Validating CECL Model for a Fixed-Rate Mortgage Portfolio

Client: US Bank

 

Objective

 

To validate the Probability of Default (PD) and Loss Given Default (LGD) models for a fixed-rate mortgage portfolio for the client.

 

CRISIL's Solution

 

  • Project Parameters
    • Validation based on logistic regression for PD model and Zero-One Inflated Beta Regression for LGD model. This includes the following steps:
      • Quantitative validation of the Logistic regression and Beta regression models
      • Development of the challenger model using alternate approach/information
    • The Logistic Regression and Beta Regression model to estimate the PD and LGD respectively for Mortgage portfolio are:
      • logit(p) = β0+ β1 * fico + β2 * mi_pct + β3 * cltv + β4 *dti + β5* Unemployement Rate; PD =(1/(1+𝑒^(−𝑙𝑜𝑔𝑖𝑡(𝑝))))
      • LGD = β0+ β1 * ltv + β2 * dti + β3 * House.Price.Index.Level
  • Data Quality & Audit
    • Replicated development dataset from raw files available in the general ledgers
    • Validation of data cleansing and preparation steps (e.g., validating imputation and transformation of data)
    • Routine checks in verifying the inclusion of data corresponding to full economic cycle, data relevancy
    • Validated the macroeconomic variables data from the internal repository which gets updated frequently
  • Validation of Key Aspects
    • Alternative approaches like contractual term, weighted average method using default/prepayment used to estimate the life of loan
    • Current default definition tested against the regulatory definition, underlying assumptions and role of senior management
    • Forecast horizon and macro-economic model forecast of reasonable and supportable forecast validated
  • Selection of Initial Pool of Variables
    • Variable selection process used in model development document and multi-collinearity validated
    • Assigned different significance levels for different variables and options to filter models with user-specified signs for different variables
  • Model Replication & Challenger Models
    • Replicated the entire variable selection process using information provided in the model development document
    • Verified the model coefficients along with the significance of the variables and verified the economic intuition of the sign of the variables chosen
    • Developed the challenger model using alternative approach/different variable
  • Independent Testing
    • Performed Model Fitting Tests, Coefficient Stability Analysis, Assumption Testing, Seasonality Check, Accuracy Tests of Model, Sensitivity and Scenario Analysis
    • Performed back-testing analysis between actual and predicted PDs and LGDs

Client Impact

 

Validation project completed on time and on budget. 

Questions

 

Looking for high-end research and risk services? Reach out to us at:

 

United States
1-855-595-2100/
+1 646 292 3520

 

United Kingdom
+44 (0) 870 333 6336

India
+91 22 33 42 3000 /
+91 22 61 72 3000