M.Sc. Student: Bucsa Paul Bogdan

Supervisor Professor: Moisa Altar

Academy of Economic StudiesDoctoral School of Finance and Banking - DOFIN

Bucharest, July 2009

DYNAMIC RELATIONSHIP BETWEEN EMERGING SOVEREIGN CDS AND BOND SPREADS AND CO-MOVEMENTS OF CREDIT SPREADS

2

Brief review regarding studies on Emerging Sovereign CDS and bond spreads.

Spreads evolution during the current financial turmoil Long term and short-term relation between Bond spreads and

CDS. Which market leads in price discovery? Co-movements and volatility spillover in emerging sovereign

CDS using a Component GARCH model A short look at the global factors that affect spreads Conclusions

Topics of the dissertation paper

3

Brief Literature review I. CDS and Bond spreads: two measures of the same risk

Although CDS and bond spreads are two measures of the same credit risk, in practice, there are various factors that cause the two to be different such as "cheapest-to-deliver" option in the CDS, the relative liquidity in the CDS and/or bond market, the global market liquidity, bond short-sale restrictions, etc. (Wit (2006), Zhu (2006))

CDS and bond spreads reflect roughly the same risk, the risk of default of the reference entity. They move in tandem in the long -run; a long bond + short risk-free bond+CDS=0 (Ammer and Cai (2007).

DYNAMIC RELATIONSHIP BETWEEN CDS AND BOND SPREADS:

-Long-run equilibrium between CDS and Bond spreads; Blanco et al. (2005), Zhu (2006), Chan-Lau and Kim (2004) show that the theoretical parity relationship between the two credit spreads holds as a long-run equilibrium condition.

-CDS market is the main forum for credit risk price discovery (Blanco et al. (2005), Zhu (2006)) or results are mixed ( Chan-Lau and Kim (2004), In et al. (2006) )

4

Brief Literature review IIDeterminants of bond spreads and CDS:-Global market and country specific determinants of bond spreads (Hartelius et al. (2008), Ebner (2009), Culha et al.(2006), Baek et al.(2005) or of bond market distress( Walti(2009))

-Ratings (as measure of a country's creditworthiness) in relation with CDS and bond spreads (Sy (2002), Hull et al. (2004), Cantor et al.(1996) Gaillard (2009) )

-The high correlation of Sovereign Credit risk and CDS relation to US stock and HY bond markets, global risk premia, international liquidity and trading (Longstaff et al.(2007), Ozatay et al.(2009), Cifarelli et al. (2006))

-Common factor, capturing global financial conditions and also associated effects on the world economy, can account up to 80% of the increase of EM spreads during the turmoil (Ciarlone et al.(2008))- Sovereign emerging Bond spreads in conjunction with an IMF supported program (Eichengeen (2006), Walti(2009))

A single-name credit default swap (CDS) is a bilateral, off-balance sheet agreement between two counterparties in which one party, the protection seller or writer, offers the other party, the buyer, protection or insurance against credit risk on a specific amount of face value of bonds (the notional amount) against a credit event by a third party (reference entity), for a specified period of time, in return for premium payments. In fewer words, a credit default swap (CDS) is an agreement between two parties to exchange the credit risk of a reference entity. (Duffie (1999))

CDS contracts caracteristics and evolution ( Meng et al. (2007), Mengle (2007)) and recovery rates (Singh et al.(2009))

5

Evolution of JPM EMBIG Global Index

Evolution of spreads of Emerging Sovereign bond spreads was affected in a high measure by the financial turmoil; financial markets events such as funds and banks that went bankrupt impacted heavily risk-aversion that reached record-highs in Autumn 2008. Lack of trust between financial institutions led to low desire to lend between the financial institutions and to businesses. Therefore, to revive the trust and lending, CBs lowered interest rates and adopted quantitative easing and started buying long term bonds from the market.

( Index of Bond spreads of Emerging Sovereigns)

6

World CDS spreads evolution

1 year evolution of 5Y CDS spreads for selected Emerging CEE sovereigns:

0

1000

2000

3000

4000

5000

6000

31/0

1/20

06

30/0

4/20

06

31/0

7/20

06

31/1

0/20

06

31/0

1/20

07

30/0

4/20

07

31/0

7/20

07

31/1

0/20

07

31/0

1/20

08

30/0

4/20

08

31/0

7/20

08

31/1

0/20

08

31/0

1/20

09

ROMANIA

HUNGARY

POLAND

CZECH REP

GREECE

AUSTRIA

UKRAINE

RUSSIA

BRAZIL

MEXICO

VENEZUELA

BULGARIA

Evolution of 5Y CDS spreads for selected countries in Latin America and Europe. (Jan 06-Apr 09):

We notice Ukraine and Venezuela have much higher CDS than the other countries, as they are more likely to default

We notice the high degree of co-movements between the countries' 5Y CDS spreads and the sharp increase after Lehman default in Sep 08

7

2 year correlations between Sovereign CDS: High correlation between CDS

There are high correlations between Sovereign CDS spreads. I consider that co-movements are in line with global factors evolutions (such as risk aversion, global economic growth, global liquidity availability…)

The correlations are significant at 1%, with smaller degree between AUSTRIA and RUSSIA/UKRAINE/ARGENTINA

8

Romania Sovereign, 5Y, EUR, Senior, Unsecured,

CDS spreads' evolution (Jan 2006-April 2009).Maximum CDS price of 780.

Evolution of the Bid Yield To Maturity for the Romania 8 ½, 2012 EUR Bond

Evolution of Romania 5Y CDS and Bond spreads

Evolution of the Mid Z-Spread Line for Romania 8 ½, 2012 EUR Bond, 31/01/2006 - 24/04/2009. Record high of 943 bp.

9

Long - run relation and price discovery in Emerging Europe Sovereign debt market. CEE CDS spreads lead their corresponding bond spreads

1. Long-term relationship between CDS and Bond spreads. Cointegration

Relation between the CDS spread and bond spread for Romania

-there is a strong similarity in the pricing of risk between the two markets in times of normal market conditions

-CDS tend to react ( price in the news) first in times of increased risk aversion and reduced optimism

-in times of turmoil, each market's liquidity and imperfections combined with product/market characteristics and high risk aversion made the gap widen…still we can see an adjusting of the spreads to long term equilibrium as the crisis eased

10

Long-term relationship between CDS and Bond spreads. Cointegration

Unit root tests results:

All CDS and bond spread series have unit roots.

  Augmented Dickey-Fuller Philips- Perron

 (t-Statistics) CDS Bond spread CDS Bond spread

BULGARIA -0.643 -0.444 -0.656 -0.190

CZECH REP. -1.031 0.434 -1.087 0.056

HUNGARY -0.501 -0.985 -0.501 -0.529

LITHUANIA -0.760 -1.356 -0.211 -0.305

POLAND -0.691 -0.478 -0.626 -0.612

ROMANIA -0.889 -1.622 -0.804 -1.074

RUSSIA -1.113 0.316 -1.758 0.292

TURKEY -2.277 -2.138 -2.532 -1.887

UKRAINE -0.903 -1.016 -0.792 -0.921

Johansen Cointegration Rank Test results:

As per our expectations, there is a long term strong relation between CDS and bonds spreads, as they are cointegrated

  CDS and bond spreads

BULGARIA 33.099**

CZECH REP. 46.835**

HUNGARY 85.499**

LITHUANIA 49.437**

POLAND 26.544**

ROMANIA 29.894**

RUSSIA 31.049**

TURKEY 43.148**

UKRAINE 39.084**** Trace test indicates 1 cointegrating eqn(s) at the 0.05 level

*, **, *** shows significance at 10%, 5% and 1% confidence level

11

  Null

Hypothesis: CDS do not cause Bond spreads Bonds spreads do not cause CDS

  LAGS F- statistics p-values F- statistics p-values

BULGARIA 1 69.109 0.000 1.485 0.223

  5 27.477 0.000 2.350 0.039

  10 14.665 0.000 6.089 0.000

  20 12.539 0.000 7.963 0.000

CZECH REPUBLIC 1 59.083 0.000 2.186 0.140

  5 27.486 0.000 2.262 0.047

  10 23.850 0.000 3.207 0.000

  20 16.674 0.000 2.012 0.005

HUNGARY 1 139.207 0.000 19.304 0.000

  5 65.454 0.000 17.177 0.000

  10 40.121 0.000 11.138 0.000

  20 24.066 0.000 9.797 0.000

LITHUANIA 1 76.288 0.000 17.451 0.000

  5 42.434 0.000 9.308 0.000

  10 29.809 0.000 8.566 0.000

  20 20.405 0.000 8.197 0.000

Short-term relation CDS- Bond spreads. Price discoveryGranger causality approach (I)

CDS market dominates the bond market at low number of lags. No single market dominates a high number of lags

12

Short-term relation CDS- Bond spreads. Price discoveryGranger causality approach (II)

POLAND 1 51.352 0.000 0.720 0.396

  5 27.344 0.000 7.552 0.000

  10 30.788 0.000 9.150 0.000

  20 21.120 0.000 7.003 0.000

ROMANIA 1 27.825 0.000 1.580 0.209

  5 16.461 0.000 12.361 0.000

  10 11.305 0.000 13.379 0.000

  20 13.846 0.000 10.675 0.000

RUSSIA 1 12.358 0.000 0.308 0.579

  5 6.632 0.000 4.493 0.000

  10 3.949 0.000 4.250 0.000

  20 4.608 0.000 2.179 0.002

TURKEY 1 80.140 0.000 2.057 0.152

  5 63.830 0.000 2.114 0.062

  10 31.813 0.000 3.804 0.000

  20 17.971 0.000 2.441 0.000

UKRAINE 1 2.049 0.153 120.927 0.000

  5 15.856 0.000 27.383 0.000

  10 14.648 0.000 20.243 0.000

  20 11.723 0.000 13.402 0.000

  Null

Hypothesis:CDS do not cause Bond

spreadsBonds spreads do not cause CDS

CDS spreads lead Bond spreads in price discovery

13

A VECM approach to price discovery As the CDS and Bond spreads are cointegrated, a solution to study the price discovery process is by an error correction model. The focus is put on the adjustment coefficients λ1 and λ2. Optimal number of lags is chosen based on SIC and AIC.

The estimated adjustment coefficients λ1 and λ2 measure the degree to which prices in a particular market adjust to correct pricing discrepancies from their long term trend. For example, if λ2 is significantly positive, it implies that the cash market adjusts to remove pricing errors, meaning that, the derivatives market moves ahead of the cash market in reflecting changes in credit conditions. Alternatively, if λ1 is significantly negative, it implies that the CDS market moves after the cash market. If both coefficients are significant, the relative magnitude of the two coefficients reveals which of the two markets leads in terms of price discovery.

Hasbrouck’s model of “information shares” assumes that price volatility reflects new information, and so the market that contributes most to the variance of the innovations is presumed to also contribute most to price discovery. Gonzalo and Granger’s model attributes superior price discovery to the market that adjusts least to price movements in the other market.

The contributions of market 1 (the CDS market) to price discovery are defined by the following expressions:

,Pcds - CDS PRICE

,Pcs - Bond spread

t

p

j

p

j jtCSjjtCDSjtCStCDStCDS ppppp 11 1 ,1,11,101,1, )(

t

p

j

p

j jtCSjjtCDSjtCStCDStCS ppppp 21 1 ,2,21,101,2, )(

22

211221

21

22

22

2122

122

1 2

HAS22

211221

21

22

2

1

12112

2 2

HAS

12

2

GGGonzalo-Granger ratio:

14

Sovereign Error-correction coefficients HasbrouckGranger- Gonzalo

ratio

  t-statistics in brackets lower bound upper bound  

  CDS equation-λ1 Bond equation-λ2      

BULGARIA -0.0057 0.0419 0.98 0.99 0.88

  [-0.59677] [ 6.15003]      

CZECH REP. -0.0003 0.0718 1.00 1.00 1.00

  [-0.02966] [ 10.0333]      

HUNGARY 0.0222 0.0774 na na 1.00*

  [ 2.56504] [ 9.85705]      

LITHUANIA 0.0020 0.0471 na na 1.00*

  [ 0.31025] [ 6.75721]      

POLAND -0.0008 0.0333 0.99 1.00 0.98

  [-0.10134] [ 5.07298]      

ROMANIA -0.0013 0.0387 0.99 1.00 0.97

  [-0.20342] [ 5.12958]      

RUSSIA -0.0097 0.0090 0.77 0.80 0.48

  [-1.76881] [ 3.49200]      

TURKEY -0.0270 0.0904 0.88 0.91 0.77

  [-1.49814] [ 4.69918]      

UKRAINE -0.1270 -0.0204 na na 0.00*

  [-5.91306] [-1.47737]      

           

AVERAGE     0.937 0.950 0.79

Price discovery occurs mainly in the CDS market

15

Co-movements, common volatility trends and spillover effects in the Emerging Countries Sovereign CDS

In this part of the paper we test for the existence of co-movements and for the existing of spillover in the CDS spreads of Emerging Sovereigns.

The countries included in this study (8) are: Romania, Hungary, Poland, Croatia, Russia, Ukraine, Venezuela and Mexico. The data used is daily and ranges from October 2004 to end of March 2009.

The model used is a Component – GARCH model that decomposes variance in a permanent component and in a transitory component. We focus on the permanent component of variance of the daily CDS returns of the above countries.

The assumptions that I have made is that there is a strong connection between daily CDS returns and daily returns in VDAX – implied volatility of DAX. Also, a term that to show there is asymmetry in the way CDS react to good and bad news.

16

C-GARCH estimates resultsCOUNTRY   ROMANIA POLAND HUNGARY CROATIA

VDAX  C  0.152298*** 0.223655*** 0.192438*** 0.084822***

MA(1)  D - -0.166860*** - -0.135738***

TREND INTERCEPT  ω 0.002026*** 0.003710*** 0.002761*** 0.001836***

TREND AR TERM B1 0.965766*** 0.961069*** 0.945350*** 0.942448***

FORECAST ERROR B2 0.094042*** 0.114841*** 0.090915*** 0.094879***

ARCH Term A1 0.237262*** 0.249878*** 0.078142** 0.323778***

ASYMETRIC TERM A3 -0.273672*** -0.246212*** 0.106918*** -0.333885***

GARCH TERM A2 0.437191*** 0.625001*** 0.315171* 0.343039***

A1+A2   0.674453 0.874879 0.393313 0.666817

COUNTRY   UKRAINE RUSSIA VENEZUELA MEXIC

VDAX   C 0.215879*** 0.314503*** 0.205356*** 0.317788***

MA(1)   D - - 0.110286***  -

TREND INTERCEPT ω 0.002976* 0.001817*** 0.001286*** 0.001265***

TREND AR TERM B1 0.988254*** 0.932973*** 0.947119*** 0.971144***

FORECAST ERROR B2 0.149415*** 0.186391*** 0.110995*** 0.035514**

ARCH Term A1 0.105617*** 0.163368*** 0.100987** 0.231196***

ASYMETRIC TERM A3 -0.135944*** -0.165618*** -0.022919 -0.241589***

GARCH TERM A2 0.634826*** -0.011388 0.587979*** 0.698551***

A1+A2   0.740443 0.151980 0.688966 0.929747

*, **, *** shows significance at 10%, 5% and 1% confidence level Full data sample available in Bloomberg and Reuters: 04/10/2004-27/03/2009Bad news's impact is stronger than good news's impact

17

Permanent component of variance

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

01

/02

/20

06

2/2

7/2

00

6

4/2

4/2

00

6

6/1

9/2

00

6

8/1

4/2

00

6

10

/09

/20

06

12

/04

/20

06

02

/01

/20

07

3/2

9/2

00

7

5/2

5/2

00

7

7/2

0/2

00

7

9/1

4/2

00

7

11

/09

/20

07

01

/09

/20

08

03

/05

/20

08

4/3

0/2

00

8

6/2

5/2

00

8

8/2

0/2

00

8

10

/15

/20

08

12

/10

/20

08

02

/10

/20

09

PCR

PHU

PME

PPO

PRO

PRU

PUK

PVZ

We notice interesting similarity between PCV and market risk aversion, proxied bellow by VDAX - German VDAX volatility index

18

Correlation coefficients between permanent components of variance

  CROATIA HUNGARY MEXIC POLAND ROMANIA RUSSIA UKRAINE VENEZUELA

CROATIA 1.00000 0.69403 0.79183 0.74089 0.73841 0.71825 0.73867 0.59267

HUNGARY 0.69403 1.00000 0.60638 0.81711 0.67257 0.65731 0.62869 0.55195

MEXIC 0.79183 0.60638 1.00000 0.70575 0.64206 0.80086 0.85362 0.66288

POLAND 0.74089 0.81711 0.70575 1.00000 0.69759 0.68539 0.65678 0.56027

ROMANIA 0.73841 0.67257 0.64206 0.69759 1.00000 0.60635 0.60866 0.44344

RUSSIA 0.71825 0.65731 0.80086 0.68539 0.60635 1.00000 0.80262 0.65552

UKRAINE 0.73867 0.62869 0.85362 0.65678 0.60866 0.80262 1.00000 0.59862

VENEZUELA 0.59267 0.55195 0.66288 0.56027 0.44344 0.65552 0.59862 1.00000

High correlation between Permanent components

High correlation coefficients between Permanent Components of Variance for the CDS returns between :

-Hungary and Poland, and in general CEE countries

-Ukraine, Russia and Mexico

Lower correlation between Venezuela and the rest, as Venezuela imbalances and default risk are high

19

Principal components analysis for the permanent components of variance.

Correlation of PCR PHU PME PPO PRO PRU PUK PVZ

                 

  Comp 1 Comp 2 Comp 3 Comp 4 Comp 5 Comp 6 Comp 7 Comp 8

                 

Eigenvalue 6.52195 0.51650 0.37397 0.18599 0.15136 0.11421 0.07785 0.05818

Variance Prop. 0.81524 0.06456 0.04675 0.02325 0.01892 0.01428 0.00973 0.00727

Cumulative Prop. 0.81524 0.87981 0.92655 0.94980 0.96872 0.98300 0.99273 1.00000

Included observations: 1159 Sample: 10/04/2004 3/27/2009      

Correlation of PCR PHU PME PPO PRO PRU PUK PVZ    

  Comp 1 Comp 2 Comp 3 Comp 4 Comp 5 Comp 6 Comp 7 Comp 8

                 

Eigenvalue 5.75161 0.68501 0.4844 0.34645 0.24077 0.19017 0.18879 0.11281

Variance Prop. 0.71895 0.08563 0.06055 0.04331 0.0301 0.02377 0.0236 0.0141

Cumulative Prop. 0.71895 0.80458 0.86513 0.90843 0.93853 0.9623 0.9859 1

Principal Components analysis results for the permanent component of variance, sample: 01/01/2007- 3/27/2009

Large degree of co-movements in Permanent components

Full data sample available in Bloomberg and Reuters: 04/10/2004 - 27/03/2009

20

PCA for PCV for Romania, Poland and Hungary

Correlation of PPO PRO PHU   

  Comp 1 Comp 2 Comp 3

Eigenvalue  2.460063  0.358330  0.181607

Variance Prop.  0.820021  0.119443  0.060536

Cumulative Prop.  0.820021  0.939464  1.000000

Principal components analysis for the permanent components of variance (full data set) for Romania, Poland and Hungary shows that first component accounts for a high 82%, showing high similarities in the movements of CDS spreads.

The result is in line with expectations as often the CEE countries are viewed as an homogenous group.

21

Spillover between PCV

In order to test for spillover effects towards and from a country (Romania) we re-estimate the CGARCH model using in the equation for the permanent component of variance for a country the lagged estimated permanent components for each of the other countries

22

Spillover effects in PCVSpillover effects from the permanent component of variance of country j to the permanent component of variance of returns of Romania CDS spreads

From country j to Romania B3 Standard Error Z-statistics Prob.

CROATIA 0.06592 0.024857 2.651738 0.0080***

HUNGARY 0.07715 0.030854 2.500581 0.0124**

MEXIC 0.21403 0.040695 5.259376 0.0000***

POLAND 0.08926 0.027716 3.220585 0.0013***

RUSSIA 0.32854 0.073088 4.495028 0.0000***

UKRAINE 0.00793 0.009950 0.797237 0.4253

VENEZUELA 0.05984 0.014221 4.207734 0.0000***

Spillover effects from the permanent component of variance of returns of Romania CDS spreads to the permanent component of variance of country j.

From Romania to country j B3 Standard Error Z-statistics Prob.

CROATIA 0.0175 0.015651 1.11897 0.2632

HUNGARY 0.0535 0.023821 2.24604 0.0247**

MEXIC 0.1310 0.047687 2.74635 0.0060***

POLAND 0.0428 0.034500 1.24066 0.2147

RUSSIA 0.0126 0.018104 0.69330 0.4881

UKRAINE -0.0049 0.006589 -0.74749 0.4548

VENEZUELA 0.0717 0.026754 2.68054 0.0074***

*, **, *** shows significance at 10%, 5% and 1% confidence level

23

Granger causality between PCV

GRANGER CAUSALITY (FROM LINE TO COLUMN) CROATIA HUNGARY MEXIC POLAND ROMANIA RUSSIA UKRAINE VENEZUELA

CROATIA NA X ** X *** *** X X

HUNGARY *** NA *** *** *** *** *** ***

MEXIC *** *** NA *** *** *** *** **

POLAND *** *** * NA *** *** ** **

ROMANIA *** *** ** X NA *** *** X

RUSSIA *** *** *** *** *** NA X ***

UKRAINE *** * *** *** *** *** NA ***

VENEZUELA *** *** *** *** *** *** *** NA

Granger Causality tests for each pair of countries' permanent components of variance (PCV) show a large number of significant causalities in PCV

*, **, *** shows significance at 10%, 5% and 1% confidence level

•We see that Croatia, a relative smaller country, has less power to influence the variance of other countries, but is influenced by the PCVs of all other countries at 1% significance level. •Mexico, a known country in the market of sovereign debt Granger causes all other PCV but is Granger caused at 1% by the larger countries in this study. •The results show that there is causality from all countries to Romania and from Romania to all countries except towards Venezuela and to Poland.

24

What drives CDS, Bond spreads?

Global factors Country specific factors

•Reserves/ GDP

•Debt/ GDP, FX debt/GDP

•Inflation

•Short term debt

•Current account balance, openness

•Budget deficit

•Industrial production growth

•GDP volatility

•History of default

•Ratings

•Socio-polical stability, democracy

•IMF program

•…

•Risk aversion

•Global liquidity - interest rates

•Global trust in financial system

•Global investment flows

•Global GDP growth

•War/conflicts in region

•Regional developments

•Gold/ commodities/oil prices

•…

25

LIBOR-OIS spread - a very good relation with the financial turmoil

LOIS, the "spread" between LIBOR rates and OIS rates is an important measure of trust in the money markets, considered by many, including former US Federal Reserve chairman Alan Greenspan, to be a strong indicator for the relative stress in the money markets. A higher spread is typically interpreted as indication of a decreased willingness to lend by major banks, while a lower spread indicates higher liquidity in the market. As such, the spread can be viewed as indication of banks' perception of the creditworthiness of other financial institutions and the general availability of funds for lending purposes.

26

OLS estimation results for J.P.MORGAN EMBIG COMPOSITE spread,monthly data observations

Dependent Variable: DLOG (EMBIGCOMPO)

Variable MODEL 1 MODEL 2 MODEL 3 MODEL 4 MODEL 5

RISK AVERSION            

  DLOG(VIX) 0.386365*** 0.379243*** 0.387438*** 0.359051*** 0.377537***

LIQUIDITY-CRISIS            

  DLOG(LOIS)   0.052305** 0.056614*** 0.049021** 0.047967**

INTEREST RATES            

  DLOG(FEDF)     -0.163924*** -0.105944** -0.10309**

ECONOMY            

  DLOG(METALS)       -0.34112***  

  DLOG(IFO)         -1.59115***

             

 Included observations 106 87 87 87 82

 Adjusted R-squared 0.391812 0.453184 0.523958 0.560874 0.578809

*, **, *** shows significance at 10%, 5% and 1% confidence level

27

Few cross-market correlations for Romania CDS

We notice high correlation between Romania 5Y CDS, VDAX volatility index and iTraxx Europe Crossover index

Evolution of the Romania 5Y CDS is similar to the implied volatility of the 1M EURRON options. This is as a result of the fact that global market conditions, especially risk aversion, affected all Emerging markets not only debt market.

Correlations between markets provide tradable (speculation, hedging) oportunities

28

There is a dynamic relationship between CDS and Bond spreads. Price discovery occurs in CDS market

There are co-movements and spillover effects in Emerging Sovereign CDS market

There are important similarities between CEE Emerging Sovereigns CDS spreads

Global market factors such as risk aversion, liquidity availability, creditworthiness, economic sentiment outlook, economic growth impact CDS and Bond spreads movements

Conclusions

29

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Board of Governors of the Federal Reserve System, International Finance Discussion Papers, No 912. Andritzky, J., G. Bannister, and N. Tamirisa (2005), “The Impact of Macroeconomic Announcements on Emerging Market Bonds”, IMF Working

Paper, WP/05/83 Baek, I., A. Bandopadhyaya, and C. Du (2005), “Determinants of market-assessed sovereign risk: Economic fundamentals or market risk

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