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Bachelor Studies in Finance
Year 2, Spring 2012
Overview
1.
Traditional credit risk management
BANKING
2.
Credit scoring models
3.
Risk Adjusted Return on Capital –
RAROC
Lecture 7
Credit risk management
Ewa Kania, Department of Banking
2
1. Traditional credit risk management
Whether or not a bank decides to extend credit depends not only on
the potential borrower’s risk but also on the
•
terms of the loan
•
All types of loans are subject to
credit risk
, i.e., a potential
that a bank borrower or counterparty will fail to meet its
obligations with agreed terms.
•
lender’s ability to
monitor
the borrower
•
lender’s ability to
enforce restrictions
on the borrower
•
Credit or default risk
is the most familiar of banking risks
and it still remains the most difficult to quantify.
Terms of the loan
•
Principal: Amount or maximum amount of the loan. ↑CR
•
According to the Basel Committee on Banking Supervision,
the goal of credit risk management
is to maximize a bank’s
risk-adjusted rate of return by maintaining credit risk
exposure within acceptable parameters.
•
Maturity: longer maturities ↑CR
•
Take-down schedule: timetable for withdrawing funds
•
Interest rate: fixed or floating. The borrower’s spread over the
base rate may vary with the borrower’s current financial
condition (e.g., leverage). Higher spreads provide protection
against default but can ↑CR.
•
Loans that are not repaid (
non-performing loans
or bad
loans or loan losses) are the most frequent cause of bank
losses.
•
Pre-payment provisions and unused commitment fees: affects
interest rate risk for fixed-rate loans.
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4
Positive covenants:
Actions a borrower agrees to take.
• Periodically furnish financial information to lender.
• Permit lender to audit any of the firm’s records.
• Promptly advise lender of changes in management or any event
that materially effects the firm’s financial condition.
• Maintain insurance on specified assets.
• Maintain certain balance sheet and income statement ratios.
• Use all loan proceeds for specified purpose and conduct business
in the same manner as is now carried on.
Negative covenants:
Actions borrower agrees not to take.
•
Warranties
• Borrower guarantees financial statements are accurate.
• Borrower has good title to assets.
• Borrower is complying with law and not involved in litigation.
• Borrower has filed for and paid required taxes.
Events of default
•
Failure to make any principal or interest payment, abide by any
covenant, default on any other debt obligation, or file for
bankruptcy triggers default on the loan.
Taking on additional debt or pledging its assets to creditors.
•
Lender can then demand that any remaining principal and
unpaid interest will become immediately due.
•
Repaying loans from owners or shareholders.
•
Merging with any other business.
•
Lender can then sell any collateral and apply the proceeds to
payment of the loan.
•
Limits on payment of dividends and manager compensation.
•
Selling assets/property except in normal course of business.
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6
Bank’s written loan policy
•
Six Cs of lending
•
Portfolio level
–
C
haracter:
Loan purpose and repayment intent
Bank’s lending goals
•
C
apacity:
Legal authority to sign binding contract
–
Quality standards for all loans
•
C
ash Flow:
Ability to generate cash to repay
–
Upper limit for total loans outstanding
•
C
ollateral:
Assets adequate to support loan
–
Description of bank’s principal trade area
•
C
onditions:
Borrower’s economic conditions
•
Organizational structure
–
•
C
ontrol:
Loan policy and how would loan be
affected by changing laws and regulations
Lending authority of loan officers and committee
–
Reporting lines duties and information
•
Lending operations
–
Sources of repayment for business loans
•
Procedures for reviewing, evaluating and making loan
decisions
Borrower’s profits or cash flows
•
Business assets pledged as collateral
–
Required documentation; maintaining and reviewing files
•
Strong balance sheet with ample marketable assets
and net worth
–
Guidelines for taking and perfecting collateral
–
Procedures for setting loan interest rate
•
Guarantees given by businesses
–
Procedures for detecting, analyzing and working out problem
loans.
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8
Loan review
• Examination of outstanding loans to make sure borrowers are
adhering to their credit agreements and the bank is following its
own loan policies
Warning signs of problems loans
•
Loan workouts
• Process of resolving a troubled loan so bank can recover funds
Loan workout process
•
Goal is to maximize full recovery of funds
•
Rapid detection and reporting of problems is essential
Delays in receiving financial statements; changes in accounting
•
Loan workout should be separate from lending function
•
Restructuring debt or eliminating dividend payments
•
Consult customer quickly on possible options
•
Changes in credit rating; adverse changes in the price of stock
•
Estimate available resources to collect on loan
•
Net earnings losses in one or more years
•
Conduct tax and litigation search
•
Deviations in actual sales from predictions
•
Evaluate management’s quality and competence
•
Unexpected changes in deposits
•
Consider all reasonable alternatives
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2. Credit scoring models
Pricing loans
f
+
BR
+
M
1
+
k
=
1
+
(
)
1
−
b
1
−
RR
•
Consumer credit scoring models
use data on historical loan
defaults to statistically determine the most relevant information
for predicting defaults on credit cards, auto loans, mortgages,
and other consumer credit.
where:
k
– contractually promised gross return on the loan per € lent
f –
administration fee
BR
– base lending rate
M
– market premium
b
– compensating balance requirement
RR
– reserve requirement
•
Credit scoring models are increasingly being used in small
business lending, especially by large lenders.
•
The
benefits
of credit scoring models are
•
Provide a rigorous, objective method for using financial data
to screen the credit of loan applicants.
The expected return on a loan
•
Reduces lenders’ time and cost of making loan decisions.
•
The models’ results can be used to
•
() ( )
E
r
= 1
p
+
k
Decide whether a loan request should be approved.
where:
p
– the probability of complete repayment of the loan
•
Decide the terms of a loan: maximum amount lent (credit
limit) and interest rate (credit spread).
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6. Besides any mortgage loans, what are your total balances on
all other loans and credit cards combined?
7. When did you last miss a loan or credit card payment?
8. How many of your loans and/or credit cards are currently past
due?
9. What percent of your total credit card limits do your credit
card balances represent?
10. Please indicate if you have ever gone through any of the
following negative financial events in the last 10 years:
bankruptcy, tax lien, foreclosure, repossession, or account
referred to collection agency.
•
Individual lenders may develop their own credit scoring model
for particular types of loans.
•
Alternatively, lenders buy credit scoring models.
•
A popular one is the FICO
®
model sold by Fair Isaac & Co.
1. How many credit cards do you have?
2. How long ago did you get your first loan?
3. How many loans or credit cards have you applied for in the last
year?
4. How recently have you opened a new loan or credit card?
5. How many of your loans and/or credit cards currently have a
balance?
www.bankrate.com/calculators/credit-score-fico-calculator.aspx
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•
Altman’s Z-score model
uses a Multiple Discriminant
Analysis to classify firms into those likely to become bankrupt
or non-bankrupt over a given future horizon.
•
For a given Type I error (classifying firm as not bankrupt when
it is) and a given Type II error (classifying a firm as bankrupt
when it is not), a critical value of
Z
could be used to approve or
deny a loan.
•
Past financial data on publicly traded manufacturing firm
financial ratios and bankruptcies were used to estimate the
regression equation
•
A major weakness of the linear probability model is that the
estimated probabilities can be below 0 or above 1.0, an
occurrence that does not make economic or statistical sense.
ZX X X X X
=
1.2
+
1.4
+
3.3
+
0.6
+
1.0
1
2
3
4
5
Z
≥ 3.0
⇒
a low default risk firm
•
Instead, we use
logistic regression
(sometimes called the
logistic model or logit model) for prediction of the probability
of occurrence of a default by fitting data to a logistic curve.
1.8 ≤
Z
≤ 2.99
⇒
an indeterminate default risk firm
Z
< 1.8
⇒
a high default risk firm
•
Maximum likelihood method is used for estimation of
β’
s:
X
1
=Working Capital / Total Assets
X
2
=Retained Earnings / Total Assets
X
3
=EBIT / Total Assets
X
4
=Market Value of Equity / Book Value of Long Term Debt
X
5
=Sales / Total Assets
1
PD
=
(
)
i
−
β
+
β
X
+
K
+
β
X
1
+
e
0
1
i
1
k
ik
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•
Ability and willingness
of the borrower to repay debt
outstanding is usually established by application of qualitative
and quantitative models. The character of the applicant is also
extremely important.
•
Discriminant analysis
places borrowers into two groups
(defaulting and non-defaulting) and, by seeking to maximize the
difference in the variance of the characteristics (e.g., income)
between these groups while minimizing the variance within
each group, seeks to derive appropriate weights for the
characteristics that discriminate between the defaulting and non-
defaulting groups.
•
Stability of residence, occupation, family status (e.g., married,
single), previous history of savings, and credit (or bill payment)
history are frequently used in assessing character.
•
The loan officer must establish whether the applicant has
sufficient income.
•
Problems
: calibrated sorting among borrowers is needed; the
weights in the discriminant function (or in any credit scoring
model) are not constant; ignoring important borrower-specific
characteristics and macro factors such as the phase of the
business cycle.
•
GDS (gross debt service)
ratio is the customer’s total annual
accommodation expenses (mortgage, lease, condominium,
management fees, real estate taxes, etc.) divided by annual gross
income.
•
TDS (total debt service)
ratio is the customer’s total annual
accommodation expenses plus all other debt service payments
divided by annual gross income.
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•
As a general rule, for a bank to consider an applicant, the GDS
and TDS ratios must be less than an acceptable threshold. The
threshold is commonly 25 to 30 per cent for the GDS and 35 to
40 per cent for the TDS ratios.
•
The techniques used for
mortgage loan credit analysis
are
very similar to those applied to individual and small-business
loans. Individual consumer loans are scored like mortgages,
often without the borrower ever meeting the loan officer.
Annual
mortgage
payments
+
Property t
axes
GDS
=
•
The loan is
automatically rejected
if the applicant
’
s total
score is less than
X
(i.e., applicants with a score of
X
or less
have, in the past, mainly defaulted on their loan)
.
Annual
gross
income
Annual
total
debt
payments
TDS
=
•
The loan is
automatically approved
if the total score is
greater than
Y
(i.e., applicants with a score of
Y
or more have,
in the past, mainly paid their loan in complete accordance
with the loan agreement).
Annual
gross
income
•
Unlike mortgage loans for which the focus is on a property,
however, nonmortgage consumer loans focus on the
individual’s ability to repay.
•
A score between
X
and
Y
is reviewed by a loan committee for
a final decision.
•
Thus, credits coring models for such loans would put more
weight on personal characteristics such as annual gross income,
the TDS score, and so on.
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20
3. RAROC
A loan customer listing the following information on the loan
application receives the following points:
•
RAROC is a measure of expected loan income in the form
of interest and fees relative to some measure of asset risk.
Characteristic
Value
Score
•
The RAROC model uses the duration model formulation to
measure the change in the value of the loan for given
changes or shocks in credit quality.
1. Annual gross income
€67,000
50
2. TDS
12%
35
3. Relations with bank
None
0
4. Major credit cards
4
20
•
The change in credit quality (∆
R
) is measured by finding
the change in the spread in yields between Treasury bonds
and bonds of the same risk class of the loan.
5. Age
37
30
6. Residence
Own/mortgage
20
7. Length of residence
2½ years
20
Income
RAROC
=
8. Job stability
2½ years
25
L –
loan value
D
L
– loan duration
R –
interest rate
∆
L
9. Credit history
Met all payments
50
∆
R
∆
L
=
−
D
⋅
L
Total score
250
L
1
+
R
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Example
•
• The actual value chosen is the highest change in yield
spread for the same maturity or duration value assets.
• ∆
L
represents the change in loan value or the change in
capital for the largest reasonable adverse changes in yield
spreads; hence, it is a measure of credit risk on the loan.
When lending in a certain region, a bank estimates its
average losses from defaults as 1% of outstanding loans
per year.
•
The worst case loss is 5% of outstanding loans.
•
Economic capital per €100 of loans is therefore
€5 – €1 = €4.
•
Income
represents a one year net income on a loan that can
be estimated as a product of spread and fees on a loan by
its value outstanding. A bank may deduct expected losses,
overhead and tax expenses to get the one year net income
on the loan.
•
The bank’s spread between cost of funds and interest
charged is 2.5% and administrative costs are 0.7%
2.5%
×
100
–
1%
×
100
–
0.7%
×
100
RAROC
=
=
20
%
4
•
RAROC should be higher than a benchmark return on
capital (ROE); otherwise a loan should not be made.
•
RAROC serves as both a credit risk measure and a loan
pricing tool.
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