Validation of statistical Model used for HPI scenario expansion for a US based G-SIB
Client : A G-SIB based in North America
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Objective
The scope of the engagement was to validate the statistical model used for HPI scenarios expansion
The model used simple linear regression model to expand national level (US) HPI to (51) state level HPI in 5 different CCAR scenarios – the model used different equation for stress and normal period
CRISIL's solution
Data Preparation: Quarterly HPI forecast was converted to monthly to predict monthly state-wise HPI
Validation Approach: (i) review of model development document, implementation file, and data integrity; (ii) review of conceptual soundness; and (iii) review of model governance
Goodness of Fit and Outcome Analysis: Serial correlation, heteroscedasticity, causality, structural break, etc., were examined for all the models. Out-of-sample statistics, out-of-time validations, backtesting, sensitivity analysis, and stress testing methods were used to ensure the model is appropriate. This included a few graphs and error tests/tables
Client impact
Validated the entire model within stringent timelines, and created extensive and thorough documentation for regulatory submission
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