sovereign debt: empirical facts - princeton university · themselves, therefore, supports our...

Sovereign Debt: Empirical Facts

Mark Aguiar

1 / 41

Nature of ContractsNo Contingency

I Payments are typically contingent only on time, not on thestate of the economy or other potentially relevant variables

I Some exceptions:

I Argentina’s 2005 restructured bonds following the 2001 defaulthad GDP-linked warrants that promised payments based onGDP growth over a stipulated level.

I Same for Greece 2012 and some other restructurings

I Relatively small profile potentially reflects inability to verifygovernment reported GDP statistics and hence open tomanipulation

2 / 41

Nature of ContractsNo Restrictions on Future Fiscal Policy

I Typically payments are not contingent on future actions of thegovernment which may affect risk

I No clause regarding future debt issuances

I No variable rate debtI An exception from the Italian experience here

I An exception from the early US experience are WWI LibertyBonds with convertibility clause

3 / 41

http://www.nber.org/papers/w3135

Nature of ContractsNo Seniority and No Security

I Other than de facto seniority of some official lending (such asIMF loans), sovereign debt has no explicit seniority structure

I Typical contract contains “pari passu” (Latin for “equalfooting”) clause that states no other outstanding or futuredebt takes precedence.

I What this means is open to interpretation, but recent rulingssuggest that bondholders will be paid in proportion to theirholdings.

I See this and this

I Sovereign bonds are not securitized. That is, bonds are notexplicitly or implicitly backed by specific assets or revenuestreams.

4 / 41

http://www.lse.ac.uk/fmg/events/financialRegulation/LFR15L_Buchheit-%28Pari-Passu-Clause-in-Sovereign-Debt-Instruments---Emory-Law-Journal%29.pdf

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2432671

Nature of ContractsLegal Jurisdiction

I Bonds can be issued under foreign law or domestic law

I Das, Papaionnou, and Trebesch (2012) document for a sampleof 43 emerging markets’ external issuances in 2009, roughlytwo-thirds were issued in NY, one quarter in London, and restgoverned by German or Japanese law.

I Even with foreign legal jurisdiction, sovereign immunity limitsability to seek compensation for default.

I Domestic law bonds vulnerable to ex post modification vialegislation

5 / 41

Domestic and Foreign Law: Difference in Spreads

themselves, therefore, supports our assumption that the market generally priced

differences in risk among the bonds.

We now turn to testing our hypotheses on whether legal differences matter for how

bonds are priced. Our dataset includes a single English-law governed bond (GGB EUR

1.027 11-Apr-2016 LON Mid Yield). To control for maturity date, we compare our single

English-law bond with a single Greek-law bond closest in maturity date to the

English-law bond (GGB EUR 3.600 20-Jul-2016 LON Mid Yield) (termed the

‘comparison bond’). Our lack of data on English-law governed bonds, combined with

the likely noise in the mark-to-market data for a thinly traded bond, prevents us from

making conclusive findings on the accuracy of pricing and the role of law in this pricing

for English-law governed bonds. Nonetheless, our single English-law bond provides a

starting point in assessing these relationships. Table 3 reports the average yields for the

different time periods in our study for the English-law and comparison Greek-law bonds

(and Figure 2 depicts the spread between Greek-law and English-law governed bonds

over time).

As reported in Table 3, we observe first that in the pre-crisis period prior to 5

November 2009, the market priced differences between English-law and Greek-law

governed bonds consistent with our hypothesis. The yield for our English-law governed

bond is on average 212.7 basis points lower than the comparison Greek-law governed

bond in the pre-5 November 2009 time period. The yield difference stays relatively stable

at 211.5 basis points on average in the period between 5 November 2009 and just before

the first ratings downgrade (up to 7 December 2009). To provide context, the spread

between Greek 10-year bonds and equivalent German bunds in December–January 2010

was in the 200–230 basis point range.40 It strikes us as unlikely that the market was

treating English-law governed Greek bonds as the equivalent of German bonds, in turn

Table 3. English-law and comparison Greek-law bonds

English-

law bond

Comparison

Greek-law bond

Difference in

basis points

1 June 2009 to 4 November 2009 1.82 3.95 212.7

5 November 2009 to 7 December 2009 1.89 4.11 211.5

8–22 December 2009 1.90 5.12 321.5

23 December 2009 to 13 January 2010 1.91 5.14 323.3

14 January 2010 to 9 April 2010 1.99 6.02 402.6

12–16 April 2010 6.07 7.02 95.5

19 April 2010 to 11 May 2010 6.10 9.85 374.8

12–28 May 2010 6.15 7.87 171.5

40 ‘10-yr Greek/Bund Yield Spread Tightest Since Mid-Dec’ Reuters (2010) 5http://www.reuters.com/article/

idUSLDE60411Z201001054 accessed 23 February 2011.

Stephen J. Choi, Mitu Gulati and Eric A. Posner � Pricing terms in sovereign debt contracts 183

at University of C

hicago on August 13, 2012

http://cmlj.oxfordjournals.org/

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6 / 41Source: Choi, Gulati, and Posner

http://www.ericposner.com/Capital%20Markets%20Law%20Journal.pdf

Nature of ContractsGreek vs. English Law Net Spread

suggesting that our mark-to-market data on the English-law bonds is noisy and

overstating the yield spread. However, even assuming noise, the directionality of the

spread suggests that prior to the initial announcements relating to Greece’s fiscal

problems, investors accepted a lower yield for English-law governed bonds compared

with Greek-law governed bonds. Given the similar maturities between the English-law

and comparison Greek-law bond, the difference in yield is likely due to the greater default

protection afforded through the English-law governed terms.

After the credit rating agency downgrades in the 8 December 2009 to 22 December

2009 period, while the Greek-law comparison bond yields increased, the English-law

bond yields did not increase, leading to an increase in the spread between the two bonds.

While the credit rating agencies did not distinguish between English-law and Greek-law

governed bonds, downgrading Greek debt generally, the market priced a greater chance of

default affecting Greek-law governed bonds compared with English-law governed bonds

at least in the initial periods of the Greek crisis. The spread peaked in the 14 January 2010

to 9 April 2010 period at 402.6 basis points, 191.1 basis points higher than in the

pre-crisis period. To assess the significance of the change in the spread, we computed the

standard deviation of spread between the English law and comparison Greek law bonds

from 1 June 2009 to 31 October 2009, the pre-crisis period. The spread had a standard

deviation of 51.1 basis points during the pre-crisis period. The 191.1 basis point increase

in the spread therefore represented over three times the standard deviation and thus a

change unlikely due to chance. Again, a caution here is that the mark-to-market data on

the English-law bonds is likely to be both noisy and sluggish in responding to market

Figure 2. Spread between Greek-law and English-law governed bonds. This figure is available in

black and white in print and in colour at CMLJ online.

184 Capital Markets Law Journal, 2011, Vol. 6, No. 2

at University of C

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http://cmlj.oxfordjournals.org/

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7 / 41Zettelmeyer, Trebesch, and Gulati, Economic Policy 2013

Nature of ContractsCurrency Denomination

I External debt typically issued in foreign currency like dollars oreuros

I Domestic bonds sometimes also issued in foreign currency(particularly for developing countries)

I Keep in mind: external vs. domestic typically refer to wherebonds are issued, not who holds them

8 / 41

Currency Composition

Euro2006 Yen Pound SDR USD Other

Region

East Asia & Pacific 4.9 25.0 0.6 4.2 64.5 0.7

Europe & Central Asia 22.1 10.3 0.5 4.3 61.3 1.6

Latin America & Caribbean 9.2 5.0 0.5 0.7 83.6 0.9

Middle East & North Africa 24.9 13.0 0.1 1.3 46.4 14.3

South Asia 5.1 12.9 0.4 18.2 62.2 1.1

Sub-Saharan Africa 19.3 5.7 0.7 18.9 45.2 10.2

Income Level

Low 7.1 9.4 1.3 41.8 27.4 12.9

Lower-Middle 10.5 16.4 0.4 6.6 62.8 3.2

Upper-Middle 11.7 6.3 0.5 0.9 79.2 1.4

High 30.6 20.3 1.4 0.2 43.6 3.9

Overall 11.2 11.0 0.5 5.8 68.5 2.9

Table 7: Currency Composition by Region and Income Level

that currency concentration varies both with the region and with the level of income. First,

even though the US Dollar is the most important currency, the level of importance can vary

significantly. For example, in the year 2000, 83.6% of the external debt stock in Latin America

and the Caribbean is denominated in US Dollars, while, for the same year, in Sub-Saharan

Africa only 45.2% of external debt is denominated in US Dollars. Second, there appears

to be some evidence that the external debt stock currency also depends on proximity. In

East Asia and Pacific, the share of debt denominated in Euro and Yen is, respectively, 4.9%

and 25.0%, while in Europe and Central Asia these figures are 22.1% and 10.3%. A similar

explanation may apply to the share of US Dollars in the total debt stock of Latin America and

the Caribbean. In terms of the currency denomination by income level, there are also some

interesting patterns. First, the importance of the US Dollar is only verified for the lower-

and upper-middle income countries. In the case of low income countries the most important

currency is Special Drawing Rights (41.8%) and in the case of high income countries the

difference between the first and the second most important currencies (US Dollar and Euro,

respectively) is much smaller than in the cases of lower- and upper-middle income countries.

22

9 / 41

DefaultDefinition

I Narrow definition: When a debtor violates a contract provision

I Larger definition: Include debt exchanges at unfavorable termsfor the creditors (ex., Greece’s 2012 restructuring)

10 / 41

DefaultFrequency of Default

I Tomz and Wright (2007,2013) construct a database of 176sovereign entities spanning 1820 to 2012.

I Unconditional probability of default is 1.8% per year

I If exclude 1945-1980 (a period with little borrowing), theprobability rises to 2.2% per year

I Debtors spend roughly 19% of the time in default

I Serial defaults: Countries that default tend to default morethan once (Reinhart and Rogoff 2004)

11 / 41

DefaultFrequency of Default

15

20

25

30

30%

40%

50%

60%

mbe

r of C

ountrie

s

ortio

n of Borrowers

Proportion of Borrowers In Default on Commercial Debt (LHS)

Countries With Paris Club Agreement (RHS)

Countries Defaulting on Commercial Debt and With Paris Club Agreement (RHS)

0

5

10

0%

10%

20%

1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Num

Prop

o

Countries Defaulting on Commercial Debt (RHS)

12 / 41Tomz and Wright 2013

DefaultWhen do Countries Default?

I Canonical model implies countries default during recessions

I Tomz and Wright (2007) find modest evidence in this regard:

I 62% of defaults begin when output is below trend

I Average deviation of output from trend at the start of defaultonly -1.6%

I Cross-sectional correlation between output and default statusis -0.08

I Output typically above trend in periods of non-default

I Many instances of sharp recessions without default

13 / 41


Table Two: Historical Relationship between Output and Default

Total

In

Default

Not in

Default

First

Year of

Default

Last

Year of

Default

Year

After

Default

Country-Years (N) 9,244 1,597 7,657 169 160 150

HP smoothing parameter = 400

Country-Years Below Trend (%) 48.8 56.2 47.3 61.5 58.8 47.3

Mean Deviation from Trend (%) -0.1 -1.4 0.2 -1.6 -1.3 0.0

HP smoothing parameter = 100


Mean Deviation from Trend (%) -0.1 -0.9 0.1 -1.7 -0.7 0.3

HP smoothing parameter = 6.25


Mean Deviation from Trend (%) 0.0 -0.3 0.0 -1.5 -0.1 0.5

31



Table Three: Historical Default Rates, by Output Quantiles

HP 400 HP 100 HP 6.25

Size of Shock Cuto¤ Default Default Cuto¤ Default Default Cuto¤ Default Default

Rate Initiation Rate Initiation Rate Initiation

(%) (%) Rate (%) (%) (%) Rate (%) (%) (%) Rate (%)

worst 5% -11.0 32.0 4.3 -9.0 31.5 4.8 -5.5 29.9 5.2

5-10% -7.0 29.3 4.3 -5.7 25.3 3.7 -3.5 20.7 3.9

10-25% -2.8 19.6 2.5 -2.3 18.7 3.0 -1.4 17.7 2.4

25-50% 0.1 15.3 1.3 0.1 15.7 1.3 0.1 14.8 1.4

50-75% 3.0 12.7 1.0 2.5 12.7 0.7 1.5 15.1 1.3

75-90% 6.9 16.2 1.4 5.5 17.0 1.5 3.6 16.6 1.2

90-95% 10.1 15.9 2.4 8.2 16.9 1.9 5.2 20.3 1.5

95-100% 20.6 2.2 22.5 2.4 21.6 1.5

32


DefaultDebt Levels

I Default status not strongly correlated with debt level (Data:1989-2006)

I Spreads positively correlated with debt in emerging economies

7 Appendix A: Tables and Figures

Table 1: Delays and Haircuts

Mean MedianCorrelation with(eT − eT−1) /eT−1

Correlation with(eT+1 − eT−1) /eT−1

Correlation withDebt/GDP

Delay 1 7.4 years 6.0 years -0.26 -0.21 0.03Delay 2 7.6 years 6.7 years -0.26 -0.21 0.03Haircut 1 38% 42% -0.25 -0.23 0.02Haircut 2 38% 42% -0.25 -0.23 0.02

Table 2: Output and Debt Levels Around Default

mean % deviation % of years Debt/GDP (%)from trend e e below trend Mean Median

years in default -0.4 54 87 61years out of default 0.4 43 51 33year before default 1.0 39 52 40year of default -1.3 64 58 45year of settlement -0.2 51 73 50year after settlement 0.1 48 72 47

Figure 1: The Relationship Between Delays and Haircuts

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16 / 41Benjamin and Wright 2009

DefaultDebt Levels

40

Figure 5: Credit Rating and Debt to Revenue Ratios

R

atin

g

Govt. debt over revenues0 1 2 3

5.5

19

Argentin

Australi

Austria

Belgium

Brazil

Canada

China

Costa Ri

Cyprus

Czech Re

Denmark

Dominica

Estonia

Finland

Germany

GreeceHungary

Iceland

Israel

Italy

Japan

Jordan

Latvia

Mexico

Morocco

Norway

Panama

Paraguay

Poland

Portugal

Slovenia

SpainSweden

Trinidad

Tunisia

Turkey

United K United S

17 / 41Eichengreen, Hausmann, Panizza 2005, Pain of Original Sin

DefaultBanking Crises

I Reinhart and Rogoff document that banking crises oftenprecede or coincide with default

I Bailouts as well as drop in revenue associated with financialcrises puts fiscal strain on government

I R&R (2009) document that government debt increases 86percent in three years following systemic financial crisis

18 / 41

RestructuringDuration

I Default period lasts until settlement is reached with creditors

I Tomz and Wright document the mean length of default is 9.9years (7.8 years in post-1970 sample). Median is 6.5 years.

I Substantial heterogeneity: Standard Deviation of duration ofdefault is 10.5 years

19 / 41

RestructuringHaircuts

I Different methods of computing “haircuts.” Need toincorporate impact of maturity changes as well as changes inface value.

I Sturzenegger and Zettelmeyer measure uses discount ratefrom exit-period yields r :

SZ − HC = 1− PDV New Debt(r)

PDV Old Debt(r)

I Alternative is market value of restructured debt relative toface value:

MV − HC = 1− PDV New Debt(r)

Face Value of Old DebtI As pre-default market value less than face value, this measure

overstates losses to creditors20 / 41


I Cruces and Trebesch (2013) document in a sample of 180restructurings spanning 68 countries and 1978-2010

I Mean haircut of 37% with a standard deviation of 27%

I Larger haircuts in Highly Indebted Poor Countries (87%)

I Similar across bank and bond debt

I Smaller haircuts pre-1990

21 / 41


VOL. 5 NO. 3 93CRUCES AND TREBESCH: SOVEREIGN DEFAULTS: THE PRICE OF HAIRCUTS

nonoccurrence that we /nd informative). We were able to gather suf/cient data to compute haircuts on all of these cases, except for the restructurings of Togo 1980 and 1983 (see online Appendix Section A2 for further details). We thus base all sum-mary statistics on a /nal sample of 180 implemented restructurings by 68 countries.

II. Haircut Estimates: Results and Stylized Facts

The dataset and estimates of the 180 deals in our /nal sample reveal a series of insights on sovereign debt restructurings.

A /rst insight is the large variability in haircut size across space and time. Figure 1 plots our estimates of H SZ from equation (2) over time and the respective, in0a-tion-adjusted debt volumes of each restructuring, as represented by the size of the circles.20 The graph illustrates the dispersion in haircuts, which has increased nota-bly since the late 1970s. Recent years have seen a particularly large variation, with some deals involving haircuts as high as 90 percent and others involving haircuts

20 We obtain negative haircuts for a small subset of cases, most of which happened in the /rst half of the 1980s. Negative haircuts typically result from a restructuring in which the interest rate on the new debt exceeds the esti-mated discount rate prevailing at the time. In such cases, any lengthening of maturities will increase the present value of the new debt, instead of decreasing it (note that most deals in the 1980s involved rescheduling only). While these look like bad deals for the government, a successful agreement can buy time and avoid a disorderly default. In severe distress, these bene/ts can outweigh the drawback of accepting a deal at unfavorable terms.

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0

50

100H

airc

ut in

per

cent

1975m1 1980m1 1985m1 1990m1 1995m1 2000m1 2005m1 2010m1

Figure 1. Haircuts and Deal Volumes over Time

Notes: This /gure plots the size of H SZ ( from equation ( 2 ) ) expressed in percentage points across countries and time. The circle size re0ects the volume of debt restructured in 1980 real US dol-lars. Haircuts range from almost nil to larger than 95 percent. The maximum haircut shows a secular rise and the cross sectional dispersion of haircuts increases over time. See footnote 20 for a discussion of the negative haircuts.

22 / 41Cruces and Trebesch AEJ:Macro 2013

RestructuringLonger Default Duration ⇒ Larger Haircut

7 Appendix A: Tables and Figures










ZWE00

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23 / 41Benjamin and Wright

RestructuringCorrelation with Initial Debt and Output

I Size of haircut and length of default uncorrelated with initialdebt levels

I Larger decline in output in year of default ⇒ longer delay andlarger haircut7 Appendix A: Tables and Figures










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24 / 41Benjamin and Wright

RestructuringDoes Default lower Debt Burden?

I Benjamin and Wright document that median and averagepost-default debt-to-GDP ratio is 5 and 25 percent higher,respectively

I Important Caveat: This is face value of debt

I In the Cruces and Trebesch sample only 57 of 180 casesinvolved reduction in face value

25 / 41

Post-Default

I Debtors are excluded from debt markets during default, andmay remain excluded thereafter

I Cruces and Trebesch look at duration between defaultresolution and first loan or bond issuance (or new credit topublic sector)

I Average duration is 2.3 years when haircuts were less than30%, but 6.1 years on average when haircuts were greater than30%

I Spreads after regaining access are increasing in the size of thehaircut

26 / 41

RestructuringTime to Access


h 0 (t) is assumed to be unknown and is left unparameterized. Accordingly, we esti-mate reduced-form models allowing the functional form of the hazard function to be explained by the data. The model is estimated via a partial likelihood function of the following form:

(7) L(β) = ) i=1

n

( exp( β# z i ) __ + j,W( t i ) exp( β# z j ) ) - i ,where W( t i ) = ( j : t j . t i ) denotes the risk set (i.e., the number of cases that are at risk of failure) at time t i . The model can be extended in a simple manner once time varying covariates are included (see Lancaster 1990).

In estimating the model, we rely on the variance correction method proposed by Lin and Wei (1989). This avoids misleading inference in the case of repeated events and is relevant because some countries in our dataset had multiple restructurings and reaccess episodes since 1980. Thereby potential learning effects are also taken into account.

As before, H SZ is the key explanatory variable of interest, while we build on Dell’Ariccia, Schnabel, and Zettelmeyer (2006); Gelos, Sahay, and Sandleris (2011); and Richmond and Dias (2009) in our choice of model speci/cation and control variables. One difference compared to the above is that we now use country rat-ings by Institutional Investor magazine instead of commercial rating agency ratings,

Haircut <30%

Haircut 30–60%

Haircut >60%

0

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0.50

0.75

1.00

Pro

babi

lity

of r

emai

ning

exc

lude

d

0 5 10 15 20

Years after the restructuring

Figure 5. Kaplan-Meier Survival Functions for Duration of Reaccess

Notes: This /gure plots three survival functions for the duration of capital market exclusion, dif-ferentiating by the size of H SZ (smaller than 30 percent, larger than 60 percent, or in between). The sample consists of 67 /nal restructurings from 1980 until 2009. The y-axis denotes the Kaplan-Meier survival estimate for each function, which represents the unconditional, joint probability that countries remain excluded from capital markets up to each year after the restruc-turing on the x-axis. The /gure suggests a positive correlation between haircut size and the prob-ability of remaining excluded for all years considered.


RestructuringPost-Default Spreads


B. Preliminary Data Analysis

We begin with a preliminary analysis of bond spreads. Figure 3 plots monthly post-restructuring spreads for the 27 debt exchanges in our EMBIG sample from 1993 until 2010. Most importantly, the /gure distinguishes between cases with hair-cuts that are higher and lower than 36.7 percent, which is the median haircut in this sample. Instead of showing plain spreads, the /gure plots the spread differential of defaulters over nondefaulters, computed by subtracting the average spread of the nondefaulters in the sample at each point in time from the spread of each defaulter. The advantage of showing the spread differential is that it can mitigate the impact of common shocks, such as the Mexican crisis of 1995 or the Russian default of 1998, and that it addresses the potential endogeneity of restructuring dates.34 The result-ing plot shows a notable difference between low-haircut and high-haircut cases. Restructurings with high haircuts feature much higher average post-restructuring spreads, especially from year three onwards. The differences often surpass 200 basis

34 We cannot rule out the possibility that low haircut countries may have restructured at times when future yields were expected to be lower than when high haircut countries restructured. Note that the /gure looks similar when using nondifferentiated bond spreads of defaulters.

0

100

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0 1 2 3 4 5 6 7

EM

BIG

spr

ead

Years after restructuring

Haircut> 37%

Haircut< 37%

The sample is split at H = 37%, the median haircut among defaulters

Figure 3. Haircut Size and Post-Restructuring Spreads

Notes: This /gure shows that high-haircut countries experience post-restructuring spreads that are about 200 bp higher than low-haircut countries, especially in years three to seven after the restructuring. Speci/cally, the /gure splits the sample in restructurings with higher and lower than median (37 percent) haircuts and plots the respective average post-restructuring EMBIG stripped yield spread (over US Treasury) in event time. The sample goes from 1993 until 2010. To avoid bias, we show the spread differential between defaulters and nondefaulters, as opposed to using the plain spread of defaulters. The differential is constructed by subtracting the average spread of the 23 nondefaulters at each point in time from the spread of the low- and high-haircut group (see text for details). Note, however, that the picture looks very similar when comparing the plain yield spreads of low- and high-haircut defaulters.


Sovereign SpreadsGlobal Risk Factor

I Correlated with output

I Significant excess returns which are priced by US equity andbond risk factors (see Borri and Verdelhan “Sovereign RiskPremia”)

I Correlated with global risk factors like US Equity Markets orVIX

29 / 41

Sovereign SpreadsLongstaff, Pan, Pedersen, and Singleton (AEJ:Macro 2011)

I Look at CDS Spreads for 26 countries spanning Oct 2000-Jan2010 (with gaps)

I The spreads are correlated across countries: The first principalcomponent explains 64 percent of the variation

I Spreads are correlated with global risk factors: The firstprincipal component has a correlation with the US StockMarket of 0.61 and the VIX -0.75

I Regress spreads on local variables as well as a number ofglobal indicators

I US equity and volatility risk premia (the difference betweenimplied and realized volatility) is an important factors

I Local variables explain roughly 40 percent of total explainedvariation in CDS spreads

30 / 41

Sovereign SpreadsMexico

050

010

0015

00EM

BI S

over

eign

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(bp)

-.06

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0.0

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th

1995q1 2000q1 2005q1 2010q1 2015q1date

Quarterly Growth EMBI Sovereign Spread (bp)

31 / 41

Sovereign SpreadsGrowth and Crises

010

2030

4050

Den

sity

-.1 -.05 0 .05Growth

Crisis No Crisis

32 / 41

Sovereign Spreads“Whatever It Takes”

010

2030

Yiel

d

2000m1 2005m1 2010m1 2015m1

Ireland ItalyPortugal GreeceSpain Germany

33 / 41

MaturityInversion of Yield Curve During CrisesBroner, Lorenzoni, and Schmukler Why Do Emerging Economies Borrow Short Term? 73

FIGURE 1. Short- and long-term bond spreads. The figures show bond spreads of three- and twelve-year maturities over time by country. Bond spreads are estimated as the difference between theyields of emerging-market zero-coupon bonds of three- and twelve-year maturity and the yield ofequal-maturity German or U.S. risk-less zero-coupon bonds.

3.1. Spreads and Prices

We first present some descriptive facts about our estimated spreads and prices. Thematurity-τ spread st,τ is defined as the difference between the yield on an emerging-market zero-coupon bond of maturity τ and the yield on a zero-coupon bond of thesame maturity issued by the United States (for dollar denominated bonds) or Germany(for Deutsche marks or euro-denominated bonds). Figure 1 displays the estimatedspread series for each country for three- and twelve-year maturity bonds. First, spreadcurves are on average upward sloping. Second, spreads increase during financial crises.For example, during the crises in Argentina, Russia, and Uruguay, spreads jump tomore than 25% (2,500 basis points). Furthermore, they all increase during the recentsubprime crisis. Third, short-term spreads are more volatile than long-term spreads.

many bonds and liquid markets. Furthermore, to avoid the spurious volatility arising from changes in thesample of bonds used in each week when we calculate spreads, we interpolate the bond prices for the veryfew missing weeks. In this way, we are able to work with a complete panel of bonds from which to derivethe spread curves. The working paper version of this paper shows similar results without this interpolation.

34 / 41Broner, Lorezoni, and Schmukler, JEEA 2013

MaturityInversion of Yield Curve During Crises

9697 98 99 00 01 02 03 04 05 06 07 08 09 10 110

500

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500

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ad (b

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500

1000

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2000

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ad (b

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Mexico

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500

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date

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ad (b

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35 / 41Arellano and Ramanarayanan, JPE 2012

MaturityLTB Prices More Volatile74 Journal of the European Economic Association

FIGURE 2. Short- and long-term bond prices. The figures show bond prices of three- and twelve-yearmaturities over time by country. Bond prices are estimated assuming a coupon rate of 7.5%.

During periods of very high spreads there is an inversion of the spread curve, withshort-term spreads becoming higher than long-term ones.

Figure 2 plots the price of a short-term (three-year) and of a long-term (twelve-year) bond with an annual coupon rate of 7.5% (paid semi-annually).13 The figureshows that the prices of long-term bonds are substantially more volatile than those ofshort-term bonds. In particular, at the onset of crises the prices of long-term bondsfall much more than those of short-term bonds, while during recoveries the prices oflong-term bonds increase much more than those of short-term bonds.

While most existing studies focus exclusively on spreads, this can be quitemisleading. The main reason is that the slope of the spread curve is not directlyinformative of the expected cost of borrowing at different maturities, since the latterdepends on the expected future behavior of spreads. In particular, an inverted spreadcurve during crises does not mean that borrowing long term becomes cheaper duringcrises. During crises spreads on long-term bonds do not go up as much as spreads on

13. For prices and returns, we choose to use coupon-paying bonds because emerging markets almost neverissue zero-coupon bonds. So the pricing errors for coupon-paying bonds are smaller than for zero-couponbonds.

36 / 41Broner, Lorezoni, and Schmukler, JEEA 2013

MaturityShorter Duration of Issuances During Crises

I BLS argue the risk premia on LTB increases more than onSTB

I Document average maturity of new issuances shortens in acrisis86 Journal of the European Economic Association

TABLE 6A. Average maturity.

Dependent Variable: Average Maturity of Issues

OLS IV OLS IV OLS IV

Main EquationCrisis Dummy −3.598∗∗ −10.600∗∗

[1.510] [5.398]3-Year Spread −0.232∗∗ −0.532∗∗

[0.099] [0.246]12-Year Spread −0.455∗∗ −0.584∗∗

[0.198] [0.265]

First StageHigh Yield Index 0.025∗∗∗ 0.488∗∗∗ 0.445∗∗∗

(Instrument) [0.004] [0.080] [0.070]

R-squared 0.122 0.080 0.124 0.125 0.140 0.150Number of Observations 4,773 4,379 4,773 4,379 4,773 4,379

Notes: This table reports ordinary least-squares and two-stage least-squares instrumental variables (IV) regressionsof the average maturity of issues on the crisis dummy and short- and long-term spreads. For the IV regressions,crisis and spread variables are instrumented by the Credit Suisse First Boston (CSFB) High Yield Index, whichis a measure of the average spread on high-yield debt securities in the U.S. corporate sector. The crisis dummycorresponds to crisis definition 1. Variables are semi-annual averages calculated using a 26-week rolling window.All regressions include country dummies. Newey–West standard errors are used to correct for the autocorrelationintroduced in the series by the rolling window. Standard errors are also robust to heteroskedasticity. The spreadvariables are in logs. Standard errors are in brackets.∗∗ Significant at 5%; ∗∗∗ significant at 1%.

We next analyze the average maturity of the bonds issued. We calculate theaverage maturity for each emerging economy by aggregating all issuances over a 26-week rolling window. The explanatory variables are the same as in the analysis ofissuance at different maturities. We include country fixed effects and use Newey–Weststandard errors to account for the lag structure introduced in the dependent variable. Inaddition to ordinary least squares (OLS), we run instrumental variable (IV) regressionsin an attempt to identify the effect of exogenous supply shocks. Our instrument forsupply shocks is the CSFB High Yield Index of the U.S. corporate sector, which istypically used to measure risk appetite. Our identification relies on taking this indexas exogenous to events in individual emerging economies.

The results are reported in Tables 6A and 6B. The OLS regressions show thatepisodes of crises and high spreads are associated with a shortening of the maturityof bond issuance. In addition, this association is substantially stronger when weinstrument crises and spreads with the CSFB High Yield Index. In Table 6B, weinclude the additional macroeconomic controls used in Tables 4B and 5B, showingthat the results are robust to the inclusion of these controls.

The results reported in this section and Section 3 point towards the importance ofsupply factors in determining the maturity structure of emerging economies, especiallyduring crises. First, the fact that excess term premia are on average positive impliesthat long-term borrowing is on average more expensive than short-term borrowing.Second, the fact that during crises the excess term premium increases while borrowing

37 / 41

MaturityShorter Duration of Issuances During Crises

I Arellano and Ramanarayanan (2012) split weeks based onwhether 1-year spread is greater or less than its median value

I Look at average maturity and duration of issuances

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38 / 41

MaturitySpanish Spreads

2004 2006 2008 2010 2012 2014

0

1

2

3

4

5

6

Spanish Bonds -- Spreads over German Bonds

Short H1 YearL Long H10 YearsL

39 / 41

MaturitySpanish Issuances

2004 2006 2008 2010 2012 2014

0

2

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6

8

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0

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Spanish Government Bond Issuances

HIn 2010 Billion Euros, 5 Months Moving AverageL

Short H<1 YearL Long H>10 YearsL

40 / 41

Summary

I Default in bad times, but not always

I Default is frequent and appears to be costly

I Default often coincides with banking crises

I Renegotiation is lengthy

I Haircuts

I Questions:I Why do countries default? (And why do they repay?)

I Why is it costly?

I How is debt priced?

I How are defaults resolved and why does it take so long?

41 / 41

sovereign debt: empirical facts - princeton university · themselves, therefore, supports our...

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