SIMM: tackling the initial margin obligation in OTC derivatives
Regulatory reforms following the financial crisis have tested banks’ ability to adapt and fundamentally changed business models. The Standard Initial Margin Model (SIMM) is another addition that could reshape many derivatives trading and risk management practices. In this article, Sapient Global Markets’ Thomas Schiebe and Sendi Cigura in partnership with Patrice Touraine and Matthieu Maurice of Global Market Solutions look at what it means for banks and how they must tackle a new wave of data and technology challenges.
Following the introduction of mandatory clearing for standardized derivatives, regulators created measures to deal with non-centrally cleared and non-standardized derivatives. This included the requirement for counterparties to establish bilateral margin arrangements and better operational risk procedures to help reduce overall systemic and counterparty risk.
To ensure a consistent approach, the International Swap Dealers Association (ISDA) developed the SIMM as a method for calculating initial margin (IM) for these instruments. Although still under review in Europe, it went live in the United States, Canada and Japan in September 2016—creating a global imperative for all banks active in derivatives trading to ensure they are prepared.
Mathematical issues associated with IM are relatively easy to solve. However, applying the SIMM methodology for regulatory compliance and addressing the complex data requirements poses a major challenge. Get this right and it becomes a valuable opportunity.
The SIMM is one part of a wider regulatory jigsaw that requires portfolio managers to handle IM for eligible trades and in-scope counterparties as well as centrally cleared derivatives and unmargined positions. Solving SIMM is an important step to creating a holistic approach that extends far beyond compliance.
The hurdles to implementation
The reality is that most firms will need to adapt or replace their solutions to support the SIMM calculations. Existing systems vary greatly across the sector and include standard spreadsheets and manual processes as well as fully integrated and automated collateral management engines. However, few if any of these will be optimized for the new requirement.
In addition, fragmented IT has become commonplace in many banks as operations have developed and diversified. They operate multiple systems for different asset types and a variety of trading books, making it difficult to consolidate all trades into any one system in order to perform the IM calculation. Integration across the process chain will be particularly important. Banks have to implement dynamic aggregation rules across multiple products, IM calculation at the group level and the cost allocation at the entity levelóall of which must be secured in a fully auditable environment.
The first challenges, therefore, are having the right solution at the heart of all this to perform the calculation and being able to import OTC exposure. A risk or front-office system is a good starting point, since it will already contain some of the required data. Even so, it might still need updating and on its own is unlikely to provide all the required functionality. Importing all the necessary information will demand new interfaces, updates to existing interfaces as well as data consolidating, cleansing and testing before it is truly ready to use.
Once the risk or front-office systems have carried out the initial calculations, they must send the information to the collateral management systems. This highlights a further challenge in that the collateral management systems may not have adequate workflows for processing the IM requirement.
Other hurdles include devising effective mechanisms for dispute management and resolution as well as tools for handling new risk factors. The ISDA regularly publishes essential risk factors for the calculation of SIMM, so being able to import these factors, including dynamic risk weights and correlations as well as what-if scenarios, will be crucial.
All this requires banks to normalize their front-office technology. They must do this at the group level and include detailed mapping of every curve bucket for consistency across all systems.
Banks must also confront the IT implications of maintaining a complex eligibility dictionary covering all legal entities within the bank, all local regulators and all trade types and decomposition rules. And when managing IM orders from the risk management team to the collateral management team, they need to be able to post all relevant amounts via the right channels—whether that’s the collateral, margin call or other systems.
Optimizing the dispute management process also calls for IT changes, since banks will have to reconcile IM positions at D+1 and validate all IM postings. In addition, systems will need to accommodate all relevant methodologies for sensitivity computation. Although SIMM prescribes finite differences, it doesn’t mention other methodologies – such as Automatic Adjoint Differentiation or Malliavin Calculus. That means that banks applying these methodologies will have to remove any restrictions to finite differences in order to avoid adverse effects on performance, architecture and workflows.
Comparing SIMM to IM schedules
New IM calculations—whether under the SIMM or more generally the Basel Committee’s alternative IM methodology—will inevitably trigger additional financial costs. This could be directly from the posting itself or indirectly via third-party custody, reporting, reconciliation and dispute management.
The fact that IM posted to a central counterparty (CCP) or third-party custodian cannot be re-hypothecated significantly affects liquidity management and funding costs. However, analyzing SIMM against these IM schedules or clearing costs is not straight-forward—and a clear view of trade profitability will only be possible through pre-deal simulations.
With a schedule-based methodology, the netting effect between trades is extremely limited—although it might be sufficient for buy-side positions where trades are long-dated and always in the same direction. The analysis becomes far more complex where there is considerable netting, so banks will need to do a complete portfolio impact analysis to determine an accurate picture. The SIMM methodology, when applied correctly in these circumstances, could reduce overall IM by as much as five times.
Optimizations, simulations and re-allocations
Understanding the true impact of IM is critical for profitability and cost transparency, but it isn’t simple to achieve. Focusing on just the IM reconciliations and postings won’t be sufficient. IM can lead to direct collateral movements, capital charges, additional transaction and valuation reporting requirements, as well as new compliance costs and trade repositories—all of which form part of the bigger financial picture. By investing in the right IT framework, banks can find smart ways to optimize these processes and achieve significant cost savings.
When it comes to simulations, banks now have a new term, the margin value adjustment (MVA), for the present value of the future funding cost of IM. Under SIMM, the hedging challenge becomes more complicated because banks need to apply the methodology within the Monte Carlo framework. They must also compute forward sensitivities at each node of the simulation grid.
In addition, banks have to reallocate collateral costs at the trade level if they are to assess the true profitability of each transaction. It is mandatory to compute these reallocated metrics on a pre-deal check level because it is the only way for traders to assess profitability and ensure they provide the right price to the final client. However, doing so in real time for every new deal is an intensive process.
The long-term strategic approach
On the surface, the SIMM is a fairly simple model that shouldn’t pose any major mathematical or computation challenges. However, it presents a complex data puzzle that exceeds the capabilities of many existing systems.
Understanding the methodology itself and addressing some initial implementation hurdles is just the beginning. It is only when banks begin to tackle the cost, liquidity and risk implications that they’ll achieve greater and longer-term gains.
The solution has to be in technology—and ultimately, the SIMM is another stepping stone to banks extending their IT capabilities beyond simply regulatory compliance. The insight they gain into true transaction costs and the ability to provide a detailed back allocation through SIMM, for example, will deliver exceptional benefits across multiple business divisions. The upshot is a front-office and treasury-driven approach that enables full cost control and efficient pricing and a collateral management process that can deliver a true competitive advantage.
Thomas Schiebe is a Business Consultant at Sapient Global Markets with a focus on clearing and collateral management, treasury and regulatory reporting. He is responsible for Sapient Global Markets’ collateral survey, regulatory compliance, automation and straight-through processing (STP), as well as developing new collateral and clearing strategies.
Sendi Cigura is a Business Consultant at Sapient Global Markets with more than four years of experience in the capital markets. He is part of the clearing and collateral team and, as such, provides a wide range of advisory services from collateral portfolio to business process optimization.
Matthieu Maurice joined Global Market Solutions GmbH in April 2013 as an Associate Director leading the new German unit, and is mostly involved in XVA projects. He built the CVA management desk of the Mittelstandsbank at Commerzbank AG in August 2010 with a focus on pricing, monitoring and hedging the counterparty risk inherent to OTC.
Patrice Touraine is one of the co-founders of Global Market Solutions and is regional managing director of its UK-based subsidiary, responsible for the UK and Nordic markets. He started his career at BNP Paribas Frankfurt in 2000 as a developer and went on to have several experiences in consulting companies and exposure to different financial software vendors.