QUANTITAIVE APPROCHES TO
CREDIT PORTFOLIO RISK AND
CREDIT MODELING
 In this chapter we describe efforts to model and measure
credit risk in whole port- folios using statistical and
economic tools, including the Merton model, the actuarial
approach, reduced-form models, and hybrid models.
 We can think of these more quantitative approaches to
measuring individual and portfolio risk as an attempt by
institutions to apply to credit risk the kind of “rocket
science” that has made such a diPerence to their
management of market risk and derivatives trading
 Economic capital is the financial cushion that a bank employs to
absorb unexpected losses
Example: those related to credit events such as default and/ or
credit migration. It's clearly important that a bank reserve the right
amount of economic capital if it is to remain solvent to any degree of
confidence . But economic capital is also increasingly important for
helping banks to price risk and to set sophisticated risk limits for
individual businesses.
 The modeling approaches we describe in the main text allow a bank
to model the distribution of values of its portfolio of obligors and
derive an economic capital number, or VaR number.
 As with market risk, the confidence level is set in line with
the bank's risk appetite or solvency standard often its
target credit rating. For example, if the confidence level is 1
percent, then the bank would be able to reassure itself that
99 times out of 100, it would not incur losses above the
economic capital level over the period corresponding to
the credit risk horizon (say, one year).
 Three factor are following level of credit risk portfolio
The credit
standing of
specific obligors
concentration
risk
the state of the
economy
 The credit standing of specific obligors
One bank might concentrate on prime or investment-grade obligors, so that there is
a very low probability of default for any individual obligor in its portfolio.
 concentration risk
The extent to which the obligors are diversified in terms of number, geography, and
industry.
A bank with only a few big-ticket corporate clients, most of which are in commercial
real estate, is rightly considered to be riskier than a bank that has made many
corporate loans to borrowers that are distributed over many industries.
 The state of the economy
During good times of economic growth, the frequency of default falls sharply
compared to periods of recession. Conversely, the default rate rises again as the
economy enters a downturn.
To make things worse, periods of high default rates, such as during 2001–2002 and
2008–2009, are characterized by a low rate of recovery on defaulted loan.
Therefore necessary to consider default probabilities, default correlations, recovery
rates, and the state dependent nature of recovery rates.
 For many financial instruments, such as credit default swaps, the recovery rate is
defined in terms of the market price of the underlying asset post default.
 We are now in a position to calculate the distribution of the changes in the bond
value, at the one-year horizon, resulting from an eventual change in credit quality.
Standard Deviation (%)
Seniority Class
Mean
(%)
Senior secured
55.80
26.86
Senior unsecured
51.15
24.45
Senior subordihôted
38.52
25.81
Subordinated
32.74
20.18
Junior subordinated
17.09
10.90
 Credit letrics and Moody's KYV proposed relatively similar models, so here we will
present only the KMV model (which is more comprehensive and elaborate). We
KMV approach constructs a three-layer factor structure model,
 First level: a composite company-specific factor, constructed individually for each
firm based on the firm's exposure to each country and industry
 Second level: country and industry factors
 Third level: global, regional, and industrial sector factor