Counterparty Credit Risk (CCR) modeling became popular in the early 2000s as B2 entered into its final phase. Many banks rushed to develop their own approaches or acquire third-party vendor solutions for counterparty credit risk exposure measuring and management.
Soon after, the trend moved to pricing adjustments—where it has continued developing an ever-increasing number of ‘Xs’ to add to the long list of xVAs—and also into more technical innovations to meet very demanding business requirements.
And yet, the CCR product management and development teams at SS&C Algorithmics have had a few very busy years targeted at meeting growing client demands for CCR exposure modeling.
The question is, “why?” What are the drivers for a resurgence of interest in this area?
It is difficult to generalize but, at least in our experience, it seems to be driven by two related factors:
First, recent regulatory changes such as SA-CCR are pushing people to reconsider their regulatory approaches into internal models, either by starting anew or by taking a final push from something they already have in place.
Second, there is an urge to consolidate and maximize what you do with what you have. Risk systems—and in particular CCR systems—are expensive, so the more you are able to do from one of these, the more room you have in your budget.
Capital and cost savings, money talks, system optimization, etc., all are essentially variations of the same theme. It is no wonder that we are seeing more and more people interested in having a discussion about cloud technology, which clearly seems to be the next step in this journey.
So, what are the main challenges that we have encountered and needed to adapt to, and that will also be faced by any newcomer into this space?
- Multiplicity and Consolidation
First, and following the line of consolidation, you want to have a system that can adapt to multiple environments and possible uses. This goes beyond the risk, finance and front office divide, and towards the need to acknowledge that any IMM bank will necessarily have sections of their portfolio under different approaches. Systems should, consequently, be able to adjust and deal with such hybrid environments.
A lot of our recent development effort has been oriented towards functionality aimed at dealing with the above, under the term of “Synthetic Netting sets.”
- Complexity in Calculation
Many things have changed since the origination of CCR modeling, e.g., mitigations with requirements such as mandatory posting of an initial margin between counterparties. The regulatory expectations also go hand-in-hand with market practices; in some cases, it has gone way beyond normal market practice as exemplified by the requirement to capitalize future settlement risk within the MPOR by the European regulator. All in all, it is clear that the entry bar is now significantly higher than what it was just a few years back.
In order to better serve our IMM clientele, our algorithm has substantially evolved to ensure it maintains its parity with both regulatory expectations and common market practice.
- Model Performance Analysis
Finally, from the perspective of the regulator, the most important aspect of the validity of a model comes from the following two items:
- The quantification of the performance of the model via a rigorous backtesting program that provides rigorous insight into model deficiencies; and
- The implementation of a scrupulous model governance process that promptly acts on the backtesting program findings.
These are just a few examples of the challenges that a bank considering starting a process towards internal models will face, and thus, the advantage of having a proven and credible partner in that process. To better understand the challenges of CCR backtesting, download our "Counterparty Credit Risk Backtesting – Ensuring the Relevance of Your Models" whitepaper.
If you’d like to discuss how SS&C Algorithmics can help your institution shift to CCR modeling, please contact us.
Written by Duncan Cryle
Head of Sell Side Product Management, SS&C Algorithmics