Credit Risk Principles

Expected Loss Components


Under Basel II, Expected Loss (EL) equates to the
Probability of Default (PD) times Loss Given Default (LGD)
times Exposure at Default (EAD), or in symbolic form:
In turn, we can expand each of these components further.
An FSA definition stipulates mortgage default (D) to be 180
days of arrears 11 as the guideline within retail exposures.
Using the 3Cs approach for default measures, we can set
conditional probability of default in Bayesian notation as:
Mortgage Type can refer to the three main market
segments of Prime, Near Prime and Sub-Prime but depends
on the Character result to determine which type is finally
applicable. The weighting parameters alpha , beta , and
gamma above, (always sum to one) will determine the
overall influence of the PD measurements for Character
and Capacity and Collateral For prime mortgages, one would expect Character to be
paramount. You would also expect to treat Collateral as
merely security underpinning the loan whilst Capacity would
have an influence somewhere in between these two
measures. However, under sub-prime mortgages Collateral
is paramount, and then Capacity in relative influence and
finally Character (as most applicants have had a chequered
credit history by simply qualifying for these types of loans).
We could therefore assume the following initial values for
the relative influence of these PD sub-parts:

As evidence of this view especially for the Collateral Sub-prime

component, we can examine the CML Repossession Risk

Review report. Authors Cunningham and Panell (2007) cite that

‖...the adverse credit loans in non-conforming RMBS have

substantially higher arrears rates than prime home -buyer

mortgages and the adverse credit sector accounts for a much

larger share of repossessions than its 5 -6% share of new lending

business. In a number of locations in London, the cumulative

repossession rates since issue on non-conforming RMBS portfolios

are around 5%, compared with the overall industry average for

2006 of 0.15%.‖ Therefore, for sub-prime or non-conforming

mortgages, given evidence of the readiness of the sector to

repossess properties, one therefore needs to over-emphasise

the Collateral component more so than the Character aspect

and Capacity could be set relatively equal regardless of the

mortgage type. Near-prime mortgages would also need to have

PD relative influences set within the risk extremes of prime

and sub-prime. You could also adjust the component part

values of alpha, beta and gamma to reflect the overall risk

appetite control and perhaps as part of the conditional PD

measure adjustments for more accurate reflection of the

current stage of the housing cycle. By way of practical

application, a typical specialist lender might have the following

segments within their mortgage portfolio : Sub-prime 60%,

Near Prime 20% and Prime 20%.

PD Character Measurement

For

it is possible to derive a PD measure of credit

character from a credit bureau score or by using an internal

application score (or even a combination of both). For an

initial risk-based pricing approach, the bureau or application

score is required. However, for any on -going risk adjustment

offers (for example, through a customer retention initiative), it

would be better to use a behavioural score and/or a bureau

score to derive the required PD. The use of mortgage bureau

scores have gained in popularity since 2000 with U.S. ones

being similar to U.K. bureaux except that less information is

recorded. According to Thomas et al. (2002), such scores will

rely upon the following general types of data:

1. Personal information (name; address; former address;

date of birth; name of current and former employers; and

identifiers: such as Social Security number in the U.S.)

2. Public record information (county court judgements;

bankruptcy; Involuntary agreements; and electoral roll

data)

3. Credit accounts history (type of account; credit limit;

payments up-to-date; arrears data and balances

outstanding)

4. Inquiries (type of credit grantors and date of inquiry)

5. Aggregated information (percent of houses at a postcode

with CCJs for example)