Automating Model Testing of Pricing and Market Risk Models
Client : Large US Investment Bank
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Objective
To help a large US investment bank speed model validation and monitoring time, reduce model risk and eliminate costs by automating model testing of pricing and market risk models.
CRISIL's Solution
Built a suite of tests for different models and respective use cases on the client proprietary platform to reduce the overall model validation timelines
Set up a nine-member team (a healthy mix of quants and programmers), led by an onsite project manager
Leverage team's strong experience in pricing models with programming in C++, Python and R
Sample tests implemented:
MC convergence
Hedge backtesting
Assumptions
Stress scenarios
Model stability
VaR grid granularity
Other features:
Flexible test interface that could be used for CCAR and other mandates
Allowed definition of further tests
Interactive results on a dashboard
Client Impact
Helped automate tests across models to reduce model validation time by ~40% for low-risk models and ~25% for high-risk models
Increased efficiencies in the model monitoring process, enabling faster results for high-risk models on a monthly basis, and for lower risk models on a semi-annual basis
Achieved reductions in model risk; exceptions from the scheduled batch immediately flag any potential model risk issues
Helped the bank reduce timelines and the number of contractor personnel required for model validation, minimizing costs
CRISIL solution covered quantitative tests for MC convergence, hedge backtesting, stability testing, assumptions and limitation testing, stress scenarios, VaR grid granularity, etc.
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