This page is for comparison of different credit risk models that used
for Allowance for loan and lease losses (ALLL) purposes and if they can
also be used for other regulatory purposes.
1. Roll rate model
Pros:
- Can see the evolution of the balance rolling for 1 bucket to the
next which can be useful for understanding how the delinquency rate will
move in the forecast.
- The calibration can be very straight-forward.
- The model forecast the loss rate directly, so there is no need to
build separate models for PD and EAD.
Cons:
- Not easy to incorporate economic indicators into the model, which is
required in the new CECL, IFRS 9, and CCAR regulation.
- The pros is also it’s cons. The model does not segregate the loss to
PD and EAD. For IFRS 9, it is not desirable since to measure if there is
“increase in lifetime default risk”, one needs to know the PD instead of
the loss rate.
- The property of the transition matrix is memoryless. The DPD state
information already contains all the information. How the balance
changed before it become the current DPD state is irrelevant. This might
not be true, since intuitively, a balance that is cured from 60+ to 30+
should be different from a balance that went from current to 30+.
- Needs segregation of product charastistics and risk. There might be
a lot of transition matrics needed to be calibrated and there might not
be enough data.
2. Cox proportional hazard ratio
Pros:
- The model gives the whole PD curve as a function of age so it’s easy
to get the 12-month PD and lifetime PD which is an advantage for IFRS
9.
- Survival model is widely used not only in Finance but also other
medical research area.
Cons:
- The survival model is just the PD model. EAD model has to be built
separately.
- The assumption of constant hazard ratio (such that the shape of the
curve should be similar when changing explanatory variables) is likely
to be breached if the left censor point is the reporting date and day
past due is one of the explanatory variable. It should work better for
forecasting at the origination date.
- Difficult to incorporate economic indicator.
3. Discrete time survival model
Pros:
- Don’t need the assumption of constant hazard ratio like the CoxPH
model, so it can forecast at the reporting date and use delinquency
information.
- Easy to incorporate economic indicators into the model.
- Easy to get the 12-month PD and lifetime (remaining life) PD for
IFRS 9 purpose.
Cons:
- Needs a separate EAD model.
- Needs some heavy lifting in coding to build models for each period
since reporting date.
4. Rating migration odel
Pros:
- Since most of the large financial institution build the IRB model
for Basel, it can leverage rating from the IRB rating system.
- The model framework is the classical PD * EAD * LGD
Cons:
- Require separate PD, EAD and LGD models
- Need to adjust for conservatism if the IRB model for Basel is used
as the underlying rating system.