Evaluation of Conducting Capital Structure Arbitrage Using the Multi-Period Extended Geske ...

41 Pages·2012·0.55 MB·English

Evaluation of Conducting Capital Structure Arbitrage Using the
Multi-Period Extended Geske-Johnson Model

By

Hann-Shing Ju, Ren-Raw Chen, Shih-Kuo Yeh and Tung-Hsiao Yang

Hann-Shing Ju is currently a doctoral student in the Department of Finance, National
Chung Hsing University.
Phone: [phone].
Fax: [phone].
Email: d9821001@ mail.nchu.edu.tw

Ren-Raw Chen is Professor of Finance, Graduate School of Business Administration,
Fordham University.
Phone: [phone]
Fax: [phone]
Email: [email protected].

Shih-Kuo Yeh is Professor of Finance, National Chung Hsing University.
Phone: [phone].
Fax: [phone].
Email: [email protected]

Tung-Hsiao Yang is Associate Professor of Finance, National Chung Hsing
University.
Phone: [phone].
Fax: [phone].
Email: [email protected]
Evaluation of Conducting Capital Structure Arbitrage Using the
Multi-Period Extended Geske-Johnson Model

Hann-Shing Ju*, Ren-Raw Chen, Shih-Kuo Yeh and Tung-Hsiao Yang

This Version: Feb. 20, 2012

*Corresponding author: Hann-Shing Ju, Doctoral student, Department of Finance, National Chung
Hsing University, Taiwan, Phone: [phone], Fax: [phone], Email: d9821001@
@mail.nchu.edu.tw. Ren-Raw Chen, Professor of Finance, Graduate School of Business Administration,
Fordham University. Shih-Kuo Yeh, Professor of Finance, National Chung Hsing University, Taiwan.
Tung-Hsiao Yang, Associate Professor of Finance, National Chung Hsing University, Taiwan.
ABSTRACT
This study uses a multi-period structural model developed by Chen and Yeh (2006),
which extends the Geske-Johnson (1987) compound option model to evaluate the
performance of capital structure arbitrage under a multi-period debt structure.
Previous studies exploring capital structure arbitrage have typically employed
single-period structural models, which have very limited empirical scopes. In this
paper, we predict the default situations of a firm using the multi-period
Geske-Johnson model that assumes endogenous default barriers. The Geske-Johnson
model is the only model that accounts for the entire debt structure and imputes the
default barrier to the asset value of the firm. This study also establishes trading
strategies and analyzes the arbitrage performance of 369 North American obligators
from 2004 to 2008. Comparing the performance of capital structure arbitrage between
the Geske-Johnson and CreditGrades models, we find that the extended
Geske-Johnson model is more suitable than the CreditGrades model for exploiting the
mispricing between equity prices and credit default swap spreads.
I. Introduction
A trader can theoretically purchase equities and short risky bonds or vice versa to
profit from relative mispricing of these two instruments. This is known as capital
structure arbitrage (or debt-equity trading), and has been the dominant trading activity
in hedge funds specializing in credit security markets. Practitioners believe in existing
cross-market inefficiencies and try to profit from these opportunities. Researchers are
also interested in this issue because the existence of arbitrage profit supports both
cross-market inefficiencies and the usefulness of quantitative credit risk models.
Chatiras and Mukherjee (2004) proposed various ways of conducting capital
structure arbitrage. For example, arbitraging price discrepancy between the
convertible and other forms of company debt is a common form of capital structure
arbitrage (see Calamos (2003)). Another form is to use the pricing discrepancy
between a company’s high yield debt and call options on its stock.
Many arbitragers, however, replace risky bonds with credit default swap
(hereafter CDS) contracts to implement capital structure arbitrage, because CDS
contracts possess more detailed data and have more liquidity than risky bonds. In
addition, many researchers have shown the significant relationship between CDS
spreads and risky bonds spreads.1 This paper uses a structural model to forecast CDS
spreads and conduct different trading strategies based on their forecasted values
relative to the realized counterparts. A significant correlation should theoretically exist
between CDS spreads and equity prices. Thus, institutional investors such as hedge
funds, banks, and insurance companies might apply their trading strategies across
credit and equity markets when the valuation models observe mispricing signals

1 See, for example, Duffie (1999), Lonfstaff et al. (2003) Hull, Predescu, and White (2004),
Houweling and Vorst (2005) and Blanco, Brennan, and Marsh (2005).
1
between a company's CDS spread and equity.
Arbitrage performance, however, can be substantially attributed to model
specification. The nature of capital structure arbitrage is to measure and take
advantage of the difference between model and market spreads. Valuation of CDS
contracts depends on default probability, estimated differently by a variety of
underlying models, which plays an important part in arbitrage performance. Most
models can predict a theoretical spread that significantly correlates with market
spread, but different assumptions regarding the default criteria affect the calculation
of CDS spreads and substantially affect arbitrage performance.
When conducting capital structure arbitrage, arbitragers must employ an
appropriate structural credit risk model to identify arbitrage opportunities. The
structural credit risk models can be traced back to Black and Scholes (1973) and
Merton (1974). The Black-Scholes-Merton option formula can exploit the analogy
between company equity and a call option that underlies the total asset value of the
company with an exercise price equal to the face value of outstanding debt. Chen and
Yeh (2006) indicated that if company value falls below the debt level, equity holders
do not benefit before all debt holders are paid off and the company defaults on the
debt at maturity.
The Merton structural model of valuing equity as a call option is a single-period
model. This model is well developed in relating credit risk to capital structure of a
firm. However, it is only a single period model, which means that a company can
default only at the maturity time of the debt when the debt payment is made. The
bankruptcy triggering mechanism is simple because the model ignores the possibility
of early default. As a result, the model cannot deal with multiple debt structure, which
is considered more realistic.
2
Two approaches extend the Merton structural model: barrier structural models
and compound option models. Barrier structural models assume an exogenous default
barrier, and default is defined as the asset value crossing such a barrier. Black and
Cox (1976) pioneered the definition of default as the first passage time of firm’s value
to a default barrier. However, the compound option models developed by Geske
(1977), and Geske and Johnson (1984) allow a company to have a series of debts.
They employ the compound option pricing technique to characterize default at
different times. The main point is that defaults are a series of contingent events; later
defaults are contingent upon prior no-defaults. Most importantly, the Geske and
Johnson model is the only structural model that endogenously specifies both default
and recovery, while the other structural models assume exogenous barrier and

recovery.
Literature investigating whether arbitrage opportunities do exist is scant, even
though debt-equity trading has become popular. Duarte, Longstaff, and Yu (2005)
examined fixed income arbitrage strategies and briefly depicted capital structure
arbitrage. Yu (2006) subsequently focused on capital structure arbitrage and was the
first to conduct a detailed analysis of trading strategies by implementing the
CreditGrades model. Bajlum and Larsen (2008) argued that the opportunities of
capital structure arbitrage could vary with model choice and indicated that model
misspecification should have a significant effect on the gap between the market and
model spreads. Visockis (2011) calculates CDS premiums using two structural
models, namely, the Merton model and the CreditGrades model. The results of the
Merton model show that average monthly capital structure arbitrage returns are
negative. On the other hand, the results of the CreditGrades model show that the
capital structure arbitrage strategy produces significant positive average returns in the
3
investigated period.
Hence, instead of using exogenous default barriers as the CreditGrades model
does, the current research uses a series of endogenous default barriers to calculate
default probabilities across different periods and predicts a theoretical CDS spread
more accurately. This paper adopts the extended Geske-Johnson model by Chen and
Yeh (2006) and uses the similar trading rule developed by Yu (2006) to evaluate
capital structure arbitrage using simulations. Among the related literature, this study is
the first to analyze capital structure arbitrage by implementing the multi-period
structural model with endogenous default barriers.
The CreditGrades model became popular in the credit derivatives market, was
jointly developed by CreditMetrics, JP Morgan, Goldman Sachs, and Deutsche Bank,
and subsequently copy written. Sepp (2006) suggested that this approach supplements
the Merton model (1974) by providing a link to the equity market, particularly to
equity options. The CreditGrades model assumes that firm value follows a pure
diffusion with a stochastic default barrier, introduced to make the model consistent
with high short-term CDS spreads.
Some investment banks have proposed using the CreditGrades model to measure
firm's credit risk. Currie and Morris (2002) and Yu (2006) argued that the
CreditGrades model is the preferred framework among professional arbitrageurs by
testing the historical data. Yu (2006) used the CreditGrades model to implement a
strategy for capital structure arbitrage. The barrier model defines default as the first
time asset value to cross the default barrier. However, the barrier over which the
default is defined is exogenous and arbitrary. This could cause negative equity value,
and an arbitrarily specified barrier could cause incorrect survival probability. Chen
and Yeh (2006) have depicted internal inconsistency of an arbitrarily specified barrier.
4
Yu’s study reveals that the capital structure arbitrageur could suffer substantial losses
at an alarming frequency. The current study suspects that this finding could relate to
model error because the result is based on the CreditGrades model, which assumes an
exogenous barrier and a single period. Hence, its empirical scope is at least limited to
both characteristics.
This paper conducts a capital structure arbitrage using the multi-period
Geske-Johnson model to implement trading analysis. As indicated in Chen and Yeh
(2006), the multi-period Geske-Johnson model is the only model that accounts for the
entire debt structure and imputes the default barrier to the asset value of the firm.
Chen and Yeh (2006) also provided a binomial lattice for implementing the
multi-period Geske-Johnson model instead of conducting expensive high-dimension
normal integrals. This work extends the binominal model using numerical
optimization techniques to assess firm asset value for matching market equity. This
shows that the multi-period Geske-Johnson model can applicably evaluate the credit
default swap spread by n-period risky debts and affect the cumulative profits of
arbitrage when using simulated trading on 369 obligators during 2004 to 2008. To our
knowledge, this is the first paper to apply the multi-period Geske-Johnson model in
predicting the CDS spread and evaluating capital structure arbitrage.
The remainder of the paper is outlined as follows. Section 2 briefly presents the
extended Geske-Johnson model and the CreditGrades model. Section 3 outlines the
trading strategy. Section 4 describes the data. Section 5 conducts an empirical
comparison between the extended Geske-Johnson model and the CreditGrades model.
Section 6 illustrates some case studies. Section 7 presents the general results of the
strategy, and Section 8 concludes all findings.

5
2. Credit Risk Models
The gap between the market and model CDS spread might vary with model
choice because of the substantial difference in model assumptions and methodologies.
The arbitrage signal of relative mispricing is delivered by different models that link
equity and credit derivative markets. However, the arbitrage signal remains stable if
the model can precisely describe CDS spread behavior. This section briefly describes
the CreditGrades model and the extended Geske-Johnson model for pricing CDS
spread as follows. Further details on the two pricing models can be found in Finger
(2002) and Chen and Yeh (2006).
2.1 CreditGrades Model
The CreditGrades model was jointly developed by RiskMetrics, JP Morgan,
Goldman Sachs, and the Deutsche Bank. The model derives from the Merton structure
model for assessing credit risk, including survival probability and credit spread. The
model solves default points with an exogenous default barrier and calculates the CDS
spread with uncertain recovery. Currie and Morris (2002) and Yu (2006) noted that the
CreditGrades model has been used for many years by participants of various credit
derivative markets and has become an industry practice benchmark across numerous
traders.
In the CreditGrades model, the firm asset V is assumed as
dV VdW (1)
t t t
whereis the asset volatility and W is a standard Brownian motion. The default
t
barrier is given by
LDLDeZ2/2 (2)
6
where L is the random recovery rate given default, D is the company’s debt per share,
L
is the mean L,is the standard deviation of ln (L), and Z is a standard normal random
variable. From the perspective of default boundary conditions, the simplest expressions
for asset value and asset volatility are given by:
V SLD (3)
and
S
  (4)
s SLD
where S is equity value, and  is equity volatility. The condition that the firm default
s
does not occur is
VeZ2t/2 LDeW2/2 (5)
0
where V is initial asset value. The approach solution for survival probability is
0
expressed as
 A lnd   A lnd 
q(t) t  d t   (6)
2 A 2 A
   
t t
A 2t2
t
where V e2
d  0
LD .
is the cumulative normal distribution function. This model can calculate a CDS
spread by linking the survival probability under constant interest rate, given by
1q(0)er(G(T )G())
c(0,T)r(1R) (7)
q(0)q(T)erT erT(G(T )G())
7