Fundamental Review of the Trading Book (FRTB) regulation requires firms within its scope to source significant amounts of data, some of which has not previously been required and is difficult to pin down.
The data management challenges of the regulation’s Internal Model Approach (IMA) to market risk capital calculations include ongoing P&L attribution, back testing and identifying whether risk factors are modellable or non-modellable based on real price data that can be difficult to source, particularly in OTC markets with little transparency.
The Standardised Approach (SA) to market risk is also overhauled by FRTB, requiring large banks to take a sensitivity-based approach to market risk and adopt the regulation’s expected shortfall standard risk measure and redefine boundaries between banking and trading books.
This webinar will discuss these and other data challenges of FRTB, consider how required data can be sourced and managed, and outline best practice approaches to compliance.
Listen to the webinar to find out everything you need to know about:
-The data requirements of FRTB
Challenges presented by these requirements
-Modellable and non-modellable risk factors (NMRFs)
-Best practice approaches to successful data sourcing
-Technology and partner solutions to support compliance
10. 1
Data Implications of FRTB | Client “data lakes”
FRTB requires banks to fundamentally change the way they look at data if they are to meet the challenges in
obtaining consistency, alignment, and understanding of provenance.
Reference
Data
Pricing
Data
Transparency
Data
Bank Enterprise Data Store
Front
Office
Risk
Finance/
IPV
Internally generated
data
Consistent &
aligned datasets
sourced from
Bloomberg
Bank “data lake”
Data consumed
consistently
across firm
Banks increasingly need to:
• Consume data consistently across front,
middle and back office operations
• Feed multiple systems across the firm in a
way that provides a reliable frame of
reference
• Seamlessly link data sets using well defined
and ideally, open symbology
• Perform robust queries and validation using
appropriate metadata
Data and model
alignment
