Fundamental Review of the Trading Book (FRTB)
With FRTB, the Basel Committee on Banking Supervision (BCBS) has completely rewritten the rules used to determine how much capital banks must hold in order to offset their exposure to market risk. It replaces the existing standardised approach with a new calculation methodology, drastically changes the way in which model approval is granted and policed, replaces value-at-risk (VAR) with expected shortfall (ES) and alters the boundary between banking and trading books. It is clear that banks will face significant challenges with the introduction of FRTB.
FRTB: The requirements and challenges
For many banks, FRTB implementation will require major changes to the current market risk infrastructure and while there are quantitative challenges it is important to be aware that this also creates a fundamental data challenge. FRTB will require firms to consolidate data from a number of sources both internal and external, including pricing and analytics, position and transactional data from trading or back-office systems alongside model outputs from risk engines. With so many data attributes and data sets to be managed, the depth and volume of data required – both firm-wide and at individual desk-level – is vast. It is essential that banks have a sufficiently scalable architecture in place to store this data and, additionally, a flexible data model that is able to adequately map relationships between datasets. As FRTB has a direct impact on business, getting data management right will result in a multitude of more tangible benefits.
Regardless of whether they currently use the standardised or internal models approach (IMA), banks will need to review their portfolios to determine if existing classifications of desks as trading or banking book are still applicable under the new trading book definitions or whether a revision of desk structure is required.
For banks choosing to apply the standardised approach, existing data feeds will need to be significantly amended and calculators replaced. The data required for the new calculations is more onerous than in the past, with the need to calculate deltas and vegas for prescribed risk factors and also the requirement to run both downward and upward shock stress scenarios in order to provide the data required as inputs into the standardised calculations.
For banks selecting to use IMA, models will need to be run at a desk level for the first time and both backtesting and P&L attribution tests must be implemented and then performed and passed regularly. Even if banks opt for the IMA, they will still need to implement the standardised approach for desks not gaining approval or for desks which fail the tests and are forced to switch from IMA to the standardised approach.
All of AxiomSL’s solutions are built on the same adaptable, high-performance platform. This gives clients the opportunity to reduce costs and complexity of compliance by using one platform to manage all requirements. AxiomSL’s solutions are fully supported and are upgraded to reflect rule and reporting template changes. The unparalleled transparency gives users the ability to drill down from the reports produced to the calculations and source data.
AxiomSL is the leading global provider of regulatory reporting and risk management solutions for banks, asset managers and insurers. It empowers clients with the tools they need to manage their financial, risk and operational requirements, and to comply with regulatory calculation and disclosure mandates around the world.
The AxiomSL FRTB solution supports the standardised approach calculations i.e. the Sensitivities Based Approach (SBA) (including the Delta Risk Charge, Vega Risk Charge and Curvature Risk Charge for all risk classes, for example General Interest Rate Risk and Credit Spread Risk), the Default Risk Charge (DRC) and the Residual Risk Add-On (RRAO). In addition, the solution enables the execution of IMA models written in R within the platform.
- Built in partnership with client
- Drill down capability
- Support for desk level calculations
- Reporting capabilities – Internal and external to regulators
- Not black box – users can view logic being applied
- No fixed input format for data into the platform
- Scalable and robust architecture to support high volumes
- Tailored for jurisdictional variations of the rules
- Ongoing monitoring of rule changes and provision of updates
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