Georges Bory, managing director and founder, Quartet FS

Central clearing counterparties (CCPs) operate in a highly-regulated market – one that is undergoing significant change, part in thanks to a wave of post-crash regulation. In particular, the Dodd-Frank Act and the move to a centrally cleared model for OTC derivatives is having a huge impact. In many ways it represents a double-edged sword for CCPs: on one hand it provides them with a fantastic opportunity to expand their businesses, yet on the other it has significantly increased the complexity of risk management within their organisations.

The issue is that within Dodd-Frank, the CTFC has included new cleared swaps customer protection standards. Often referred to as “Legal Segregation with Operational Commingling” (LOSC), these fundamentally change how firms treat cleared swaps customer positions and related collateral. This change, along with mandates such as Customer Gross Margining (GCM), has meant that CCPs have had to start looking at risk in an entirely new way. They have needed to start managing the risk associated with far more portfolios, at a far more granular level. They have also needed to start looking at risk collectively, across all of their asset classes. All of these changes have meant that a move to real-time analysis is now critical.

In a nutshell, the reformed derivatives landscape means that CCPs now have an even bigger responsibility for managing risk than ever before. Their ability to manage members’ positions and issue accurate margin calls has a direct effect on broader systemic risk and, ultimately, the overall stability of the markets.

So what’s the problem? As is often the case, the issue is legacy technology and infrastructure. Many CCPs’ underlying systems simply don’t give their analysts the visibility and flexibility they need to manage risk on a real-time basis. Legacy risk systems are often batch-orientated, meaning they deliver analysts snapshots of P&Ls, margins, collateral and overall market risk information at 20-30 minute intervals throughout the day. Typically, this means extracting and storing sets of data each time to perform queries. Such a system is ill-suited to coping with the new complexities involved in instruments such as swaps, particularly credit default swaps. It means that the mark-to-market process takes a lot of time and effort, as analysts manually work through the results to determine the outputs.

This issue is only exacerbated by the fact that existing systems often mean analysts have to look at multiple pre-aggregated sets of information. If they want to look at information in a new way, the technology team has to carry out significant development work. And segregated tools naturally don’t deliver a consolidated view of risk across all asset classes and portfolios – for instance after looking at interest rate swaps, analysts will often have to look at options on a different screen.

This all links to the issue of performance. For CCPs, high performance is business critical. They normally handle millions of trades and prices every day. Systems that rely on pre-aggregating positions from a database make it very difficult for analysts to build positions on the fly. As a result, the risk analysts using the system will lack insight, while the database will likely struggle with the high load. This means the business will be unable to achieve the performance and response times needed. In the new environment of increasingly complex instruments and ballooning volumes, this approach simply will not cut it. To achieve the required detailed, real-time view using such a system would mean trebling or even quadrupling the size of the analyst team, which will almost certainly be cost-prohibitive.

So what do CCPs and their analysts need from their infrastructure in order to cope with the new normal? Well, for a start it needs to deliver speed and performance. Systems that use in-memory computing have an advantage here. More traditional OLAP cubes might be able to deliver on speed, but will struggle to deliver on performance and cope with high volumes. The system needs the ability to keep pace with multiple feeds of data, centralising data from across an array of venues, asset classes and countries. In addition, any infrastructure needs to be able to host and add multiple calculations as well as provide adequate support for ad hoc queries.

Integration is also key: a system that can seamlessly plug into a CCP’s existing technology stack removes the costly and time-consuming need to introduce new proprietary hardware during implementation. Flexibility and customisability are also major factors. Off-the-shelf, out-of-the-box solutions are all well and good so long as their features precisely match the needs and risk analyst profile of the CCP. But the chances of this are slim. CCPs are complex organisations each with their own idiosyncrasies and specific needs. A system that can be tweaked and modified – for instance, the building of a custom calculation engine – will inevitably be able to better serve the needs of the CCP in question.

This goes to show that selecting the right system with the right features is only half of the challenge. The manner of implementation and level of support from the provider is also important. A close partnership with the technology provider from the very start of the project is necessary in order to reap the full benefits of customisation. It will make potentially beneficial modifications easier to spot and build. A good provider will also offer on-site consulting to help the CCP get the most from the new platform. Training and advice for analysts and IT users will mean that by the time the system goes live, those who need to use it will have a deep understanding of its features, maximising performance and usability. A close partnership with the provider during implementation will also make it easier for them to link the new platform to other critical applications in the CCP’s business.

For CCPs that can get this transition right, the potential business benefits are myriad. Equipping analysts with real-time insights gives analysts full visibility over the effects of new trades added to a portfolio, as well as the effects of changes to market data on existing portfolios. This means they can see the current P&L, react to events as they happen, deliver timely mark-to-market, and gain better control over market updates. With a good system, they’ll also be able to compare this insight to historical scenarios at the touch of a button. This results in deeper analysis of breaches and events, and faster, more accurate resolutions.

This in turn means greater protection and reduced risk for the CCP’s members. It will be able to instantly notify its customers of abnormalities so they can take action before the next banking cycle occurs. A good, automated system will even be able to detect an incorrectly entered price before the client is aware of the effect on their P&L, enabling analysts to escalate and remedy the situation before it even becomes a problem. In effect, it means that analysts no longer need to proactively monitor the markets or system for issues themselves. On the contrary, the system will proactively alert them to any issues. This means analysts can spend more of their time and skills on fixing problems, rather than looking for them.

All of this automation – combined with collapsing multiple tools into one – equals greater efficiency, which in turn leads to significant cost savings. It frees up analyst time in such a way that CCPs can meet the new requirements without significantly adding to headcount. It also means that CCPs avoid the large and costly development cycles required to satisfy the growing demand for ad-hoc specific views at increasingly granular levels of detail across multiple lines of business. And partnering with an established provider means avoiding the sheer time and hassle involved in building an equivalent in-house system from scratch.

The new environment is here, now: time is a luxury CCPs simply do not have. The new environment is also, of course, characterised by increased competition between CCPs. Those that can offer effective, real-time risk management – while keeping prices down thanks to a lower cost-per-trade – will be able to attract and service greater volumes and stay one step ahead of their competitors.