92 CHAPTER
4 Credit Risk
recovery rates for utilities are substantially higher than those for real estate/construction
rms. In any case, industries show signifi cant differences in LGD through time, since
recoveries are much lower in years in which an industry is in distress (Acharya, Bharath,
and Srinivasan 2003; Gupton 2005).
This introduces the crucial issue of whether PD and LGD are linked. In recent years,
empirical evidence has shown that PD and LGD are positively correlated, since they both
tend to grow in recession years; see Hu and Perraudin (2002), Acharya, Bharath, and
Srinivasan (2003), Altman et al. (2005), and the survey by Altman, Resti, and Sironi
(2005). A simple picture of the problem is given in Table 4-8, comparing recovery rates
(i.e., 1 minus LGD) in recession and expansion years.
Despite the recent literature in favor of the existence of a PDLGD link, empirical
studies are based essentially on market LGD for bonds, and this still leaves some theoreti-
cal questions open concerning the correlation between PD and bank loans’ workout LGD.
For instance, while Acharya, Bharath, and Srinivasan (2003) explain the correlation with
industry effects, Altman et al. (2005) nd a clear relationship among PD and LGD for
bonds but argue that a microeconomic model based on supply and demand of defaulted
bonds may explain the link more than a macroeconomic model linking both PD and LGD
to the phase of the economic cycle. In other words, lower recoveries (measured by bond
prices after default) may be explained because in bad years the higher default rate may
imply an excess of supply, and hence a lower market value, of defaulted bonds. If one
agrees on the demand-and-supply explanation for the PD–LGD link, it may still be ques-
tioned whether expected recovery from nontradable bank loans should be assumed to be
as correlated to PD as bonds recoveries are, or, instead, whether the variability of the
loans’ LGD should be driven almost exclusively by speci c factors (such as the duration
of the workout process or the type of underlying assets). Through time, the efforts by
larger banks to increase the size and extent of internal databases for LGD quantifi cation
will make it possible to give an answer through detailed empirical tests.
The issue of the PDLGD correlation is critical, since credit portfolio models (dis-
cussed later) usually assume LGD to be either xed or stochastic but independent of PD.
While even a stochastic LGD would not substantially change VaR estimates for a portfolio
as long as it is independent of the PD, a stochastic and positively correlated LGD would
make 99% or 99.97% potential losses become much higher (Altman et al. 2005).
4.7 Estimating Exposure at Default
Exposure at default is defi ned as “the expected gross exposure of the facility upon default
of the obligor” (Basel Committee 2006a). While for certain kinds of loans the amount
the bank is lending is fi xed and predetermined, the issue of EAD is particularly relevant
TA B L E 4 - 8 Recovery Rates in Expansion Versus Recession Years (Moody’s Data, 1970–2003;
Percentages)
Standard 25th 50th 75th Number of
Mean Deviation Percentile Percentile Percentile Observations
Recessions 32.07 26.86 10.00 25.00 48.50 322
Expansions 41.39 26.98 19.50 36.00 62.50 1703
All 39.91 27.17 18.00 34.50 61.37 2025
Source: Schuermann (2005), based on Moody’s 1970–2003 data.

Get Value at Risk and Bank Capital Management now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.