Individual loan credit risk
Transcript of Individual loan credit risk
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BUS FINANCE 826
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Overview
• The analysis and measurement of credit risk on individual loans. This is important for purposes of:– Pricing loans and bonds– Setting limits on credit risk exposure
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Credit Quality Problems• Problems with junk bonds, LDC loans,
residential and farm mortgage loans.
• More recently, credit card loans and auto loans.
• Crises in Asian countries such as Korea, Indonesia, Thailand, and Malaysia.
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Web Resources
• For further information on credit ratings visit:
Moody’s www.moodys.com
Standard & Poors www.standardandpoors.com
Web Surf
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Credit Quality Problems
• Over the 90s, improvements in NPLs for large banks and overall credit quality.
• Recent exposure to borrowers such as Enron.
• New types of credit risk related to loan guarantees and off-balance-sheet activities.
• Increased emphasis on credit risk evaluation.
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Types of Loans:
• C&I loans: secured and unsecured– Spot loans, Loan commitments– Decline in C&I loans originated by commercial
banks and growth in commercial paper market.
• RE loans: primarily mortgages– Fixed-rate, ARM– Mortgages can be subject to default risk when
loan-to-value declines.
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Consumer loans
• Individual (consumer) loans: personal, auto, credit card.– Nonrevolving loans
• Automobile, mobile home, personal loans
– Growth in credit card debt• Visa, MasterCard • Proprietary cards such as Sears, AT&T
– Risks affected by competitive conditions and usury ceilings
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Other loans
• Other loans include:– Farm loans– Other banks– Nonbank FIs– Broker margin loans– Foreign banks and sovereign governments– State and local governments
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Return on a Loan:
• Factors: interest payments, fees, credit risk premium, collateral, other requirements such as compensating balances and reserve requirements.
• Return = inflow/outflow
k = (f + (L + M ))/(1-[b(1-R)])
• Expected return: E(r) = p(1+k)
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Lending Rates and Rationing
• At retail: Usually a simple accept/reject decision rather than adjustments to the rate.– Credit rationing.– If accepted, customers sorted by loan
quantity.
• At wholesale: – Use both quantity and pricing adjustments.
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Measuring Credit Risk
• Qualitative models: borrower specific factors are considered as well as market or systematic factors.
• Specific factors include: reputation, leverage, volatility of earnings, covenants and collateral.
• Market specific factors include: business cycle and interest rate levels.
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Credit Scoring Models
• Linear probability models:
Zi =
– Statistically unsound since the Z’s obtained are not probabilities at all.
– *Since superior statistical techniques are readily available, little justification for employing linear probability models.
n
jjijX
1, error
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Other Credit Scoring Models
• Logit models: overcome weakness of the linear probability models using a transformation (logistic function) that restricts the probabilities to the zero-one interval.
• Other alternatives include Probit and other variants with nonlinear indicator functions.
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Altman’s Linear Discriminant Model:
• Z=1.2X1+ 1.4X2 +3.3X3 + 0.6X4 + 1.0X5
Critical value of Z = 1.81.
– X1 = Working capital/total assets.
– X2 = Retained earnings/total assets.
– X3 = EBIT/total assets.
– X4 = Market value equity/ book value LT debt.
– X5 = Sales/total assets.
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Linear Discriminant Model
• Problems:– Only considers two extreme cases (default/no
default). – Weights need not be stationary over time.– Ignores hard to quantify factors including
business cycle effects. – Database of defaulted loans is not available to
benchmark the model.
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Term Structure Based Methods
– If we know the risk premium we can infer the probability of default. Expected return equals risk free rate after accounting for probability of default.
p (1+ k) = 1+ i– May be generalized to loans with any maturity
or to adjust for varying default recovery rates.– The loan can be assessed using the inferred
probabilities from comparable quality bonds.
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Mortality Rate Models
– Similar to the process employed by insurance companies to price policies. The probability of default is estimated from past data on defaults.
– Marginal Mortality Rates:
MMR1 = (Value Grade B default in year 1) (Value Grade B outstanding yr.1)
MMR2 = (Value Grade B default in year 2) (Value Grade B outstanding yr.2)
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RAROC Models
– Risk adjusted return on capital. This is one of the more widely used models.
– Incorporates duration approach to estimate worst case loss in value of the loan:
– L = -DL x L x (R/(1+R)) where R is an estimate of the worst change in credit risk premiums for the loan class over the past year.
– RAROC = one-year income on loan/L
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Option Models:
– Employ option pricing methods to evaluate the option to default.
– Used by many of the largest banks to monitor credit risk.
– KMV Corporation markets this model quite widely.
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Applying Option Valuation Model
• Merton showed value of a risky loan
F() = Be-i[(1/d)N(h1) +N(h2)]
• Written as a yield spread
k() - i = (-1/)ln[N(h2) +(1/d)N(h1)]
where k() = Required yield on risky debt
ln = Natural logarithm
i = Risk-free rate on debt of equivalent maturity.
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*CreditMetrics
• “If next year is a bad year, how much will I lose on my loans and loan portfolio?”
VAR = P × 1.65 ×
• Neither P, nor observed.
Calculated using:– (i)Data on borrower’s credit rating; (ii) Rating
transition matrix; (iii) Recovery rates on defaulted loans; (iv) Yield spreads.
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* Credit Risk+
• Developed by Credit Suisse Financial Products.– Based on insurance literature:
• Losses reflect frequency of event and severity of loss.
– Loan default is random.– Loan default probabilities are independent.
• Appropriate for large portfolios of small loans.
• Modeled by a Poisson distribution.
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Pertinent Websites
• For more information visit:
Federal Reserve Bank www.federalreserve.gov
OCC www.occ.treas.gov KMV www.kmv.com
Card Source One www.cardsourceone.com
FDIC www.fdic.gov
Credit Metrics www.creditmetrics.com
Robert Morris Assoc. www.rmahq.orgWeb Surf
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Pertinent Websites
The Economist www.economist.com
Fed. Reserve Bank St. Louis www.stls.frb.gov
Federal Housing Finance Board www.fhfb.gov
Moody’s www.moodys.com
Standard & Poors www.standardandpoors.com
Web Surf