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The path to becoming SA-CCR-friendly

European banks and early adopters of SA-CCR in the US are already reporting counterparty credit risk under the new methodology. Hit by the increase of capital requirements, they routinely search for exposure restructuring and optimization opportunities to reduce the costs of the new regulation for clients and shareholders alike while keeping the capital charge in check.

Escaping the “black box”

There are a lot of calculation libraries on the market that can help compute the charge under the new methodology. However when it comes to portfolio optimization and understanding the risk, computing the numbers is not enough. 

Since the regulation involves non-linear calculations effects and the need to offset risk within hedging sets, asset classes, buckets, etc – you also need a human-friendly yet advanced tool that allows you to zoom-in and zoom-out calculations interactively, include and exclude products and positions, run scenarios to identify what is driving capital and predict what would happen if you restructure them. 

Factors driving the EAD are different from counterparty-to-counterparty – is the exposure dominated by rates, or FX products? By short or long term instruments? For each particular case you might want to change the view depending on the factor prevailing for the current analysis and continue the exploration:

  • Evaluate the contribution of individual products and trades, inspect the trade details
  • Run a quick What-If simulation to see the impact of potential additional trades for different balance sheets, or changes in collateral and agreement
  • Drill down to see the exposures contributing the most to the capital charge and hence where to concentrate the restructuring efforts

Let’s imagine a credit risk manager notices a large counterparty exposure in short-dated futures. With the SA-CCR self-service analytics she can drill down and inspect the contribution of that exposure into capital, as well as run what-if scenarios to simulate the impact of potential risk mitigation strategies. She can create a watchlist for that counterparty or set a limit. She can create a high-level view and share it with senior management or go as deep into the details of the SA-CCR calculation details as she wants within the same system.

Example of a view built by an analyst to visualize EAD and PFE DtD changes 

The SA-CCR Accelerator

To offer an alternative to black boxes and static data analytics solutions, ActiveViam has developed the SA-CCR Accelerator. Building upon our signature technologies the SA-CCR Accelerator contains pre-packaged but configurable source code. It features all of the necessary calculations needed to meet regulatory requirements, but also all the analytics tools you need to explain and optimize. It has successfully passed the ISDA Unit tests.

Compared to other solutions, the SA-CCR Accelerator offers several unique features to make risk management a true asset to the business and not “just” a reporting function:

  • Viewing exposures in a flexible way, with user defined drill-downs, allocations and incremental analysis of the SA-CCR risks – interactively recomputing contributions based on user selections, 
  • ‘What-If’ framework for users to run scenarios and compare calculated risk numbers “before and after

Additionally, to help achieve stronger data and risk governance, users can leverage: 

  • Data adjustments, and data quality investigations to support adjustments, as well as audit process,
  • Risk data Sign-Off workflow and official risk production.
  • Limits and root cause analysis.

EBA approach to SA-CCR

Under the Basel approach, a transaction is impacting only one risk class. The EBA approach is more sophisticated as it takes into account trade sensitivities to derive the risk classes a trade should be included into. In the SA-CCR Accelerator you can compare both approaches side-by-side.

Running scenarios

The ability to compare  “before” and “after” metrics interactively is particularly beneficial. Typically,  our clients use a What-If framework to perform these experiments:

  • Pre-trade simulations or restructuring events to see the impact at all levels of the non-linear aggregation
  • Change the features of the contracts (e.g., margined/unmargined, CSA features such as MTH, Maturity Date)
  • Perform stress tests
  • Simulations with different Supervisory parameters

As a simple illustration, I built this view below to visualize a counterparty’s EAD before and after a trade restructuring: in my simulation maturity of a trade has been changed to a shorter period. As a result its exposure has moved from the third to the first maturity bucket, and since the first bucket was originally empty, shorter maturity resulted in increased AddOn.

In the below screenshot, the “Base” column shows the current state of the portfolio, and the “Prediction” column shows the What-If simulation.

Example of a view built to predict non-linear impact of a trade maturity changes

Connect from Excel

Many credit risk managers who need to crunch SA-CCR prefer doing so in Excel – the general tool of choice.  For this reason, the SA-CCR Accelerator supports an Excel connection natively, so that business experts may slice-and-dice EAD, EAD parameters and incremental impact of positions through Excel Pivot Tables, among other UI’s.  Big volumes of data are not a problem anymore since risk data aggregation and SA-CCR calculations are performed by the Accelerator running on a server, not on the user’s laptop inside Excel.

The complexity – both on the infrastructure side, as well as with the calculation methodology – are transparent to the end-user, as the EAD, replacement costs, PFE, add-ons and multipliers are just Calculated Fields in an Excel Pivot Table, as an example. Everyone from a junior analyst to the managing director can browse the organization’s counterparty credit risk numbers on Day 1.

Contact us today to explore whether the  SA-CCR Accelerator is the right fit for your organization and can help improve your counterparty credit risk analytics toolkit.

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Anastasia is a quantitative financial analyst and risk management practitioner experienced in modern data analysis tools and frameworks. For the last 10 years she has been working on the edge between IT and risk management as a subject matter expert and product owner guiding development and adoption of risk management and pricing solutions for different banks in Europe. At ActiveViam Anastasia works on Fintech Accelerators helping to deliver best in class solutions meeting modern risk management needs.

Anastasia Polyakova

Anastasia is a quantitative financial analyst and risk management practitioner experienced in modern data analysis tools and frameworks. For the last 10 years she has been working on the edge between IT and risk management as a subject matter expert and product owner guiding development and adoption of risk management and pricing solutions for different banks in Europe. At ActiveViam Anastasia works on Fintech Accelerators helping to deliver best in class solutions meeting modern risk management needs.

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