Geoffrey M Ngene

Econometric 6283.

Research paper.

Table of Content Page1. Introduction……………………………………………………………………………..3

2. Literature Review………………………………………………………………………..5

2.1 CDS and Bond Spreads…………………………………………………………………5

2.2 Studies Comparing CDS Rate and Yield Spread……………………………………….6

2.3 Studies on Yield Spreads………………………………………………………………..6

3. DATA AND EMPIRICAL METHODOLOGIES………………………………………..93.1 Sources of Data…………………………………………………………………………..93.2 Empirical Methodology………………………………………………………………….9

4. Empirical results and analysis…………………………………………………………….134.1 Unit root test…………………………………………………………………………….134.2 Long run Relationship and conitegration analysis………………………………………144.3 Short-term dynamic……………………………………………………………………...164.4 Price discovery and Error correction Mechanism………………………………………..17

5.0 Summary and Conclusion………………………………………………………………...20References……………………………………………………………………………………22

Tables

Table 1………………………………………………………………………………………....13Table 2…………………………………………………………………………………………14Table 3…………………………………………………………………………………………15Table 4…………………………………………………………………………………………16Table 5…………………………………………………………………………………………19Table 6…………………………………………………………………………………………20

1

The Price Discovery mechanism in Credit Derivative Markets: Evidence from Sovereign CDS Market

Abstract

The paper sets out to investigate the credit risk pricing by Credit default swap (CDS) and sovereign bond markets in eight sovereigns namely Argentina, Brazil, Chile, China, Mexico, Philippines, Russia and S. Africa. Theoretically, the two markets should be integrated since they price the same credit risk. Therefore, a long run relationship should exist and at equilibrium, arbitrage opportunities should, at least ideally, be non-existence. Using non-stationary, cointegration, granger causality, Vector Error correction models econometric methodologies and a new price discovery measure, I found that all the eight sovereigns investigated have cointegrated CDS and bond markets and long run relationship between the two series exist for the period spanning 2004 to 2009. In six out of eight sovereigns, the CDS market leads bond market in price discovery. This is because informed traders actively participate in CDS market to trade risk and seek liquidity. Moreover, CDS market is less embedded by contract specifications, credit rating, liquidity and tax implications among other factors that mitigate efficiency of bond market in price discovery. The error correction mechanism is properly working in all the eight markets with the right signs

1.Introduction

2

A credit default swap (CDS) is a type of Credit derivatives. CDS is the most popular credit

derivative. CDS generally allow companies to trade and manage credit risks. The CDS market

has grown rapidly since the International Swaps and Derivatives Association produced its first

version of a standardized contract in 1998. A CDS contract seeks to provide insurance against a

default by a particular company or sovereign entity (bond issuer or borrower). The borrower is

called the reference entity who has issued a reference bond. A default by the borrower is known

as a credit event. The CDS buyer (typically a bondholder) makes periodic payments to the seller

until the maturity date of the CDS contract or until a credit event such as bond obligation

default or acceleration, restructuring by reference entity, bankruptcy, credit rating downgrade,

repudiation or moratorium (for sovereign entities) among others, occurs. The protection obligates

the seller of CDS to deliver the reference bond at its par value (buyer receives payoffs) when a

credit event occurs. (Hull, Predescu and White, 2004))

A CDS spread or premium, usually expressed as a percentage (in basis points) of the principal, is

the rate of payments made by the CDS buyer per year. Example: If country x has issued a

sovereign bond with a principal of $100 million and a CDS spread for a 5-year contract on

country x is 450 basis points, then the CDS buyer pays $4500,000 per year to the seller. The

buyer obtains the right to sell the bond at $100 million par to the CDS seller in case of default by

country x or on occurrence of a specified credit event. CDS spreads, unlike bonds, are less

encumbered by covenants and guarantees.

A CDS thus combines a traditional insurance policy with a tradable feature. It offers a number of

advantages to market players. It’s has largely facilitated debt securitization. Longstaff et al

(2007) argue that by using sovereign CDS data, investors can “factor out” the component of

sovereign bond returns due to changes in interest rates and focus instead on the returns due

exclusively to sovereign credit risk. Furthermore, the sovereign CDS market is often more liquid

than the corresponding sovereign bond market, resulting in more accurate estimates of credit

spreads. Blanco et al. (2005) argue that the CDS market also serves as a financial tool for

investors and traders to short the sovereign bonds without any liquidity problem. According to

Chan-Lau and Kim (2004), a sovereign bond spread is a premium paid by an issuing government

to compensate for additional risk. This premium is generally calculated as the difference between

the yield of the risky sovereign bond and the yield of a risk-free bond, such as a US or German

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government bond, or a risk-free market rate such as the London Interbank Offered Rate

(LIBOR). CDS has enabled credit risk hedging among institutional investors who can easily shift

their credit risk exposure to a third party (CDS sellers) who also earn income from insurance

business. CDS is an off-balance-sheet item in the books of the CDS investors. It thus improves

the quality of the investors’ accounting book, which in turn entices them to invest even more in

risky bonds for higher amount of interest incomes.

The CDS rate quotes of each CDS contract provide bond investors with a direct and observable

measure of credit risk premium. Unlike the traditional bond market spread (bond yield less risk

free asset), the CDS market provide a greater opportunity for more accurate valuation of credit

risk. Meanwhile, arbitrage trades between CDS and its corresponding bonds may finally squeeze

out any price disparity between CDS rate and yield spread.

A wide range of institutions participate in the credit-derivatives market. Banks, security houses,

and hedge funds dominate the protection-buyers market, with banks representing about 50

percent of the demand. On the protection-sellers side, banks and insurance companies dominate

(British Bankers’ Association (2002)).

Since mid 2007, the damages of subprime loan defaults have affected both the U.S. and

international financial markets. As the systemic financial crisis ease in 2009, it becomes

increasing imperative to rethink the actual contribution of credit derivative products to bond

investment in terms of information discovery and market efficiency. Is the prosperity in credit

derivative market a panacea or a catalyst for the credit crisis? Can it really help to hedge credit

risk? CDS spreads, unlike bond spreads, do not incorporate any risk-free benchmarks. The CDS

spread (premium) is premised on sovereign bond markets. Therefore, there exist a very close

relationship between sovereign bond spreads and sovereign CDS spreads since Sovereign CDS

spreads directly reflect the market’s assessment of the sovereign’s credit risk.

.From the foregoing questions, this paper examines the pricing information advantage in CDS

market and price information discovery relationship between CDS spreads and yield spreads in

sovereign bonds. This rest of the paper is organized as follows: Section two reviews the

literature. Section three detail data and empirical methodology. Section four explains the results

while section five discusses summary and conclusions.

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2. Literature Review

CDS is a relatively new derivative whose literature is growing fast in an attempt to investigate

the pricing mechanism in CDS market. Over the past decade, credit derivatives market has

grown astronomically from a USD 40 billion outstanding notional value in 1996 to an estimated

USD 8.2 trillion at the end of 2006 (British bankers Association) and eventually to total

outstanding notional amount exceeding 62 trillion dollars by the end of 2008 (Christopher and

Chi (2009))

2.1 CDS and Bond Spreads

Investors holding risky bonds protect themselves against default (credit) risk by buying the

corresponding CDS. Under conditions of no arbitrage, this strategy requires that CDS and bond

spreads are equal. This is premised on the idea that a risky corporate or sovereign bond pays a

risk-free rate plus a constant spread. If the bondholder buys a CDS on the risky bond to insure

against possible default, such a transaction should yield a return equal to risk-free rate plus the

CDS and bond spread differential, or default swap basis. However, the default swap basis should

not exist since the two transactions are equivalent to holding a risk-free bond. If this was to hold

sway, changes between CDS and bond spreads should have a one-on-one mapping (Duffie

(1999) and Hull and White (2000))

According to Chan-Lau and Yoon (2004), there are several factors that cause CDS and bond

spreads to diverge. First is the liquidity in the CDS and/or bond markets. The sovereign bond

market is more liquid than CDS market for sovereign bond issuers. The lower liquidity of CDS

implies that CDS will trade at a higher spread than referenced bond to compensate traders for

liquidity risk. The spread differential between CDS and bonds should remain constant as long as

relative liquidity between the two markets remains unchanged. There is also the potential for

liquidity to migrate from one market to another causing CDS and bond spreads to be different.

Another cause of spread divergence is the “cheapest-to-deliver” option in the CDS contract. This

option allows buyers of a CDS to deliver a bond that is the cheapest/ worth less than the one

referenced in the contract. The CDS/protection seller then faces a bigger loss than originally

anticipated. In the light of this, it is reasonable for the seller to charges a CDS spread that is

higher than the bond spread. The excess spread is a cheapest-to-deliver (CTD) premium and will

generally cause the spread differentials between CDS and bond to widen. However, this

premium should be stable over time since the number of bonds available for delivery remains

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unchanged in the long run. Past research comparing CDS rate and yield spread is more limited.

This research can relate to three groups of past studies

2.2 Studies Comparing CDS Rate and Yield Spread

A number of papers have been written to address the information discovery relationship between

CDS and bond markets. Houweling et al (2001) and Hull et al (2003) find that there is no price

equilibrium between corporate CDS rate and bond yield. They argue that the average price

difference is around 10 basis points. However, Zhu (2006) finds the price discrepancy between

corporate CDS rate and yield spread only exist in a short run, and price equilibrium is observed

in a long run. Longstaff et al (2003) and Zhu (2006) using treasury rate as the benchmark risk-

free find significant differences between CDS spreads and bond yield spreads. The analysis of

Weekly lead-lag relationships between CDS spread changes, corporate bond spreads and stock

returns of US firms in a VAR framework shows that both stock and CDS markets lead the

corporate bond market which render support for the hypothesis that information seems to flow

first into stock and credit derivatives markets and then into corporate bond markets. Blanco et al

(2005) and Ammer and Fang (2007) examine the relationships between CDS premiums and bond

yield spreads for emerging market sovereign borrowers and find that these two measures of

credit risk deviate considerably in the short run due to factors such as liquidity and contract

specifications but a stable long-term equilibrium relationship between CDS and bond spread

exist for most countries. However, Chan-Lau and Yoon (2004) do not find any equilibrium price

relationship among the bond and CDS markets and the equity markets. The results for price

discovery are also mixed.

.

2.3 Studies on Yield Spreads

These studies can be decomposed into two based on models used in yield spread pricing. The

structural models pioneered by Black et al (1973) and Merton (1974); and reduced form models

developed by Jarrow et al (1995). Scholars with affinity for structural models define credit risk

as the outcome of relentless deterioration of a firm’s asset with underlying assumption that a firm

defaults on its obligations if the value of its asset value falls below a specified threshold.

Research by Alexander, Edwards, and Ferri (2000), Longstaff et al (2007) and Powell and Juan

(2008), find that yield curve, stock price, stock price volatility and financial leverage are the

6

most statistically significant factors in determining a firm’s credit risk. However, the overall

explanatory power of these factors is quite small. Collin-Dufresne et al (2000) argues that a large

part of the dynamics of corporate yield spreads cannot be explained by the common variables

identified by Longstaff et al. Amato et al (2003) observe that yield spreads of corporate bonds

tend to be many times wider than what would be implied by expected default losses alone. They

call this phenomenon "credit spread puzzle". The growth of credit risk studies over the years

indicates the complexity and difficulty in measuring and predicting credit risk. . Duffie (1999)

and Hull and White (2000) suggest that in the absence of market friction, arbitrage forces CDS

spreads to be approximately equal to the underlying bond spreads, and the CDS and bond

spreads are positively correlated.

Reduced form models, on the other hand deem default time as a random stopping time with a

stochastic arrival intensity. Previous literatures conclude that taxation, default and liquidity

components are the important factors in determining default risk. Nonetheless, Gruber et al

(2001) claims that the expected default risk can only explains about 25% of the observed credit

spread. In attempt to find the latent factors missing from these standard models, Driessen (2003)

pools all the significant factors from both groups of studies into a single linear regression.

However, no great breakthrough is documented.

In the studies about the credit risk in sovereign bonds, Edwards (1984) and Ming (1998) use

factors in structural models to explain credit risk, and Duffie et al (2000) uses reduced form

models. Both groups fail to find the main force driving the changes in credit risk in sovereign

bonds. Ammer and Fang (2007) find that bond spreads lead CDS premiums for emerging market

sovereigns more often than has been found for investment-grade corporate credits, consistent

with the “cheapest-To-Deliver (CTD) option. This constrains CDS liquidity for riskier

borrowers. Furthermore, the CDS market is less likely to lead for sovereigns that have issued

more bonds, suggesting that the relative liquidity of the two markets is a key determinant of

where price discovery occurs. However, Norden and Weber, (2004) find that stock returns lead

both CDS and bond spread changes while CDS spread changes Granger cause bond spread

changes for a higher number of firms with and without cointegrated credit spreads. The reverse

granger cause was weaker. Moreover, the CDS market is significantly more sensitive to the stock

market than the bond market and the magnitude of this sensitivity increases when credit quality

becomes worse. The contribution to price discovery of the CDS market relative to the bond

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market is substantially stronger for US than for non-US reference entities. Remolona et al (2007)

argue that Sovereign spreads can be decomposed into two components: the expected loss from

default and the risk premium. The latter reflects how investors price the risk of unexpected loss

hence it is less obvious but often the larger part of the spread. The risk premium compensates

investors for the fact that the realized loss from default may exceed the expected loss. Such a

default risk is asymmetric because the possible losses from default are large relative to the

possible gains from an absence of default.

Jarrow et al (2005) claim that a zero default risk premium in a world of risk-averse investors is

only possible if defaults on different bonds are independent and investors are able to diversify

away any idiosyncratic risks by holding a sufficiently large portfolio of bonds. Empirical results

pertaining to presence of sovereign risk premium in a spread and its size are mixed. In the case

of corporate bonds, the empirical evidence points to a rather large risk premium. Driessen (2005)

and Berndt et al (2005) estimates an average premium of 189 basis points after accounting for

tax and liquidity effects

Amato and Remolona (2003) suggest diversifiable and idiosyncratic risks respectively account

for three quarters and for one quarter of the default risk of BBB/Baa-rated corporate bonds. It’s

unclear whether sovereign defaults are more highly correlated than corporate defaults. It can be

argued that idiosyncratic risk is harder to diversify for sovereign bonds because there are fewer

available issues. A trend that is gaining currency among users of structural models is a

presumption that credit spread measure just default risk credit and not risk premia. (See Gapen et

al (2005) Oshiro and Saruwatari (2005), and Diaz Weigel and Gemmill (2006)) on the premise

that risk indicators are highly correlated with market spreads over time.

3. DATA AND EMPIRICAL METHODOLOGIES

3.1 Sources of Data

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The data used for price discovery is monthly sovereign CDS spreads and emerging market bond

index (EMBI) spreads for each sovereign entity. EMBIG is traditionally a market-capitalization

weighted index. The index includes U.S. dollar denominated Brady Bonds, Eurobonds, traded

loans, and local market debt instruments issued by sovereign and quasi-sovereign entities for

debt denominated in U.S. dollars, with a minimum current face value outstanding of US$500

million. (JPMorgan Securities Emerging Market Research (1999)). EMBIG spreads are

frequently used in IMF, NBER, credit trade papers and similar studies. The data is provided by

JP Morgan Chase for 5 year CDS contracts. The monthly data run from beginning of January

2004 to end of October 2009. This results in 70 observations for each of the seven emerging

sovereign entities (Argentina, Brazil, Chile, China, Mexico, Malaysia, South Africa and Russia

except Russia which has 65 observations who’s CDS spread data was available beginning June

2004.

Empirical studies by Chan-Lau and Kim (2004), Zhu (2006) and Ammer and Fang (2007)

employ daily spreads while Longstaff and Singleton (2007), Erdem, Geraldo and Bae (2008)

and Pires, Pereira and Luís (2009) use monthly spreads in their studies. In this study and in line

with the arguments of Longstaff and Singleton (2007), I will use monthly spreads since in

examining long-run relationship between CDS and bond markets using cointegration tests,

monthly frequency appears more robust compared to daily or weekly data.

3.2 Empirical Methodology

The study employs a number of econometric methodologies namely cointegration analysis,

Granger causality tests and vector error correction model (VECM) proposed by Hasbrouck

(1995) and Gonzalo and Granger (1995) to test the equilibrium price relationship and price

discovery between CDS and bond spreads. The cointegration test using trace statistic and

maximum eigen values was proposed by Johansen (1988, 1991) and is frequently used to test the

long-term relationship between non-stationary time series, The econometric methodologies are

broadly dissected into two stages. First, the nonparametric Augmented Dickey-Fuller (ADF) unit

root test and Phillips-Perron (PP) unit root test by Phillips et al (1988) are applied to CDS and

bond spread series to confirm if they are non-stationary. In the second stage, I examine the order

of cointegration for the two variables. Theory predictably stated that the two prices should be

9

equal in the long run since they price the same credit risk. The cointegration, based on the

theoretical premise should be [-1 1] for CDS and bond markets respectively. If this should hold, I

will just require to test if basis (CDS spread-bond spread) is stationary. If both spreads are non-

stationary or of I (1) process, and the basis is stationary, arbitrage opportunity between the two

markets does not exist in the long run.

The price discovery measures are Granger causality tests and vector error correction model

(VECM). Granger and Newbold (1974) showed that the conventional ordinary least squares

(OLS) for analyzing linear relationships between two variables is inadequate when the variables

are non-stationary since OLS would erroneously imply that the variables are closely connected

by a linear equation though completely independent of each other. Cointegration analysis

addresses such shortcomings of OLS in the attempt to determine whether there is an equilibrium

price relationship between two markets. An equilibrium relationship between the variables exists

if the linear combination of the variables is stationary. Stationary series is characterized by a

constant variance of variables whose values have linear combination and are centered on a mean.

The coefficients of the linear combination constitute the cointegrating vector of the cointegrated

variables. Therefore, testing for the existence of an equilibrium price relationship is equivalent to

testing for the existence of a cointegrating equation. The ADF and PP regression equations,

respectively, to determine whether the prices series are characterized by a unit root are as

follows;

Where Δy is the first difference operator, p is the number of lags, and e is an error term. The

inclusion of lagged first-differenced variables in the regression controls for higher order

correlation. In both equations 1 and 2,

H o : β=0 : y t is characterized by unit root (non-stationary )

H 1 : β≠0 : y t is stationary .

If CDS and bond spreads are characterized by unit roots, I will use Johansen (1991)

cointegration rank test to test for the existence of a long term price equilibrium relationship.

The cointegration rank test is performed on a vector autoregression of order p:

10

Δ y t=α+βY t=1+δ1 Δ y t−1+δ2 Δ y t−2+−−−−+δt−p Δ y t−p+et (1)Δ y t=α+βY t=1+et (2)

x t=β 0+β 1 x t=1+β 2 xt−2+β 3 x t−3+−−−−+β t− p x t− p+et (3 )whereX=(2x1 ) vector of vector of CDS and bond spreads,

β 0=(2x1) vector of intercept terms

β i=(2x1) vector of intercept terms

et=(2x1 ) vector of stochastic shocks that may or may not be correlated with each otherThe above is the intial cointegration equation . The cointegration test equation takes the following form .

Δ x t=γ xt−1+∑i=1

p−1

R i Δ x t−1+e t (4 )

For cointegration relation to hold, the term γ x t−1 should be stationary [ I ( 0) ] while matrix γ should be singular (determinant equals zero since rows and columns are linearly dependent ) and have a rank of 1 .Therefore, there exist 2×1 vectors α and β such that γ = αβT , where the vector β is the cointegrating vector andand α is the vector of speed of adjustment parameters .From the foregoing, the following hypothesis is tested .

H 0 : Coefficient matrix γ has a full rank of 2 (CDS and bond spreads are not cointegrated )

H 1 :Coefficient matrix γ has a reduced rank of 1 (CDS and bond spreads are cointegrated )

If this null hypothesis is rejected, then the two price series are cointegrated and I can affirm that

there exists an equilibrium price relationship between them.

If the variables are cointegrated, VECM is appropriate to check for the price discovery

mechanism. Enders (2004), Chan-Lau and Yoon (2004) and Erdem et al (2008) argues that if

linear relationship between two variables is already stationary, meaning that they are

cointegrated, differencing the relationship entails a misspecification error.

Given two series x and y, we say that series x Granger causes series y if past values of x contain

useful information beyond that contained in past values of y to explain the current value of y. The

price discovery between CDS and bond markets is determined CDS and bond spread series using

the following regression form:

Δy t=a1+∑j=1

p

b j Δ y t− j+∑j=1

p

d j−1 x t− j (5 )

Where a is a constant, p is the number of lags, Δ is the first difference operator, and the

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are bj and dj, j = 1,..,p are coefficients associated with the lagged values of series y and x ,

respectively. In the context of equation (5),

H o :d j=0 : x does not granger cause y (no price discovery in market x t

H 1 :d j≠0 : x t Granger cause y . There is preice discovery in market x .

Theory suggests two-way Granger causation. Granger causality tests were performed for 1, 3 and

6 month lags, so that price discovery up to a 6-month horizon could be tested for each sovereign

VECM test the degree to which a variable deviates from the long-run equilibrium over time. In

this case, VECM will be applied to establish the lead-lag relationship between CDS (CD) and

bond (BD) markets.

CD t−βi BDt−γi=υt (6 )

[ ΔCD t ¿ ]¿¿

¿¿

¿

¿

Equations (6) and (7) form the bulwark of VECM. Equation (6) is the cointegrating equation

while Equation (7) is the VECM simultaneous regression equations matrix. The residuals of

equation (6) should be stationary, I(0), for the two markets to be cointegrated. The focal points in

VECM framework are the speed of adjustment parameters, (error correction coefficients) α1 and

α2, which are ideally price discovery measures indicating relative efficiency of the markets. The

comparisons of the signs and magnitudes of these coefficients help in making conclusion

whether the CDS market is leading the bond markets in price discovery and vice versa. The β

matrix indicates the long run equilibrium relationship between sovereign CDS and bond markets.

The estimated adjustment coefficients α1 and α2 measure the degree to which prices in a

particular market adjust to correct pricing divergence from their long term trend. A significantly

positive α1 implies that the CDS moves ahead of the bond market in reflecting changes in credit

conditions. If α2 is significantly negative, it implies that the CDS market moves after the cash

market. If α1 and α2 are significant with correct signs, the relative magnitude of the two

coefficients reveals that price discovery happens in both markets.

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4. Empirical results and analysis

The descriptive statistics (Table 1) shows that Argentina has the highest range and standard

deviation of both CDS and basis spreads (CDS spread less bond spread), underlying the

sovereign risk associated with country. This is supported by the default of 152 sovereign bonds

with $81billion by Argentina government in December 2001 and announcement of harsh

restructured payment plan in 2005 (Shapiro and Pham, (2006)). China has the lowest volatility of

CDS and basis spreads. Of the three variables analyzed, bond spreads has the lowest standard

deviation for all sovereigns. Mexico’s standard deviation of both CDS and basis spread is

essentially identical (84.14 and 84.11 respectively)

Table 1: Descriptive statisticsCountry Obs Variable Mean Maximum Minimum S.deviationSkewness KurtosisArgentina 70 CDS spread 730.97 4071.90 1.50 1069.51 2.06 5.97

bond spread 87.45 130.27 39.37 22.02 -0.14 2.60Basis spread 643.52 4028.24 -75.82 1083.09 2.07 1.57

Brazil 70 CDS spread 242.71 900.20 62.60 175.42 1.57 5.50bond spread 551.92 744.10 352.02 104.45 -0.24 1.97Basis spread -309.21 548.19 -617.71 259.89 1.25 4.13

Chile 70 CDS spread 54.99 254.22 7.80 61.75 2.08 6.33bond spread 186.89 229.90 162.15 17.01 0.76 2.94Basis spread -131.90 57.17 -177.25 55.99 2.29 7.36

China 70 CDS spread 49.48 247.50 10.00 50.63 2.23 7.64bond spread 276.15 337.39 337.39 26.93 0.66 2.37Basis spread -226.66 -67.82 -267.72 40.33 2.15 7.82

Mexico 70 CDS spread 116.79 458.23 29.00 89.14 1.94 6.47bond spread 346.22 425.29 281.34 36.42 0.06 2.15Basis spread -229.43 97.04 -334.55 89.11 1.55 5.36

Phillipines 70 CDS spread 286.89 515.00 99.20 132.12 0.33 1.67bond spread 364.79 489.70 257.69 65.97 -0.15 1.83Basis spread -77.90 257.31 -312.16 184.95 0.46 1.76

Russia 65 CDS spread 171.53 764.58 38.50 183.11 2.13 6.71bond spread 549.06 684.41 417.08 58.60 -0.20 2.67Basis spread -377.53 256.95 -539.10 204.91 1.96 6.11

South Africa 70 CDS spread 119.84 465.00 24.20 104.74 1.73 5.37bond spread 349.93 439.24 294.50 33.11 0.68 3.33

4.1 Unit root test Unit root test was done using ADF and PP methods. The regression model had a drift (intercept)

or non-zero mean was assumed. The lag selection under ADF was done using Akaike

Information Criterion (AIC). Under PP method, the bandwidth (BW) was automatically selected.

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BW shows the lag truncation parameter. Results in table 2 show that CDS and bond spreads for

all the eight sovereigns are non-stationary (I(1)). The basis spread for China and Mexico are

stationary (I(0)). This implies that there is no arbitrage opportunity between the CDS and Bond

markets in the long run in the two sovereigns. The non-stationary basis spread in the other

markets imply that the markets are inefficient and arbitrage opportunities exist in Argentina,

Brazil, Chile, Philippines, Russia and S. Africa between CDS and bond markets.

Conservatively, it can be concluded that China and Mexico have efficient sovereign bond

markets. This is particularly so for Mexico whose bonds are not only highly rated but have been

issuing sovereign bonds for a long time. It is thus a “mature” market.

Table 2: Unit root test using ADF and PP tests

ADF stationarity test of spreads PP Stationality test of spreads Country CDS AIC lags Bond AIC lags Basis AIC lags CDS BW Bond BW Basis BWArgentina I(1) 5 I(1) 1 I(1) 5 I(1) 2 I(1) 2 I(1) 2Brazil I(1) 10 I(1) 2 I(1) 10 I(1) 2 I(1) 1 I(1) 15Chile I(1) 5 I(1) 9 I(1) 3 I(1) 3 I(1) 4 I(1) 3China I(1) 5 I(1) 3 I(0) 10 I(1) 3 I(1) 25 I(1) 2Mexico I(1) 5 I(1) 2 I(0) 4 I(1) 1 I(1) 12 I(1) 0Philipines I(1) 4 I(1) 2 I(1) 4 I(1) 4 I(1) 7 I(1) 4Russia I(1) 7 I(1) 4 I(1) 6 I(1) 3 I(1) 2 I(1) 3S. Africa I(1) 6 I(1) 2 I(1) 5 I(1) 3 I(1) 6 I(1) 3

CDS=credit default swap; AIC= Akaike information criterion;PP=Phillip Perron and BW=bandwidth

4.2 Long run Relationship and conitegration analysisThe cointegration relationship between the spreads is summarized in table 3. As the theory has

predicted, both CDS and bond spreads are cointegrated but under different lag selection methods.

The natural candidate for the cointegration equation is;

CDSit=λ+ βiBondit

where λ=0 and βi=1 for country i at time t if the market is efficient in pricing.

λ represents the basis (CDSspread-bond spread) which should be zero if CDS and bond markets

are efficient in pricing the securities. βi indicates the long term relationship between CDS and

bond spreads. Example: From table 5, the long run relationship between CDS and bond spreads

for China is: CDSit= 46.4468Bondit; CDSit=-0.7186Bondit; CDSit= -2.6387Bondit; CDSit= -

1.7874Bondit; CDSit= -2.1588Bondit; CDSit=-1.5568Bondit; CDSit= -4.8865Bondit; CDSit=

-4.8865Bondit; and CDSit=-2.1916Bondit for argentina, Brazil, Chile, China, Mexico,

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Philippines, Russia and S. Africa respectively. Johansen cointegration Trace test (Johansen 1988,

1991) finds supporting evidence of a cointegration relationship between the two spreads for all

sovereigns. Therefore, both markets price the credit risk in the long run. This confirms

theoretical underpinnings that the two prices are of the same risk, and in the long run, market

forces will eventually remove the arbitrage opportunity between the two markets.

Table 3 shows that 6/8 sovereigns (Argentina, Chile, China, Philippines, Russia and S. Africa)

have cointegrated spreads using likelihood ratio method compared to 3/8 sovereigns (China,

Russia and S. Africa) using FPE, 4/8 sovereigns (China, Mexico, Russia and S. Africa) using

AIC and SC and finally, 2/8 sovereigns (Chile and China) using HQ method. Russia and S.

Africa series are cointegrated under all lag selection methods except HQ. There is inconclusive

literature on lag selection procedures. Blanco et al. (2005) and Euro Bond Market Study (2004)

use AIC measure. Zhu (2006) and Norden and Weber (2004) use both AIC and SBIC measures.

Chan-Lau and Kim (2004) and Ammer and Cai (2007) favor SBIC measure. Lutkepohl (1993)

asserts that SBIC and HQIC provides consistent estimates of the true lag order, whereas AIC and

FPE will overestimate the true lag order with a positive probability. Zhu (2006) claims that the

AIC and SBIC measures generally point to one or two periods with a maximum of five days as

the optimal lag length. This is consistent with my results in table 3 especially for SBIC method.

In line with this arguments, I used a combination of Likelihood ration, AIC and SBIC methods I

testing cointegration of the two spreads.

Table 3: Cointegration and lag selection criteriaCountry Johansen L. Ratio Johansen FPE Johansen AIC Johansen SBIC Johansen HQ

Coitgn # lags Coitgn # lags Coitgn # lags Coitgn # lags Coitgn # lagsArgentina Yes 13 No 2 No 2 No 2 No 2Brazil No 9 No 3 No 14 Yes 1 No 1Chile Yes 6 No 12 No 15 Yes 4 Yes 6China Yes 16 Yes 16 Yes 18 No 1 Yes 18Mexico No 12 No 6 Yes 15 No 2 No 6Philipines Yes 20 No* 22 No* 22 No* 22 No* 22Russia Yes 12 Yes 12 Yes 14 Yes 2 No 14S.Africa Yes 12 Yes 12 Yes 12 Yes 1 No 12

L.Ratio is the likelihood ratio and coingn=cointegration. FPE: Final prediction error; AIC: Akaike information criterion; SBIC: Schwarz Bayesian information criterion; HQ: Hannan-Quinn information criterion

4.3 Short-term dynamic

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Short-term dynamic attempt identify the linkage between two variables (CDS and bond markets) and how efficiently each market fully integrate new information regarding changes in the credit risk. This is achieved using Granger causality tests on the relationship between the two series for each sovereign. The tests are performed using equation (5). Table 4 above reports the results of Granger causality tests with 1, 3 and 6 lagged periods. Granger causality occurs 7/8 sovereigns at different lags. The exception is Philippines. There is no granger causality between the two markets under 1, 3 and 6 lags. This evinces that Philippines CDS and bond markets are segmented or highly inefficient. This is a unique case which require further investigation since theoretically, the two markets should be connected granted that they price the same credit risk. In Argentina, bond market Granger cause CDS market at 3 and 6 lags but not vice versa.

Table 4: Granger causality tests between CDS spread and Bond spread

Country LagsCDS does not

p values

Bond does not

p values

    cause Bond   cause CDS  

Argentina 1 0.560 0.457 0.452 0.504  3 1.380 0.258 4.766* 0.005  6 1.343 0.256 2.129** 0.066Brazil 1 3.164** 0.080 4.523* 0.037  3 1.979 0.127 2.134 0.105  6 0.864 0.527 1.925** 0.095Chile 1 5.941* 0.018 5.941* 0.018  3 2.184** 0.099 5.377* 0.002  6 2.237** 0.054 3.452* 0.006China 1 4.583* 0.036 0.561 0.456  3 1.655 0.186 1.593 0.200  6 2.526* 0.032 3.424* 0.006Mexico 1 4.361* 0.041 1.113 0.295  3 1.384 0.256 2.810* 0.047  6 2.096** 0.070 3.034* 0.013Philipines 1 0.644 0.425 0.180 0.673  3 0.416 0.742 0.212 0.888  6 0.736 0.623 1.199 0.322Russia 1 2.548 0.116 0.264 0.609  3 1.485 0.229 0.725 0.541  6 2.594* 0.030 2.657* 0.027S. Africa 1 6.782* 0.011 0.000 0.987  3 2.023 0.120 0.249 0.862  6 1.987** 0.085 2.136** 0.065

*Reject null (statistically significant) at 5%, **Reject null (statistically significant) at 10%.

This is a one-way or unilateral causality. Brazil has feedback or bilateral or two way causality

under one lag but a bond to CDS causality under 6 lags. Chile reports a bilateral causality under

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1, 3 and 6 lags. In china, CDS Granger cause bond market under 1 lag (Unilateral causality) but a

bilateral causality exists under 6 lags. In Russia, bilateral causality operates under 6 lags. In

South Africa, a bilateral causality exists under 6 lags but a unilateral causality is also present

under 1 lag. A weakness of causality tests is that they indicate a close dynamic connection

between the two markets, but there is no clear evidence that this connection goes in a certain

direction.

4.4 Price discovery and Error correction Mechanism

To further investigate price discovery and pricing error correction between CDS and bond

markets, I used Vector Error Correction Model (VECM) described under equation (6). The lags

are selected using likelihood ratio, AIC and SBIC lag selection methods. The price discovery and

pricing error correction depends on both the significance and magnitude of α1 and α2

coefficients. The two coefficients are the hallmark indicators of speed of adjustment and which

market (CDS or bond) moves to adjust for price discrepancies. If α1 is significantly negative, the

CDS market moves to correct the price discrepancies. If α2 is significantly positive, the bond

market moves to correct the price discrepancies. Table 5 shows that in 6/8 sovereigns, α1 is

significantly negative (t-statistic >-1.96). Therefore, in Argentina, Chile, China, Mexico, Russia

and S. Africa, CDS market leads bond markets in price discovery. This is attributable to three

factors. First, informed traders trade in CDS market hence there are more market participants.

Secondly, CDS accord the easiest and most liquid way to trade credit risk especially if shorting

credit risk. Thirdly, bond market trades bond credit risk while CDS market trades bond plus loan

plus counterparty credit risk. In Brazil and Philippines, bond market lead CDS market in price

discovery. This is not unusual. Chan-Lau and Kim (2004) note that the bond market may

dominate price discovery where banks are both the investors and the insurance (CDS) buyers.

Additionally, CDS positions are more buy-and-hold natured since banks do not trade in CDS. In

such cases, bond markets are more active, liquid and have high trading volume hence they may

lead CDS market in price discovery.

Gonzalo and Granger (1995) proposed a benchmark which reflects the contribution of each

market to price discovery. The benchmark is defined as the ratio of the speed of adjustment

coefficients. Ratio=α1/α1-α2. The lower and upper limits of this ratio are 0 and 1. When the ratio

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is greater than 0.5, it implies that the CDS market leads in price discovery and the bond market

moves afterwards to correct for pricing discrepancies. When the ratio is less than 0.5, the bond

market leads the CDS market. When the measure is close to 0.5, both markets contribute to price

discovery hence evidence is hazy on which market is more important. From table 6, CDS leads

bond market in price discovery in 7/8 sovereigns. The bond market moves afterwards to correct

for pricing discrepancies. This is consistent with the argument that the derivatives instrument

tends to be more efficient in price discovery because there is neither a funding restriction nor a

short-sale restriction in the CDS market. If the CDS market moves ahead of the bond market, the

CDS spreads could become higher than bond spreads in the short run and credit risk pricing

errors persevere for a while. This effect culminates in positive basis spreads during mispricing

period. The exception is Chile where bond market leads the CDS market which confounds

theory. There are potential explanations to this. CDS markets are novel in some emerging

sovereigns and it’s not unusual to encounter empirical results which deviate from theory such as

Chile. Moreover, factors such as liquidity conditions, contract specifications (i.e. CTD), credit

rating and rating events, lagged basis spreads, investor base and macroeconomic factors all play

incalculable role in pricing errors.

Blanco et al. (2005) and Zhu (2006) only consider significantly negative α1,

and significantly positive α2, as the appropriate adjustments to correct the

pricing error (α2- α1). Enders (2004), however, contends that the gap can be

closed with three different scenarios. In evaluating the appropriate error

correction mechanism, Erdem, Geraldo and Bae (2008) departs from these authors and

argue that there are five different cases that a positive gap can be closed

and for a negative gap, they add five scenarios similar to the ones

enumerated below.

1 - An increase in CDS spread and a larger increase in bond spread

2 - A decrease in CDS spread and a smaller decrease in bond spread

3 - A decrease in CDS spread and an increase in bond spread

4 - A decrease in CDS spread and no change in bond spread

5 - No change CDS spread + an increase in bond spread

Erdem, Geraldo and Bae (2008) provide the measure, α2- α1, to check whether one of

the five scenarios above occurs as an appropriate error correction

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mechanism. In any of the five cases, a positive value for α2- α1 will be enough

for the appropriate error correction. To this end, if the error at previous time

period is positive, the measure α2 –α1 should be positive for the gap to close

in any of the five scenarios. Consequently, if the error is negative, α2 –α1

should be positive for the gap to close. In sum, regardless of the sign of the

error at time t-1, α2 –α1 measure should be positive for the error correction

mechanism to truly work properly. In line with this argument, table 6

provides a summary for error correction measure. In all eight sovereigns, the

error correction mechanism is working appropriately since the measure is

positive.

Table 5: Speed of adjustment coefficients and long run relationship( ) means standard error and [ ] means t-statistic which is compared with 1.96 critical value for 5% significance level to evaluate which market leads in price discovery

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CountryArgentina -0.1376

(-0.08029)[-1.71325]

Brazil -0.2304

Adjustment parameter α1 (CDS)

Table 6: Error correction mechanism and price discovery measure

Country Error CorrectionArgentina -0.1376 0.0006 0.1382 Yes 0.996Brazil -0.2304 0.0268 0.2572 yes 0.896Chile -0.0046 0.0245 0.0291 Yes 0.160China -0.0601 0.0423 0.1024 Yes 0.587Mexico -0.0987 0.0278 0.1265 Yes 0.780Philippines -0.1210 0.0175 0.1385 Yes 0.874Russia -0.0468 0.0156 0.0624 Yes 0.750S. Africa -0.0820 0.0342 0.1162 Yes 0.706

α1 α2 α2-α1 α1/α1-α2

5.0 Summary and ConclusionThis paper investigates the pricing contribution of the CDS contracts to sovereign bond market.d

market. Using various econometric tests, I conclude that sovereign CDS market was able to

provide up-to-date credit risk information to sovereign bond investors during the period from

January 2004 to September 2009. Therefore, CDS market may be regarded as the information

center in discovering the changes in credit risk. Furthermore, the pricing equilibrium between

CDS rates and yield spreads does exist; and the persistence of pricing disparity varies from 1

month to 6months

In this paper, I showed that the linkage between sovereign CDS and bond markets is strong for

the period spanning January 2004 to September 2009. Specifically, 7/8 (87.5%) of sovereigns

reported either unilateral or bilateral causality. My analysis has two main contributions. First, it

covered a period and a cross section that has not been investigated before. Second, it tests price

discovery and error correction mechanism at the same time. Other studies have analyzed one of

the two issues but not both. This deviates from past studies and question the how price discovery

has been carried out. Using different lag selection methods, I show that the CDS and sovereign

bond markets are all (100%) cointegrated hence long run relationship between the two markets

exist.

A measure for market efficiency is the condition that the derivative or CDS markets dominate

the underlying asset (bond) markets in price discovery. This was confirmed in 6/8 (75%) of all

sovereigns. There are a few candidates for explaining the deviation of sovereign markets from

corporate markets in terms of efficiency. First, no-arbitrage assumptions do not perfectly hold in

sovereign markets. For instance, it is not possible to short the sovereign bonds in many cases yet

liquidity, credit rating, contract specification such as Cheapest to deliver (CTD) and

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macroeconomic factors among others issues play a role in risk pricing. The analysis showed that

pricing error correction mechanism is working properly for all sovereigns.

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