risk variable (in difference against Germany) – Control variable

liq European

Central Bank Monthly Liquidity risk. Outstanding amounts of long-term securities issued by each General government divided by the issuance of the whole sample. Negative expected sign.

X2: Credit risk variables (in difference against Germany) – Interest variables debt (debt2)

Eurostat Quarterly

Gross public debt. Positive expected sign (debt2 is the squared debt, in order to take into account possible non-linear effects: in this case, negative expected sign).

bal Central government budget balance. Positive expected sign.

effort

Eurostat, authors’

calculation

Quarterly Annual

Fiscal effort is the deviation from budgetary primary balance, i.e. the difference between theoretical stabilising primary balance and current primary balance. Positive sign is expected. Annual data only for primary balance, quarterly data otherwise.

gfcf Eurostat, Eu.

Commission Quarterly Public investment in % of public spending. Negative expected sign.

borrepo EURIBOR Daily Spread between unsecured three-month EURIBOR interest rate and collateralized three- month EUREPO interest rate. Ambiguous expected sign.

recap E.U. Daily

The binary variable takes the value “1” when recapitalization schemes, government guarantees or bank restructuring are implemented. State aids are available on the European Commission website:

http://ec.europa.eu/competition/state_aid/newsletter/index.html X3: Global risk and international environment – Control variables

sp500

Federal Reserve

Bank Monthly

Price index of stocks listed on the S&P 500 index (closing price). Negative expected sign.

spreadba Spread between the yield on U.S. corporate BAA bonds and AAA bonds. Positive expected sign.

X4: Risk aversion – Control variable

eurex ECB Monthly Implied bond volatility in the Eurozone (Eurex). Positive expected sign.

X5: Monetary factors – Interest variables

bir French Treasury

Agency Daily

Break-even inflation rate: difference between the April 2019 4.25% French bond and the July 2020 2.25% French bond indexed on future inflation in the Eurozone. Ambiguous expected sign.

excliq ECB Monthly Excess Reserves of credit institutions, Euro area, outstanding amounts, Millions of euros. Negative expected sign.

X6: Dummy variable for structural breakdown – Control variable

break

EFSF;

Hellenic Ministry of

Finance;

French Treasury

Daily Impact of the first aid plan for Greece on each European country in the sample. So for all countries we use “1” from May 2010 up to December 2011.

Except the breakdown, which is a dummy variable, growth rates of variables mentioned above are used in the

estimates. Only the most significant results are reported in Tables 2 to 4. Additional estimations and complete

tests are available upon request.

22

Annex 2. Correlation matrix with statistical significance (* significant at 1%)

spread liquidity debt debt2 bal effort gfcf

gspread 1.0000

liquidity -0.0191 1.0000

debt -0.0017 -0.0012 1.0000

debt2 0.0017 -0.0003 0.9721* 1.0000

bal 0.0838 0.0008 0.0067 0.0054 1.0000

effort 0.0555 -0.0073 0.0051 0.0022 0.0405 1.0000

gfcf 0.0210 0.0048 0.0003 0.0004 0.0088 0.0023 1.0000

borrepo 0.2937* -0.0118 -0.0195 -0.0124 -0.0679 -0.0032 0.0120

recap 0.0706 -0.0603 0.0537 0.0689 0.0102 -0.0175 0.0536

sp500 -0.4139* -0.0065 -0.0308 -0.0288 0.0369 -0.0053 0.0183

spreadba 0.3599* -0.0322 -0.0363 -0.0294 -0.0285 -0.0158 -0.0068

eurex 0.2402* 0.0166 -0.0095 -0.0167 -0.0262 0.0269 0.0246

bir -0.2492* 0.0018 -0.0190 -0.0170 0.0474 -0.0393 -0.0082

excliq -0.1176 0.0090 -0.0066 -0.0052 -0.0081 0.0051 -0.0057

break -0.1337* -0.0378 0.0370 0.0280 0.0619 -0.0370 0.0380

borrepo recap sp500 spreadba eurex bir excliq

borrepo 1.0000

recap 0.0074 1.0000

sp500 -0.5910* -0.0672 1.0000

spreadba 0.6429* 0.0711 -0.6102* 1.0000

eurex 0.3094* 0.0239 -0.1713* 0.2493* 1.0000

bir -0.4724* -0.0615 0.5189* -0.3997* -0.3206* 1.0000

excliq -0.1159 -0.0625 0.0584 -0.0150 -0.1231 0.2751* 1.0000

break -0.0225 0.0147 0.1392* -0.0108 0.0084 0.0197 0.0927

break

break 1.0000

23

Figure 1. Ten-year government bond spreads in Euro area, compared to German benchmark bonds (% points)

Source: ECB

Table 1. Descriptive statistics, ten-year government bond spreads (2008-2013)

Country Average Std dev. Min. Max.

Austria 0,6 0,29 0,19 1,49

Belgium 0,97 0,57 0,22 2,97

Spain 2,14 1,49 0,15 5,55

Finland 0,36 0,15 0,11 0,8

France 0,57 0,33 0,12 1,54

Greece 8,62 7,4 0,37 27,39

Ireland 3,24 2,28 0,22 9,71

Italy 2,11 1,33 0,37 5,19

Netherlands 0,36 0,15 0,1 0,69

Portugal 4,26 3,5 0,28 12,03

Source: ECB, authors’ calculation

24

Table 2: First set of regressions: baseline model, monthly, 2008/1-2013/12 (Endogenous variable: spread)

Full sample Sample without Greece

Fixed effects Double clustered standard errors Fixed effects Double clustered standard errors

Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 liq -0,0040***

(0,0007)

-0,0035***

(0,0006)

-0,0056***

(0,0007)

-0,0051***

(0,0007)

-0,0036*

(0,0022)

-0,0032 (0,0024)

-0,0052**

(0,0024)

-0,0047*

(0,0026)

-0,0039***

(0,0006)

-0,0034***

(0,0006)

-0,0053***

(0,0006)

-0,0048***

(0,0006)

-0,0037*

(0,0022)

-0,0032 (0,0024)

-0,0051**

(0,0025)

-0,0045*

(0,0027) spreadba 0,469***

(0,0327)

0,521***

(0,0341)

0,468***

(0,0328)

0,518***

(0,0344)

0,469***

(0,157)

0,521***

(0,138)

0,468***

(0,166)

0,518***

(0,146)

0,460***

(0,0352)

0,514***

(0,0374)

0,459***

(0,0353)

0,511***

(0,0378)

0,460***

(0,163)

0,514***

(0,140)

0,459***

(0,170)

0,511***

(0,148) eurex 0,194***

(0,0401)

0,206***

(0,0403)

0,197***

(0,0403)

0,210***

(0,0405)

0,194**

(0,0971)

0,206**

(0,0895)

0,197**

(0,0906)

0,210**

(0,0848)

0,196***

(0,0448)

0,209***

(0,0450)

0,199***

(0,0450)

0,212***

(0,0453)

0,196**

(0,0980)

0,208**

(0,0920)

0,199**

(0,0934)

0,212**

(0,0887)

bir -0,182***

(0,0486)

-0,176***

(0,0493)

-0,182 (0,192)

-0,176 (0,192)

-0,190***

(0,0537)

-0,184***

(0,0543)

-0,190 (0,203)

-0,184 (0,204)

excliq -0,0017***

(0,0003)

-0,0015***

(0,0004)

-0,0017***

(0,0002)

-0,0015***

(0,0002)

-0,0018***

(0,0004)

-0,0016***

(0,0004)

-0,0018***

(0,0002)

-0,0016***

(0,0002)

break -5,005***

(0,878)

-4,765***

(0,902)

-5,003 (3,184)

-4,763 (3,164)

-4,462***

(0,770)

-4,202***

(0,787)

-4,460 (3,184)

-4,200 (3,160) Constant 3,954***

(0,0255)

4,162***

(0,0493)

7,049***

(0,535)

7,080***

(0,532)

3,955***

(1,407)

4,163***

(1,420)

7,049**

(2,797)

7,080**

(2,764)

3,758***

(0,0286)

3,977***

(0,0544)

6,517***

(0,464)

6,551***

(0,461)

3,759***

(1,425)

3,978***

(1,439)

6,517**

(2,752)

6,550**

(2,714)

Obs. 710 710 710 710 710 710 710 710 639 639 639 639 639 639 639 639

R2 0,161 0,163 0,178 0,179 0,160 0,163 0,177 0,178 0,159 0,162 0,172 0,174 0,158 0,161 0,172 0,173

Countries 10 10 10 10 10 10 10 10 9 9 9 9 9 9 9 9

Fixed-effects regressions; 10 u. of cross-section. Note: (standard error). *** Significant at 1%, ** Significant at 5%, * Significant at 10%.

25

Table 3: Second set of regressions: public finance and banking, monthly, 2008/1-2013/12, full sample (Endogenous variable: spread)

Fixed effects Double clustered standard errors

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 liq -0,0056***

(0,0006)

-0,0057***

(0,0007)

-0,0052***

(0,0007)

-0,0056***

(0,0007)

-0,0056***

(0,0007)

-0,0052***

(0,0006)

-0,0055***

(0,0006)

-0,0054**

(0,0025)

-0,0054**

(0,0025)

-0,0050*

(0,0026)

-0,0054**

(0,0025)

-0,0054**

(0,0025)

-0,0049*

(0,0025)

-0,0053**

(0,0025) debt 0,0015*

(0,0008)

0,0018*

(0,0009)

bal 0,0049

(0,0031)

0,0049 (0,0031)

effort 7,41^e-05**

(2,38^e-05)

7,44^e-05***

(2,28^e-05)

7,70^e-05***

(2,29^e-05)

7,73^e-05***

(2,21^e-05)

debt2 5,46^e-07***

(5,40^e-08)

6,37^e-07***

(1,61^e-07)

gfcf 0,0001

(0,0001)

0,0001 (0,0001)

0,0002 (0,0001)

0,0002 (0,0001)

borrepo -0,0339*

(0,0181)

-0,0470**

(0,0174)

-0,0339 (0,0710)

-0,0470 (0,0695) spreadba 0,478***

(0,0349)

0,481***

(0,0351)

0,485***

(0,0358)

0,477***

(0,0350)

0,477***

(0,0351)

0,532***

(0,0354)

0,542***

(0,0353)

0,478***

(0,134)

0,481***

(0,132)

0,485***

(0,136)

0,478***

(0,134)

0,477***

(0,134)

0,532***

(0,198)

0,542***

(0,197) eurex 0,203***

(0,0405)

0,206***

(0,0385)

0,199***

(0,0420)

0,203***

(0,0406)

0,202***

(0,0407)

0,208***

(0,0425)

0,214***

(0,0412)

0,203**

(0,0876)

0,206**

(0,0858)

0,199**

(0,0872)

0,203**

(0,0876)

0,202**

(0,0875)

0,208**

(0,0846)

0,214**

(0,0850) excliq -0,0015***

(0,0004) -0,0015***

(0,0004) -0,0015***

(0,0004) -0,0015***

(0,0004) -0,0015***

(0,0004) -0,0016***

(0,0003) -0,0016***

(0,0003) -0,0015***

(0,0002) -0,0015***

(0,0002) -0,0015***

(0,0002) -0,0015***

(0,0002) -0,0015***

(0,0002) -0,0016***

(0,0003) -0,0016***

(0,0003) break -2,642**

(0,992)

-2,859**

(0,943)

-3,214**

(1,034)

-2,639**

(0,984)

-2,652**

(1,004)

-3,173**

(1,025)

-2,637**

(1,0002)

-2,643 (2,792)

-2,859 (2,750)

-3,210 (2,801)

-2,641 (2,788)

-2,655 (2,796)

-3,169 (2,774)

-2,640 (2,767) Constant 4,938***

(0,598)

5,037***

(0,574)

5,608***

(0,635)

4,937***

(0,596)

4,939***

(0,607)

5,592***

(0,631)

4,956***

(0,612)

4,938**

(2,321)

5,038**

(2,285)

5,608**

(2,332)

4,937**

(2,320)

4,940**

(2,321)

5,592**

(2,300)

4,957**

(2,284)

Obs. 690 690 680 690 690 680 690 690 690 680 690 690 680 690

R2 0,170 0,180 0,179 0,170 0,170 0,180 0,172 0,169 0,179 0,179 0,169 0,169 0,180 0,172

Countries 10 10 10 10 10 10 10 10 10 10 10 10 10 10

Fixed-effects regressions; 10 u. of cross-section. Note: (standard error). *** Significant at 1%, ** Significant at 5%, * Significant at 10%.

26

Table 4: Second set of regressions: public finance and banking, monthly, 2008/1-2013/12, without Greece

Fixed effects Double clustered standard errors

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 liq -0,0053***

(0,0006)

-0,0054***

(0,0006)

-0,0049***

(0,0006)

-0,0053***

(0,0006)

-0,0053***

(0,0006)

-0,0048***

(0,0006)

-0,0052***

(0,0006)

-0,0052**

(0,0026)

-0,0053**

(0,0026)

-0,0048*

(0,0027)

-0,0053**

(0,0026)

-0,0053**

(0,0026)

-0,0448*

(0,0026)

-0,0052**

(0,0026) debt 0,0014*

(0,0008) 0,0017*

(0,0009)

bal 0,0056

(0,0032) 0,0056*

(0,0033)

effort 7,50^e-05**

(2,40^e-05)

7,53^e-05**

(2,31^e-05)

7,70^e-05***

(2,28^e-05)

7,73^e-05***

(2,20^e-05)

debt2 5,17^e-07***

(4,72^e-08)

6,30^e-07***

(1,69^e-07)

gfcf 3,76^e-05**

(1,21^e-05)

4,07^e-05**

(1,24^e-05)

4,85^e-05*

(2,74^e-05)

5,16^e-05*

(2,80^e-05)

borrepo -0,0327

(0,0202)

-0,0440*

(0,0192)

-0,0327 (0,0722)

-0,0440 (0,0707) spreadba 0,469***

(0,0379)

0,472***

(0,0380)

0,476***

(0,0388)

0,469***

(0,0380)

0,469***

(0,0379)

0,521***

(0,0379)

0,529***

(0,0367)

0,469***

(0,136)

0,472***

(0,134)

0,476***

(0,137)

0,469***

(0,136)

0,469***

(0,136)

0,521**

(0,203)

0,529***

(0,202) eurex 0,206***

(0,0452)

0,209***

(0,0429)

0,202***

(0,0469)

0,206***

(0,0453)

0,206***

(0,0452)

0,210***

(0,0475)

0,217***

(0,0458)

0,206**

(0,0908)

0,209**

(0,0890)

0,202**

(0,0906)

0,206**

(0,0908)

0,206**

(0,0908)

0,210**

(0,0884)

0,217**

(0,0887) excliq -0,0016***

(0,0004) -0,0016***

(0,0004) -0,0016***

(0,0004) -0,0016***

(0,0004) -0,0016***

(0,0004) -0,0017***

(0,0004) -0,0017***

(0,0004) -0,0016***

(0,0002) -0,0016***

(0,0002) -0,0016***

(0,0002) -0,0016***

(0,0002) -0,0016***

(0,0002) -0,0017***

(0,0003) -0,0017***

(0,0003) break -1,981**

(0,825) -2,234**

(0,785) -2,470**

(0,801) -1,979**

(0,813) -1,969**

(0,819) -2,431**

(0,787) -1,954**

(0,815) -1,984

(2,729) -2,233

(2,685) -2,467

(2,724) -1,982

(2,723) -1,971

(2,727) -2,428

(2,698) -1,956 (2,699) Constant 4,309***

(0,487) 4,437***

(0,466) 4,905***

(0,477) 4,308***

(0,483) 4,309***

(0,486) 4,890***

(0,471) 4,325***

(0,492) 4,309**

(2,188) 4,436**

(2,150) 4,906**

(2,189) 4,308**

(2,187) 4,309**

(2,187) 4,890**

(2,157) 4,325**

(2,152)

Obs. 621 621 612 621 621 612 621 621 621 612 621 621 612 621

R2 0,166 0,180 0,175 0,166 0,166 0,176 0,169 0,166 0,180 0,175 0,166 0,166 0,176 0,168

Countries 9 9 9 9 9 9 9 9 9 9 9 9 9 9

Fixed-effects regressions; 10 u. of cross-section. Note: (standard error). *** Significant at 1%, ** Significant at 5%, * Significant at 10%.