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Chapter 6: Credit Analysis Models (Measures of credit risk (PV of expected…
Chapter 6: Credit Analysis Models
Reduced form models
the default event is consider as a "surprise"
estimation of parameters
implicit approach
historical approach
strengths and weaknesses
strengths
Business cycle is taken into account
No need for specification of balance sheet structure
Model inputs are observable
weakness
hard to properly formulate the model and back test it
purpose: overcome the key weakness of structural models:
assumption of company's asset trade
=> replace by:
company debt's trades
impose their assumptions on the
outputs of structural models
(the probability of default and loss given default) - rather than on balance sheet structure itself =>
increase flexibility in matching actual market conditions
Traditional credit models
Credit scoring
used for small owner- operated businesses and individuals
ranks a borrower's
credit riskiness
(not provide default probability)
main features
Different implications for the probability of default
depending on the borrower and the nature of the loan that has been extended
Credit scoring stability overtime
Not the
percentile ranking
of borrower among a universe of borrowers
Not explicitly depend on current economic conditions
Credit ratings
used for companies, sovereigns, sub- sovereigns, and those entities' securities, as well as asset- backed securities
rank the credit risk (but not provide default probability)
create an
ordinal ranking
(from highest to lowest) of borrowers by riskiness and aid to
portfolio selection
and
risk management
strengths
simplicity
stability
weakness
no explicit link with the business cycle
compensation system
with
potential conflict of interest
that may distort the accuracy of credit ratings
inconsistent link with default probability
Measures of credit risk
Default probability
Loss given default = 1 - Recovery rate
Recovery rate: percentage of recovered in default
Expected loss = default probability x loss given default
PV of expected loss
:
largest price
one would willing to pay on a bond to a
third party
(e.g: an insurance company) to
remove the credit risk
while holding the bond
PV of expected loss:
modifications to the expected loss
Explicitly adjust the
probabilities
to account for the risk of the CFs (
the risk premium
)
Include the
time value of money
in the calculation - discounting the future CFs to the present
Structural models
Aim: understand the
economic of a company's liabilities
and build in the insights of option pricing theory
link between option pricing theory and structural models:
call option analogy for equity
company's owners (equity holders) have
limited liability
Strengths
Optional analogy of a company's default probability and recovery rate
Estimated using current market prices
Weakness
balance sheets hard to model
firm's asset value is unobservable
inherit errors
in the model's formulation
business cycles are not taken into account
The term structure of credit spreads
Credit spread = Expected percentage loss + Liquidity risk premium
The difference between the average yields on the
risky
zero- coupon bond and the
riskless
zero- coupon bond
credit spread is entirely due to credit risk
Asset- backed securities
issued by "
special purpose vehicle
" (SPV) => SPV own a collection of assets "collateral pool" (collection of loans of a particular type)
measures
expected loss
PV of expected loss
probability of loss (probability of default does not apply)