Transcript Slide 1

Credit Risk Management
Chapters 11 & 12
Credit Risk Management
 uniqueness of FIs as asset transformers
– What do we mean?
– What type of risk do FIs incur due to this role?
– What are important things FIs must do to deal
with/reduce these risks?
 importance of sound banking system for
economic health
– Japan
– US credit problems in 1980s and 1990s
Credit Analysis
 all analysis should be geared to one
decision
– Does FI grant the loan or not?
– stated loan policy
– clear documentation
– criteria not discriminatory
– minimum credit standards
– standard application forms
Types of Loans
 Commercial and Industrial loans
– maturities
– uses
– amounts
 syndicated loans
– secured or unsecured
– spot/loan commitment
– importance of commercial paper
Types of Loans
 Real Estate loans
– mortgage loans and home equity loans
– commercial vs. residential mortgage loans
– ARMs
 Consumer loans
– i.e., personal or auto loans
– revolving loan
– usury ceilings
Real Estate Lending
 large secondary market forces
standardization of applications
 major factors in accept/reject decision
– applicant’s ability and willingness to repay
– value of borrower’s collateral
 characteristics/standards used to assess
requirements
Real Estate Lending
 GDS (gross debt service) ratio - gross debt
service ratio calculated as total accommodation
expenses (mortgage, lease, condominium,
management fees, real estate taxes, etc.) divided
by gross income
 TDS (total debt service) ratio - total debt ratio
calculated as total accommodation expenses plus
all other debt service payments divided by gross
income
Credit Scoring
 expresses applicant’s credit quality numerically –
removes some subjectivity
 helps FI manager:
– numerically establish which factors are important in
explaining default risk
– evaluate the relative importance of factors
– improve pricing of default risk
– be better able to screen out bad loan applicants
– be in better position to calculate any reserves needed to
meet expected future loan losses
Credit Analysis
 Consumer and Small Business lending
 Mid-Market Commercial and Industrial
– firms with annual sales of about $5-$100 million
– subjective and objective in evaluation
Credit Scoring Models
 linear probability and logit models
– use past data as inputs into model to explain repayment experience
on old loans
– relative importance of factors in explaining past repayment is used
to forecast repayment probabilities of new loans
 Linear discriminant models
– while above models project a value for expected probability of
default if a loan is made, discriminant models divide borrowers into
high or low default risk classes contingent on observed
characteristics
– Altman’s Z
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Z = 1.2X1 + 1.4x2 + 3.3x3 + 0.6x4 + 1.0x5
X1 = working capital / total assets
X3 = EBIT / total assets
X5 = sales / total assets
X2 = RE / total assets
X4 = MV equity / BV LT debt
The KMV Model
 Banks can use the theory of option pricing to
assess the credit risk of a corporate borrower
 The probability of default is positively related
to:
– the volatility of the firm’s stock
– the firm’s leverage
 A model developed by KMV corporation is
being widely used by banks for this purpose
Return on Loan
 methods to calculate
– ROA (return on assets)
– RAROC (risk-adjusted return on capital)
 factors that affect FI’s return on loan:
– The base lending rate on the loan (L)
– The credit risk premium (m)
– Fees earned as a result of the loan (e.g. origination fee and credit
line fees)
– Whether the borrower repays in full on time
– The value of collateral and ease and cost of collections if the
borrower defaults
– The nonprice terms and conditions on the loan (other than fees):
 Origination fee (f)
 Compensating balances (b)
 Reserve requirements (R)
ROA
 ROA per dollar lent by FI is
f  ( L  m)
1 k  1
1  [b(1  R )]
 ROA per $ lent is going to be greater than simple
promised interest return on loan if b>0 because FI
gets to keep compensating balance
Example 1
 A bank has a base lending rate of 8% (L),
and charges a certain customer a 110 basis
point risk premium (m). The bank also
charges a 1% origination fee (f). The bank
requires the borrower to maintain
compensating balances of 7% of the loan
amount. The reserve requirement is 10%
and the loan amount is $1 million.
Example 2
 A corporate customer obtains a $1 million line of credit
from a bank. The customer agrees to pay a 9% interest
rate and agrees to make compensating balances of 6% of
the total credit line and 3% of the amount actually
borrowed. These will be held in non-interest bearing
transactions deposits at the bank for one year. The bank
charges a 1% loan origination fee on the amount borrowed
and a 0.25% commitment fee on the unused line of credit.
The expected draw down (loan amount) is 60% of the line
for one year. Reserve requirements are 10%. What is the
expected rate of return to the bank?
Example 3
 The credit risk premium (m) can be set based on historical
default rates on loans of this category and rates of return
on defaulted loans. For instance in order to earn the base
loan rate of say 9% if the default history of a given loan
category is as follows:
 % of loans
Default experience
RoR on category
 98%
No default
9% + m
 1.5%
Limited default
0%[1]
 0.5%
Total writeoff
-100%

[1] The percent of loans and the rates of return numbers
should both be net of recoveries
RAROC
 The risk adjusted return on capital (RAROC)
originated by Banker’s Trust is now widely
used instead of the ROA method of loan
pricing presented above.
One year net pretax income on the loan
RAROC 
Capital at risk
Example 4
 Continuing with Example 1 from above and adding the additional
necessary information will illustrate how to calculate the RAROC:
 The loan had income of $101,000. Suppose the dollar cost rate
(including interest and noninterest costs) of providing the loan is
10.3%. The net loan amount was $937,000 so the dollar cost is thus
$96,511 (=$937,000  0.103).
 Suppose that typical default rates may be 0.3% in a given year.
However, according to historical default rates, the 99th percentile, or
the extreme loss rate, for this loan category is 3%.[1] This means that
the bank believes that in the worst case scenario (which in this case
should happen only once every hundred years), 3% of the loans will
default instead of the typical 0.3%.
 Suppose further that based on historical data the bank can expect to
eventually recover 25% of the loans that default.
Loan Portfolio Risk
 credit scoring and RAROC and other
methods helped FI analyze risk of individual
loan
 also need to measure credit risk to entire
loan portfolio
 simple models of loan concentration risk
– migration analysis
– concentration limits
Concentration Limits
 external limits set on the maximum loan size
that can be made to an individual borrower
 set limits by assessing the borrower’s
current portfolio, its operating unit’s
business plans, its economists’ economic
projections, and its strategic plans
Example 5
 Suppose management is unwilling to permit losses
exceeding 10% of an FI’s capital to a particular
sector. If management estimates that the amount
lost per dollar of defaulted loans in this sector is 40
cents, the maximum loans to a single sector as a
percent of capital, defined as the concentration
limit, is
 CL = maximum loss as % of capital * (1/loss rate)
= 10% * (1/0.4)
= 25%