🎯 Project Aim
The aim of the project was to design and implement a new counterparty level Credit Risk metric, called Stress Loss, enabling daily monitoring of stressed exposures.
The scope of the project was initially for Hedge Funds; however was later scaled to all counterparties.
To calculate the Stress Loss, the requirement was to apply historical stress scenarios to individual positions, aggregating risk across asset classes within legal agreements, and aligning exposures to predefined credit limits, with collateral offsets factored in.
👥 Stakeholders
Credit Risk, Global Markets, Model Validation
❗Why It’s Important
Having a framework that incorporates stresses against a counterparty portfolio, for well defined scenarios, it prevents unexpected losses.
By having a transparent, daily view of the risks against each counterparty, proactive steps can be taken to mitigate where risks go above a threshold.
Using realistic, historic stress scenarios (10d, 99.9%ile) means that the Institution is protecting against potential Tail Risk events.
It also encourages teams to structure trades and agreements in such a way as to be more risk-efficient.
The implementation of a clear methodology to calculate stress also supports the ability to set informed limits on trading activity.
By having a transparent, daily view of the risks against each counterparty, proactive steps can be taken to mitigate where risks go above a threshold.
Using realistic, historic stress scenarios (10d, 99.9%ile) means that the Institution is protecting against potential Tail Risk events.
It also encourages teams to structure trades and agreements in such a way as to be more risk-efficient.
The implementation of a clear methodology to calculate stress also supports the ability to set informed limits on trading activity.
✅ Benefits
By measuring potential losses under stress at a granular position level using historical 10-day 99.9% moves, it ensures that risk capture is realistic and severe, not just model-based or theoretical.
Improved counterparty monitoring, to allow Credit Risk to make daily assessments for the amount of risk taken in relation to the assigned limits.
The implementation of the “bottom’s up” approach allows diversification of asset classes, whilst also keeping a level of conservatism.
The calculation integrates the eligible collateral offsets to give a more realistic representation of the net risk.
It demonstrates a proactive, data-driven approach to managing credit stress.
Improved counterparty monitoring, to allow Credit Risk to make daily assessments for the amount of risk taken in relation to the assigned limits.
The implementation of the “bottom’s up” approach allows diversification of asset classes, whilst also keeping a level of conservatism.
The calculation integrates the eligible collateral offsets to give a more realistic representation of the net risk.
It demonstrates a proactive, data-driven approach to managing credit stress.
🛠️ Tools Used
SQL | Python | Excel | JIRA | Confluence
🧩 My Role
Collateral & Margin Business Analyst:
- Definition and implementation of the logic to calculate the OTC Stress Loss Margin, with validation from Credit Risk Exposure Management, and Global Markets
- Created the Python based control framework to validate Trade Populations, Collateral Variance, EUC Population Control, Duplicate Checks & Exclusion/Inclusion Validation
- Creation of Business Requirements, where approved requirements are then documented functionally in JIRA ready for IT implementation
- Perform QA checks on each development step, to check that delivery passes the defined Acceptance Criteria
- Presentation of delivered output, with explanations, to Credit Risk, and Global Markets, for approvals, ahead of release
- Documentation of the process for Production Support to inherit the process for ongoing BAU