Other parts of this series:
- Fundamental Review of the Trading Book (FRTB) Rules Pose New Challenges to Banks
- Banks’ Trading Frameworks Under Pressure
- Fundamental Review of Trading Book: Three Big Issues for Banks
- Fundamental Review of Trading Book Data Issue: Risk Sensitivities Sourcing
- Fundamental Review of Trading Book Data Issue: Market Data Sourcing
- Fundamental Review of Trading Book Data Issue: Risk Calculators Data Gaps
- Fundamental Review of Trading Book Data Issue: Risk Calculators Data Gaps – Part 2
- Fundamental Review of the Trading Book: Addressing Data Challenges
- Fundamental Review of the Trading Book: Addressing Data Challenges – Part 2
- Fundamental Review of the Trading Book: Transforming Market Risk Practices
In previous blogs, we have examined the data challenges related to data gaps in risk calculators, including those that can appear when using the Standardized Approach (SA) and the Internal Model Approach (IMA). No matter which approach is taken, an effective Fundamental Review of Trading Book (FRTB) program needs sound data sourcing, calculation and management strategy to address these challenges.
Some of the major steps that banks should take include:
- Identify a consistent set of sensitivities. Banks should have a methodological approach for categorizing sensitivities or risk exposure for individual risk classes. Calculation methodologies should be consistent across different areas of the bank. Ideally, sensitivities are calculated only once—using a “golden source” calculator—and then used by different teams throughout the bank as needed. The definition of sensitivities across the front office and risk management team should be the same, which can be achieved by having a common taxonomy for both teams.
- Define a centralized architecture for sourcing and data. There should be a central repository for all risk sensitivities within banks, receiving data from different golden sources and storing it by risk class, category, tenor and risk factor, respectively. This will entail identification of golden sources of sensitivities calculation across risk classes, as well as the creation of standards for sensitivity data sourcing. Among other considerations, data quality standards for managing high-quality risk sensitivities data for internal and external audit approvals should be established.
- Manage IMA risk factors and liquidity horizons. Dimensions of analysis here include taxonomy—such as individuation of criteria and indicators for distinguishing between modelable and non-modelable risk factors—and data sourcing, such as participating in data pooling initiatives within the industry, or subscribing to third-party vendors for obtaining real prices. Another data sourcing consideration is the identification of data providers and establishment of vendor relationships to obtain real pricing information. Computations should be structured to facilitate including or excluding the trading desk considered eligible (or ineligible) for the Internal Model.
In the next blog, we will look at additional steps such as planning for profit and loss attribution and managing SA risk sensitivities, all designed to support flexible, agile FRTB implementation.
For more information see SlideShare deck: “Fundamental Review of the Trading Book (FRTB) – Data Challenges“
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