FRTB – Complexity! Are you Ready?

Anja Thoste, Quant Foundry

Many banks have been running FRTB Programmes on and off for several years now, but how ready are banks actually? Given the revised timelines, and the indication by the ECB to commit to these timelines without further postponement, there is not much time left and banks need to be ready with the IMA (Internal Model Approach) implementation by end of 2019/mid 2020 at the latest to meet the waiver application deadlines; there is a bit more time left for the Standard Approach (SA) with a reporting date as of the beginning of 2021. It has to be noted that the IMA waiver application will also need to cover the governance framework and aspects of SA, for example processes to transfer a desk from IMA to SA, and vice versa.

At the Quant Forge division of Quant Foundry, we have a holistic view on the FRTB implementation and are not focussing on specific calculations or methodological aspects which have been discussed in other articles – some of which are listed below. We are aiming at those leading FRTB programmes or sub-projects, as well as all those who are currently involved in implementing FRTB.

As a mandatory component of FRTB, all banks have to implement the Standardised Approach. This needs to be prioritised first, and then incrementally the bank should assess the potential of RWA optimisation by developing and adding IMA models where those models would have the highest impact on capital. The pre-requisite is an integrated and strategic system infrastructure and data model that can be extended to new asset classes, products and functional requirements as required.

The FRTB guidelines are complex and require a complete review and potential restructure of the current front to back market risk infrastructure together with an implementation of a new governance, processes and controls, and workflows. The calculations – SA, ES, NMRF, IMA-DRC, Backtesting and P&L Attribution – are the core components and need to be embedded in a governance framework that includes workflows, processes and controls. Consistency across the calculation components in regard to data (trade, reference, market, organisational hierarchy) within the FRTB framework as well as across other risk areas e.g. counterparty credit risk, SA-CCR and stress testing/CCAR , is essential for an organisation to understand the risk drivers, manage risk efficiently and optimise capital.

The demands on data extend beyond current regulation – challenges such as equity index decomposition, mandate information of the CIU, proxying for the reduced risk factor set and stressed period selection, consistent product and instrument data. The task of implementing an FRTB framework becomes much more challenging as the regulatory requirements have not been finalised yet, continuous regulatory updates are being published as well as there is still a lot of room for interpretation. The diagram below attempts to provide an overview of the components and processes an FRTB framework needs to entail including common components and dependencies.

A few examples of challenges in designing a target operating model for FRTB include: –

Common components across SA and IMA

  • Ability to evidence full trade population and risk factor completeness
  • Frequent organisational changes that require restatement of the organisational hierarchy. Under FRTB changes must not impact the IMA desks – otherwise this would likely trigger a new model approval
  • Consistency of input and output data across the three SA components to allow e.g. fair value capping for securitisations, as well as consistency across SA and IMA for comparison
  • For DRC, approaches for hybrid products (equity and credit) for SA- and IMA DRC need to be specified

SA (SBM, RRAO and SA-DRC)

  • The risk weights for securitisations require an extensive calculation leveraging on the F-IRB or A-IRB approaches and rating waterfalls
  • The RRAO requires the notional which has not been defined by BCBS for FRTB. As suggested in a BCBS FAQ the SA-CCR definition can be used. Existing notionals in banks often do not reflect the economic value and/or are simply wrong. This is a major initiative and needs to be tackled early enough.
  • Lookthrough and non-lookthrough approaches for CIUs and index instruments and waterfall logic
  • It seems a few banks are relying on (internal) models for SA (e.g. alternative approaches for delta and vega sensitivities or JTD); it has to be ensured that these are validated, and respective controls and processes are being set up

IMA (ES, NMRF and DRC)

  • Enhancements to the risk factor discovery and enrichment process must now include Liquidity Horizons, modellabilty and proxying (general vs. idiosyncratic risk, and reduced risk factor set)
  • The IMA calculations require workflows and data flows in particular a) for risk factors to feed into ES or NMRF, idiosyncratic or general risk, and b) to determine the approach for NMRF based on data availability (full, partial or no timeseries, idiosyncratic and general risk)
  • Workflows have to be designed to support the reduced risk factor set (to explain 75% of the overall risk) and stressed period selection, and any dependencies between them

Target Operating Model

Given the remaining time until the IMA waiver approval process starts, as well as reduced budgets for 2019 due to other higher priority projects like Brexit, the Programme Manager needs to push through key decisions by senior managers and sooner than later including:

  • Model Choice: What is the cost of ownership, model effectiveness, efficiency of process and delivery cycle? Although difficult, the cost/benefit to implement an IMA model over the next 5-10 years should be assessed; this needs to include benefits across other calculations (e.g. internal VaR, EC). Rather than doing a tactical implementation to meet the regulatory timelines, it might be more sensible and cost efficient to focus on SA first before implementing IMA. FRTB provides an opportunity for banks to reengineer their market risk framework and system infrastructure not only for regulatory purpose but more importantly for internal risk management purpose
  • Target Operating Model (TOM): Strong leadership across the organisation is required to define an integrated and efficient target operating model that supports standardisation and automation from front to back where possible.
    • Given the complexity of FRTB and high demands on processes and data, this should not be left to the last minute. This would need to include workflows to support the calculations, and any alignments between components e.g. IMA and SA, and common input data and well as an integrated reporting
    • In defining a TOM, data and process ownership needs to be reviewed. A strong recommendation would be to move the responsibility for the Standardised Approach under market risk to provide the right subject matter expertise as well as allowing alignment between SA and IMA. It is common in Tier 1 banks to implement a large part of the FRTB calculations in front office; this has to be taken into account in designing the TOM and it requires the market risk functions (management, IT, methodology and change) to lead in the formulation of methodologies and a future resilient market risk system
    • A good target operating model needs to entail all aspects required by the regulation (as discussed above) as well as linking front office, market risk and finance processes with efficient controls and standardised interfaces as well as common data sources
  • Market Risk Methodology: It has been common in a lot of Tier 1 banks that front office has been implementing all or some of the FRTB calculations. This requires a rethink of the roles and responsibilities for model ownership, model validation, governance and run the bank processes, as well as a close cooperation between front office quants, market risk methodology and model validation
  •  Engagement Model of the FRTB Programme: In delivering an FRTB target operating model and front to back implementation, it is essential that the businesses, front office and market risk IT, market risk change, market risk management, front office and market risk methodology, analytics and finance share a common vision of the future state and work collaboratively. The FRTB Programme Manager should be supported by project managers who are leading the implementation efforts in the respective areas e.g. front office and methodology with the support of senior management in front office and market risk
  • FRTB Desk Structure: An early view of the desk structure is mandatory to facilitate impact assessments and rethinking the business strategy. While the Volcker desks have been used as a starting point for FRTB, they may not lead to an optimal regulatory capital nor do they provide full coverage for FRTB. The desk structure needs to be supported by integrated pilot implementations of the IMA and SA based on actual data and sources (rather than the spreadsheets or standalone/non-integrated implementations often used for the Quantitative Impact Study/impact assessments) for the organisation to get a view on the capital impact
  • For Tier 2 banks, a vendor solution might be the best option to implement best in class calculations that at the same time allows the bank to focus on data standardisation, quality, accuracy and timeliness. (see also Article #2 below)
  • Common Infrastructure: SA FRTB shares common requirements with SA-CCR and a similar approach with SA-CVA. In regard to SA-CCR a common approach for the implementation of the notional and sensitivities could be deployed (it has to be noted here that there is still unclarity around the sensitivity definition and materiality for SA-CCR). In order for an organisation to benefit from these commonalities, these topics need to be considered in the engagement model and programme set up, functional and technical design, and the target operating model

How we can help?

Based on the experience and subject expertise we have at the Quant Forge division in FRTB implementations, we can support the Programme Manager to conduct a short-sprint assurance work on the current project within the context of cost-benefit analysis and the set of desks highlighted for IMA, roadmaps and project plans, deliverables to date, IMA cost vs capital benefit analysis, team skillset, data quality, computation readiness and funding options.

We would provide support in reworking the impact analysis with the view of changing the organisational hierarchy for IMA, defining approaches that optimise the necessary methodology choices and work with the quant teams to build tools that support impact analysis, data quality checks and eligibility monitoring that will be central to all FRTB implementations.

Related Articles

  1. FRTB Target Operating Model, Vivek Shah and David K Kelly, March 2018
  2. FRTB : Building a Better Risk Process a COO’s dream, Chris Cormack, 21 July2018
  3. The lessons to be learnt from Historical, Chris Cormack, 14 March 2018
  4. Addressing the FRTB challenge, Chris Cormack, 12 October 2016
  5. Is FRTB an Equities Business Nightmare? Chris Cormack, 24 October 2016

The Quant Foundry

The Quant Foundry was founded by Chris Cormack and David Kelly to provide professional consulting services, quantitative team augmentation services, software design and development as well as mathematical, social science and scientific research. Our consulting division – Quant Forge – provides subject experts in Market, Model, Counterparty, Credit and Operational Risk who have a track record in delivering complex regulatory-driven programs such as FRTB in Tier 1 and 2 banks as well as providing senior-level assurance.

Anja Thoste is a Director and Senior Delivery Lead in the Quant Forge Division of Quant Foundry, specialising in large regulatory market and counterparty risk change programs in Tier 1 banks