non sapevo dove postarlo, chiedo eventualmente ai moderatori di spostarlo (o cancellarlo, sarà un post alquanto confuso...
)
An Empirical Analysis of Bond Recovery Rates
Division of Research and Statistics
The Federal Reserve Board
20th and C Streets, NW
Washington, D.C. 20551
December 2004
siccome ho sempre osteggiato i tentativi di "fare statistica" nei default, al solito predico bene e razzolo male...
A frictionless, structural view of default has the unrealistic implication
that recovery rates on bonds, measured at default, should be close to 100
percent.
This suggests that standard “frictions” such as default delays,
corporate-valuation jumps, and bankruptcy costs may be important drivers
of recovery rates. A structural view also suggests the existence of
nonlinearities in the empirical relationship between recovery rates and
their determinants. We explore these implications empirically and find
direct evidence of jumps, and also evidence of the predicted nonlinearities.
In particular, recovery rates increase as economic conditions improve from
low levels, but decrease as economic conditions become robust. This
suggests that improving economic conditions tend to boost firm values,
but
firms may tend to default during particularly robust times only when
they have experienced large, negative shocks.
ed a primo impatto, non sembrerebbe necessario condurre una ricerca così strutturata (tutti i default dal 1982 al 2002) per arrivare a queste conclusioni peraltro abbastanza condivisibili...
The credit risk of corporate debt has two components: the likelihood of default
and the recovery rate given default. Understanding the determinants of these risks is
critical for the design and implementation of debt pricing models and risk management strategies
the empirical reality that
recovery rates at default (or RAD)—measured by bond price at default as percent of par
value—for nonfinancial corporations over the past two decades
have averaged only about
40 percent with a standard deviation of about 28 percent.
The main sample
consists of over 1,300 nonconvertible public bonds issued by U.S. nonfinancial firms
that
defaulted between 1983 and 2002, inclusive. The regression sample sizes depend on the
specification, with the smallest sample being about 600 observations. Focusing on
recovery rates at default is reasonable, since such rates are the actual recovery rates for
investors that choose to sell their bonds at the time when an issuer defaults. Indeed,
many investors do sell their bonds at default, as indicated by the active secondary market
for defaulted bonds (see, for example, Altman 2003).
These findings also complement the results from other studies of recovery rates.
In the most exhaustive study to date, Acharya et al. (2003) analyze recovery rates
measured at default (
RAD) and at resolution (
RAR), where resolutions include
bankruptcy emergences, liquidations, and out-of-court restructurings. They find that
RAD increases with firm and industry financial performance, bond size, and bond
seniority, and that
RAR increases with industry financial performance, bond seniority,
and less time-in-default. (i fallimenti veloci premiano il recovery rate)
The existence of a
macroeconomic factor in recovery rates is an important issue
for the design of credit risk models. Our results not only show that such a factor exists,
they also suggest strongly that the relationship between macroeconomic conditions and
RAD takes a particular nonlinear form.
RAD increases as economic conditions improve
from relatively low levels, but it decreases as economic conditions become particularly
robust. As a result, while defaults may be rare during very robust times, recovery rates
may be relatively low. The intuition is that firms may tend to default during particularly
robust periods only when hit by very bad shocks, which in turn depress recovery rates.
This is surprising, since it implies that an idiosyncratic factor (a firm-level jump) affects
the functional form of the relationship between RAD and the systematic factor through
the conditionality of default.
Figure 1 plots annual recovery rates (left scale) against two commonly-used,
annual measures of macroeconomic conditions (right scale) from 1983-2002. The figure
highlights, with shaded regions, the NBER-defined recession periods: July 1990-March
1991, and March 2001-November 2001. The series in the figure are denoted as follows:
the diamonds represent the weighted average RAD of nonfinancial straight bonds by
default year (using defaulted amounts as weights); the triangles denote the deviation of
annual real GDP growth rate from its mean of 3.3 percent; and, the dots indicate the
percentage deviation of annual real GDP from its trend (trend GDP is calculated using
Hodrick-Prescott filter).
Aggregate recovery rates appear cyclical at times, except that they are very low in
particularly robust periods.
The average RAD for recession periods was 31 percent,
while the average for expansion periods was 42 percent. However, the lowest average
annual RAD of 20 percent was in 2000—a year of robust economic growth. This
nonlinearity dampens the generally positive relation between recovery rates and
macroeconomic conditions. Indeed, the correlation between RAD and real GDP growth
is just 0.2, and the correlation between RAD and detrended GDP is -0.4.20 This confirms
the weak correlations found in previous papers (for example, Altman et al. (2004) and
Acharya et al. (2003)), and suggests that the empirical relationship between RAD and
macroeconomic variables may be nonlinear.
The coefficients on the industry profit margin are all
positive and significant, with the point estimates indicating that a one percentage point
increase in industry profit margin leads to about a 40 basis point increase in RAD. The
coefficients on the dummy variable for whether the bond was issued by a firm in the
energy or utility industry are all significant (at the 95 percent confidence level), with
point estimates suggesting that RADs for such firms are at least 22 percentage points
higher, ceteris paribus, than RADs for other firms. The higher level of recovery rates on
bonds issued by energy and utility firms is well known, and we have little to say about
why this is the case.
The results, shown in Table 6, indicate that
telecom and steel industry bonds have
lower recovery rates. The coefficients on the telecom dummy variable are all significant
(at the 95 percent confidence level), and they suggest that
RADs on telecom bonds are
about 20 percentage points lower, ceteris paribus, than on other bonds (the omitted
category). The coefficients on the steel industry dummy are significant in two of the
three specifications, and they suggest that
RADs on steel bonds are about 11 percentage
points lower, ceteris paribus, than on other bonds (the omitted category).
