Regulatory Future-Proofing of Pre-Trade Architecture for Cleared and Non-Cleared Derivatives

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Written by Paul Walsh, Head of EMEA Consulting at Vox Financial Partners

Many Investment Banks are looking at business simplification, including product and process optimisation, to reduce overall costs. Consolidation of infrastructure is seen as a primary enabler to achieve this. However, observers of the pattern of regulatory change will see that this journey is also advocated and encouraged by the regulators, as demonstrated by the portfolio of change introduced since the financial crisis of 2007-2008.

In the wake of the financial crisis, the G20 agreed that all standardised derivatives contracts should be traded on an exchange or electronic trading platform and cleared through central counterparties (CCPs). Since then, there has been an ongoing push to minimise the divergence between pre- and post-trade risk management.

Around the same time, CVA was introduced to account for the risk of potential default of a counterparty on a derivative agreement. Later, CVA became more commonly described as xVA, with FVA (funding) and DVA (benefit of own default) included. Each ultimately accounted for in the trading price and monitored through risk and financial control processes.

Further complexities were subsequently introduced with the inclusion of OIS (cost of funding collateralised derivatives) into the trading price and discussions around factoring in KVA (cost of holding Reg capital) and MVA (cost of posting initial margin) at some future point.

With the more recent introduction of Best Execution as part of MiFID II in 2018 and more stringent control around pre/post-trade transparency and Algo/HFT Trading, including measures such as circuit breakers, threshold management, real-time risk monitoring, and monitoring through surveillance, it is clear that regulators continue to drive the management of trading risk and market/counterparty risk to be both more timely and also more closely aligned across all products.

Significant events still to play out

Brexit

With many organisations still migrating a portion of their business from their London booking Entity to an EU-based entity, there is a significant amount of work managing client, contract and trade migrations ongoing. This work will be complicated further by platform changes that must take place at the same time.

Fundamental Review of the Trading Book (FRTB)

Although the introduction of FTRB is now delayed to January 2023, it still drives significant business and infrastructural changes, with the need to segregate businesses between Banking and Trading book, ensure clarity and approach in each trading desk, and leverage Internal Model Approach (IMA) for calculation of expected shortfall and non-modellable risk factors where appropriate.

IBOR

With the migration away from LIBOR and other regional overnight rates onto Risk Free Rates (RFRs), there will be a material infrastructural change that must be aligned with external parties such as CCPs, in tandem with contract and process adjustments with counterparties.

The scale of this ongoing change means that despite the imperative, it is challenging for Banks to optimise their operational setups and manage large-scale externally driven change simultaneously.

Typical infrastructure challenges faced by many Banks

Given the changes in the broader market and the necessity to “keep up” with regulatory adjustments, many Banks continue to find themselves with a less than optimal infrastructure. Common inefficiencies experienced include:

  • duplication of systems and infrastructure (multiple data stores, multiple “calculation engines,” duplicated functionality, etc.)
  • lack of model alignment between Front Office Trading and Risk & Financial Control, leading to P&L and risk discrepancies/adjustments, and to challenges in meeting regulatory demands
  • fragmented and/or duplicated management and ownership of instrument, market, and trade data
  • multiple operational/support teams to manage different setups
  • duplication of processes (model validation, market data sourcing and management, management of trade and life-cycle events, limit management, reconciliation processes, etc.)

Consolidation/Simplification Model and Approach

Several different consolidation or simplification approaches can be adopted, dependent on the strategy and starting position of the Bank.

Centralised platform

Adoption of a single centralised platform, either across all asset classes or on a per asset class basis. The central solution holds all key information and supports Trading, Risk Control, Financial Control, and core Settlement/Clearing processes, including feeding regulatory transaction reporting solutions.

Main features:

  • Single platform used to manage trading risk, market and counterparty risk, settlement, CCP connectivity, and to operate as a golden source for all departments
  • The solution encompasses reference data, market data, and trade repository
  • Single analytics library and calculation infrastructure used by all parties

Benefits:

  • Can be maintained by a single IT group and a single Quant group
  • Front-Office/Back-Office reconciliations and data duplication minimised
  • Processes can be streamlined for instrument, market data, and trade life-cycle event management processes
  • Increased opportunities for the unification of trading and risk control processes
  • Migration to this model can be achieved in a phased manner

Challenges:

  • May not fully support all functional nuances required by all departments
  • May be more complex to manage due to a single centralised backlog of changes to support all groups
  • May introduce some volume limitations for very high-volume businesses
  • Although this is a stated aim of many organisations, the model has not been proven utilising vendor platforms for very large or complex businesses, and most organisations cannot afford to create an internal solution to manage the full breadth of complexity

Separate platforms

Front-Office, Risk Management, Financial Control, and Operations each utilise their platform at an aggregated level for the function across multiple asset classes and potentially even at an asset class level.

Main features:

  • Each core solution tailored to the specific needs of one group
  • Key data must remain aligned between the core systems
  • Valuations must be aligned between the pre-trade and post-trade risk management platforms (though there could be some timing differences in delivering updates/changes to each, which may be beneficial)

Benefits:

  • Less likely that compromises will be required by one group due to the needs of another
  • Specialisations of asset class and specific product-based volumes can be accommodated in a more tailored manner
  • Likely to be less change from the current model than other approaches and can be executed in a phased approach

Challenges:

  • High likelihood of duplication of infrastructure, process, and Analytics/Quant solutions
  • Likely duplication of core data management (Trades, Instrument, and Market data)
  • Data and process duplication leads to increased reconciliation, increased operational risk, and increased cost to the business
  • New regulatory-driven near-real-time risk monitoring regimes and best execution regimes become more challenging to meet due to the disparate nature of the solution

Hub and spoke model

Comprises central store(s) for Trades, Reference Data (Instruments), and Market Data, leveraged by separate Trading, Risk, and Settlement/Clearing processes.

Main features:

  • Centralised hub(s) operating as Trade repository, Instrument repository, and Market Data repository
  • Clear governance model as to how the central repositories are updated and maintained up-to-date, including clear responsibilities for management of the golden copy of the data at various stages in the trade cycle
  • Clear operational model determining how the “spoke” systems receive appropriate updates as necessary
  • Can additionally be leveraged to support streamlining of cross-business processes

Benefits:

  • Single ownership and streamlined process for management of core data
  • Ensures alignment between various platforms on core data
  • Supports clean model for reconciliations and data governance
  • Supports specialisations of various functional components to allow parallel evolution minimising contention
  • Migration to this model can be achieved in a phased manner

Challenges:

  • Some duplication of infrastructure, process, and Analytics/Quant solutions (although some mitigation is possible, e.g., use of single analytics library across multiple platforms or functions)
  • New regulatory-driven near-real-time risk monitoring regimes and best execution regimes remain challenging to meet due to the disparate nature of the solution

Considerations that will impact the approach

Scale of operation

Smaller operations with less complexity and volume will find it easier to move towards a single, more centralised model. Specifically, organisations with a more “vanilla” set of products/asset classes can leverage an extension of a standard “Treasury” model. This will lead to a potential compromise from all departments on some specifics and provide benefits of shared infrastructure (applications, calculation infrastructure, model libraries, etc.), shared data, and opportunities to streamline and unify processes.

Historical setup

Organisations with a more diverse infrastructure, potentially due to historical mergers or acquisitions, may find migration to a single central solution extremely challenging. Additionally, organisations with a very diverse product offering, or very high volumes in specific products, may find that a more distributed model will be necessary for at least the short/medium term to meet these specialized businesses’ demands.

Analytics, pricing, and risk calculations

Less complex organisations will have more straightforward analytics requirements. Under many circumstances, these needs can be met directly by vendor solutions, both for intra-day trading risk and also for VaR, historical simulation, Monte Carlo calculations, or FRTB, leveraging High Performance Computing (HPC) or grid computing solutions where necessary.

In more complex environments, vendor-only analytic solutions are unlikely to be sufficient. This will lead to a need to integrate in-house specialised libraries, either directly leveraging vendor integration APIs or capabilities or via a less direct solution whereby results are calculated externally via in-house solutions and subsequently injected into the Trading/Risk management platform.

Approach for a more regulatory future-proofed model

It is clear that from a regulatory perspective and also as sound business management, it is becoming more and more necessary to ensure that all pre- and post-trade activities (that can impact the cost of a trade over its lifetime) need to be transparent where possible in the pre-trade phase and should be available on a timely basis throughout the trade life-cycle. This will enable appropriate trading risk management, transparency, and monitoring from a counterparty and market risk control perspective while ensuring any margining/collateral management activities that may be required are factored into the process transparently from the outset.

The optimum approach is likely to be the centralised model for less complex/smaller businesses or a derivative of the Hub and spoke model for broader, more complex businesses and those with some specialised product or asset class needs.

There will be an ongoing push from the regulators to ensure all these activities operate quickly and seamlessly. However, without drive and focus to streamline operations, it is clear that organisations with fragmented architectures and sub-optimal cost profiles will see these challenges further exacerbated, and costs will continue to increase over time.

Ultimately, with the continuing low-interest environment, the survivors will be those that can move to a more streamlined and standardised infrastructure whilst managing the additional complexity of ongoing changes such as Brexit, FRTB, and IBOR in an agile and efficient manner.

At Vox, we regularly help financial institutions to manage the complexity of change, be it Regulatory, Digital Transformation, or Data related. If you need help, get in touch with Phil Marsden at phil.marsden@voxfp.com or visit www.voxfp.com to learn more.

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