Empirical examination of the Greek non-government bond market
Nicholas T. Kokores
Panteion University of Athens, Greece
ABSTRACT
The introduction of the euro has forced the development of new products
and greater investor demand for instruments with higher return/risk profiles and
greater mathematical and computational complication such as credit derivatives,
asset backed securities and inflation linked bonds. In Greece the bank debt
securities have been the dominant force in the non-government bond market, due to
the reshaping of regulatory framework under the ongoing Basel II process and the
consolidation of European capital markets after the initiation of monetary union.
This paper offers an empirical examination of the development of this market using
macroeconomic factors such as interest rates and GDP. Furthermore, this research
builds upon the existing studies by looking into the underlying factors that explain
the price differential in the non-government bond market and which have
implications for the mathematical modeling of individual bonds.
I) Introduction
After the introduction of euro many categories of investors that prefer assets
denominated in the local currency where able to increase the diversification of their
investment within the euro area. This case study focuses on the Greek market but
for comparative purposes we include findings from the American and the European
bond markets too.
The government bond market still retains the leading position in the
Eurobond market mainly due to the creditworthiness of the borrowers, the high
liquidity of the issues and the well-developed derivatives market.
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The secondary market activity mainly takes place in the “over the counter”
(OTC) market. Non-government issuing activity has increased substantially since
the introduction of the euro. Specifically, according to ECB data from end-2000 to
end-2003, the outstanding amount of non-government debt securities issued by euro
area residents rose from 3,052 billion to 4,558 billion euros, which is an increase of
almost 50%.1 One explanation is that, the low interest rate environment present in
the bond and money markets drove investors towards higher-yielding investments
like the corporate bonds. However, the lack of a common hedging instrument
remains a serious obstacle for the corporate bond market.
Corporate and financial bonds are often issued in international financial
centers, such as Luxemburg, which has the predominant position among the
banking locations.
There is no argument about the domination of bank debt securities in the
non-government bond market for all euro area countries (ECB 2004a). Furthermore,
among Greek bank bonds, the percentage of small issues – below 500 million euros
– was about 90% while in the rest of the euro-area was well below 40% in 2004
(ECB 2004b). In addition to the above, financial firms, such as banks, tend to have
leverage ratios above 90% where on the other hand, non-financial institutions use
much lower debt ratios.
Another major difference between bank bond and government bond sector is
the coupon structure. According to ECB data 65% of euro-area government bonds
still have fixed rate coupons, while in financial bond market this percentage is
substantially lower (ECB 2004a). Therefore, the patterns of coupon structures in
government and financial bond market do not allow for a direct calculation of yield
differentials. In order to surpass this obstacle a transformation of the standard yield
calculation is needed that is presented latter on.
1
For a detailed analysis of the euro-area bond market see (ECB 2004a).
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II) Theoretical Background and Methodology
The theoretical literature on corporate bond pricing (credit risk valuation)
can be segmented into two categories: 1) structural (contingent claim) credit risk
models and 2) reduced form models. The classical “structural” method of valuation
for default risky bonds is based on the works of Merton (1974), Longstaff and
Schwartz (1995) and Collin-Dufresne and Goldstein (2001) while the more recent
“reduced form” model is used by Duffie and Singleton (1998) and Lando (1998).
One of the pivotal differentiations between these models is that the first one
attempts to model the asset value and capital structure of the firm, whereas the latter
uses proxy variables for the capital structure in order to model yield spreads on the
basis of differing default probabilities. This paper is based mainly on the second
category but we also use findings from the first category.
In this paper debt securities (bonds) refer to securities other than shares
excluding financial derivatives, which should cover issues by resident entities
(originator) irrespective of the currency and market of issuance. Moreover, we do
not use bond indices for our analysis since they tend to present a filtered universe of
the outstanding debt markets due to the criteria of bond selection that are enforced.
Our sample consists of weekly data covering prices of Greek bank bonds for
the period from September 2000 up to October 2004. The bond prices are collected
from Bloomberg Database that produces composite prices for each bond. Bonds
classified as non rated are excluded from the analysis. Furthermore we eliminate
such bonds as securitized bonds and quasi-government bonds.
Yield differentials (spreads) are mainly created by the government bond
yields themselves, as the yield spread of euro area government bonds over German
bonds – also called “Bunds” – which are the “benchmark” for the euro-area bond
market.
The major obstacle that we face when trying to calculate yield spreads for
floating rate bonds is that we cannot readily predict the cash flows because the
coupon payments reset periodically, based on a reference rate such as the threemonth “Euribor” rate (Euro Interbank Offered Rate) posted daily by the ECB. The
way to overcome this difficulty is to compute the discount margin, which is the
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margin relative to the base index rate such that the present value of cash flows
equals the price plus accrued interest of the bond. A second way is to calculate the
yield to maturity spread of a bond as the difference between the yield to maturity of
the floating rate bond and the yield to maturity of the index rate: YTMSpread=
YTMFloat – YTMIndex. In this study we use the second calculation method. The
variables that we are going to examine as the driving forces of this yield spread are
presented below.
III) Description of proxy variables and testable hypotheses
1) Default risk parameter
For modeling default risk, one proxy variable is implemented in the current
study. We are using the ratings announced by Standard & Poor’s and Moody’s
which capture the effect of both probability of default and the recovery rate. Recent
research incorporates ratings as a parameter of default risk. Specifically, Elton et al.
(2001) use Moody’s Standard & Poor’s (S&P’s) bond ratings in order to depict a
proxy of company specific financial ratios. However, in this study we are going to
use a combined rating scale. The first step is to transform S&P’s and Moody’s
ratings into two distinct numerical scales (for S&P’s from AAA=1 down to D=21
and for Moody’s from Aaa=1 down to D=21). We then combine the two scales into
one composite measure by averaging the two numerical ratings for each bond. For
instance an entity rated Aa1=2 and AA=3 would be rated 2.5. Therefore, we assume
that S&P’s and Moody’s ratings indicate the same level of risk. Furthermore we
expect a positive correlation between this variable and the yield spread meaning
that a higher number on the derived rating scale (poorer actual rating) will result to
a higher yield spread for the bond.
2) Liquidity parameter
According to many academics, an investor should expect some
compensation for the liquidity offered in the corporate bond markets versus the
government bonds market. Specifically, Collin-Dufresne et al. (2001) find evidence
that liquidity significantly influences credit spreads changes. Liquidity is also
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important because some institutional investors might not be able to participate in a
corporate bond issue if it is of less than a specific benchmark size, commonly set to
500 million euros.
The ease with which a security can be traded in the market (liquidity) is best
depicted by the difference between the bid price (the price at which the holder can
sell securities) and the offer price (the price at which the purchaser can buy
securities) also known as the bid-offer spread. However, for the “over the counter”
market this spread is difficult to be calculated with accuracy since there is a lack of
data from the bond dealers. Therefore we use an indirect measure of liquidity,
namely, nominal amount outstanding for each bond issue. We expect a negative
relationship where the higher outstanding amount should be a driver towards lower
yield spreads.
3) Maturity parameter
Another reason why bonds might be perceived by investors as carrying
different risk is the “age” of the bond meaning the term to maturity. However, there
is no definitive evidence from the finance literature that bonds tend to differentiate
in relation to their maturity. Nevertheless, the maturity structures of each issuer are
closely linked to the debt requirements that are faced by these issuers.
Furthermore, a common restriction imposed in most of the empirical
analysis on bond markets is to exclude bonds with “optionality” (call or put
options).2 However, in this analysis we include callable bonds by shortening their
maturity down to the call date since in all the Greek outstanding issues of our
sample the call feature shows a 100% certainty of execution. We expect a positive
relationship, where the increase of the bond’s maturity results to higher yield
spreads.
2
The alternative might be to construct a model that explicitly prices the option effect but most of the
empirical analysts prefer to examine the determination of credit risk separately from the option
pricing.
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4) Interest rates parameter
There is an argument that lower interest rates are usually associated with a
weakening economy and therefore higher credit spreads. Specifically, according to
evidence provided by Duffee (1998) for fixed rate corporate bonds, a negative
relationship exists between changes in credit spreads and interest rates.
We use two proxies for the interest rate variable, one for the short term rates
and another one for the slope of the curve (term structure of interest rates). As a
proxy for short term interest rates we use the three-month “Euribor” rate to capture
trends in the closely related to bonds, money market and also, mainly because
around 90% of the Greek financial bonds are floating rate notes that have coupon
payments as a spread based on such a rate. As a proxy for the slope of the interest
rate curve we use the spread between the ten year and the two year yields of the
euro-area government bond curve.
5) Parameter for macroeconomic factors
We are going to use two proxies for the macroeconomic environment. The
first one is the monthly rate of change for the annualized Gross Domestic Product
(GDP) inputted in order to capture the effect of output growth, and the second one
is the monthly rate of change for the annualized Consumer Price Index (CPI) to
capture the effect of inflation. We expect the first one to be negatively and the
second one to be positively correlated with the changes of the yield spread.
IV) Results
In Table 1 below we present the findings of our model estimation in relation
to each proxy. The partial regression coefficient estimates β1, β2,... ,βκ=7, are the
parameters which quantify the individual effect of each of these explanatory
variables on Y (x1, x2, x3, …, xn=6, explanatory variables). The dependent variable
“Y” is calculated as the natural logarithm of the difference between yield to
maturity of the floating rate bond and the yield to maturity of the base index
(YTMSpread= YTMFloat – YTMIndex) in order to depict rates of change for the credit
spread.
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Table 1: Regression results for the filtered sample
Expected
sign
Coefficient t-value
-8.5392
-365.04
Variable
Intercept
Rating
Outstanding
Amount
Maturity /
Call Date
ST Interest
Rates
Curve Slope
+
+0.1489
175.13
-
-4.7312
-41.10
+
+0.4225
67.59
-
+0.1327
-5.7625
85.78
-12.39
GDP_an
-
-7.35 E-6
-2.35
CPI_an
+
+6.90 E-5
4.77
P-value Data source
0,0091 Published announcements
0,0087 Author’s calculations
0,0127 Companies’ Publications
Companies’ Publications
0,0191 Author’s calculations
0,0375 Bloomberg, ECB
0,0288 Bloomberg, ECB
National Statistical Service
0,1132 -Greece/ Bloomberg
National Statistical Service
0,1283 -Greece/ Bloomberg
As it is depicted above, all coefficients are significant except for the GDP
and CPI indicators that are not significant even at the 5% significance level. An
explanation might be that even though the bonds of our sample all have Greek
originators, the macroeconomic factors that affect them might be closely related to
the overall euro-area rather than just the Greek one.
In addition to the above, all have the predicted sign except for short term
interest rates which resulted to the opposite sign from the one expected, signaling
that changes in the three-month money market rate cause spreads to move in the
same direction. A qualitative explanation of this might be that, low short term
interest rate levels induce investors to shift their preferences from low to high-risk
assets. Such a trend results in a decrease of the yield spreads differentials.
Overall the model has a R2 equal to 0.7823 and an adjusted R2 of 0.7582,
while the model’s Durbin-Watson statistic is 2.06.
The interest rate premium therefore compensates the investors for the higher
default risk of a financial institution with a poorer credit rating. Furthermore the
decomposition of credit spreads shows that the risk premium increases substantially
for lower-rated debt.
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V) Conclusions
This study mathematically tests a model that examines the relationship
between Greek financial bonds yield spreads and its dependence to specific
underlining factors: default, liquidity, maturity, interest rates and macroeconomic
indicators. The results add to the previous work that suggests the significance of
these factors. Furthermore, the empirical findings emphasize the deepening of the
Greek non-government bond market. Thus we can conclude that, although the
European and the Greek non-government bond markets differ in term of number of
bonds, the empirical results for both markets are quite similar.
References
Collin-Dufresne, P., Golstein, R., and Martin S., (2001). The determinants of credit
spread changes, Journal of Finance, 56, 2177-2208.
Duffie, D., & Singleton, K. J. (1998). Econometric modeling of term structures of
defaultable bonds, Working Paper, Graduate School of Business, Stanford
University.
ECB, (2004a). The euro bond market study, December 2004.
ECB, (2004b). Developments in euro-denominated bond issuance in 2004,
Quarterly Bulletin, No 79, October – December 2004.
Elton, E., Gruber, M., Agrawal, D., Mann, C., (2001) Explaining the rate spread of
corporate bonds, Journal of Finance, pp.247-277.
Lando, D. (1998). On Cox processes and credit risky securities. Review of
Derivatives Research, 2 (213), 99-120.
Longstaff, F. and Schartz, E., (1995). Valuing risky debt: A new approach, Journal
of Finance, 53, 1213-1242.
Merton, R. (1974). On the pricing of corporate debt: the risk structure of interest
rates. Journal of Finance, 11, 288-300.
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