Data Science Applications - Credit Risk Modeling
5 important questions on Data Science Applications - Credit Risk Modeling
What is Cumulative Logistich Regression?
log(P[C<=R]/(1-P[C<=R]) = theta + B1x1 + B2x2 + .....
Parameters are estimated using maximum likelihood.
For each observation you can calculate the probabilities for all ratings.
What is the Rating Migration Analysis?
Markov process, default state is absorbing state. Can find state in X months by using matrix multiplication
Sometimes this process is questioned
- downgrades tend to be more easily followed by further downgrades
- duration dependence effect: the longer an obligor keeps same rating, the lower migration probability
- migration probabilities tend to be correlated with business cycles
What is the Rating Philosophy?
PIT (Point in Time)
- take into account obligor specific, (non) cyclical info
- rating changes rapidly with macroeconomic situation
- PD is best estimate of obligors default during next 12m
- IFRS 9
Through the Cycle (TTC)
- only take into account non cyclical info
- rating robust with respect to macro economic situation
- PD is best estimate of obligors default during credit cycle
- Basel
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What PD calibrations are there?
- sampling variability
- external economic conditions
- internal effects
Basel requires availability of 5 years of data for calibration
Calibrated PD = best estimate PD + conservative add on
Uncorrected cohort DR
- all customers still at risk at beginning of year t (Nt)
- # of customers from Nt that default during year t (Dt)
- DR = Dt / Nt
Withdrawal corrected cohort DR
- withdrawals: 50%
- DR = Dt / (Nt-0.5Nw)
PD calibration: average, maximum, upper percentile or use bionomial itnerval to account for sample variability.
What rating philosophies are there?
TTC: Basel compliant
Both when it comes to calibratio
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