In our previous blog, we looked at the data challenges related to data gaps in risk calculators, specifically examining those that can appear when using the Standardized Approach (SA).  However, banks taking the Internal Model Approach (IMA) face challenges of their own, including: 

  1. Data sourcing.  The revised IMA approval process proposed under the Fundamental Review of Trading Book (FRTB) rules puts the onus on banks to obtain the data for market risk calculations, as well as for developing a robust testing mechanism to obtain approval for use of internal models.  Data sourcing for IMA models present challenges such as managing complex risk factor mappings containing different asset classes; having a clear process of “non-modelable” risk factors for identification and implementation, and mapping liquidity horizons for different asset classes.   The IMA framework also specifies that the risk factors have to be supported by an external, verifiable price, rather than the internal prices many banks use for risk calculation.
  2. Assumptions.  FRTB rules detail the process for the profit and loss (P&L) attribution for the internal models, which requires full revaluation methods rather than the approximation methods banks currently use.  To use full revaluation methods, banks will need to use data for full sets of positions; they will have to create systemic assumptions to fill in the missing data.  This may not sit well with regulators, who may insist on the SA calculation in the absence of hard data to back the internal models. 
  3. Data taxonomy.  As is the case with SA, a consistent data taxonomy is essential for all risk computation within the bank.  The IMA approach calls for addressing products booked outside the normal data ecosystem, which may present bespoke data challenges. 
  4. Rules interpretation.  There are data issues related to the interpretation of the rules for Risk Theoretical P&L for satisfying the P&L attribution using the IMA approach.  This means that additional guidance is needed from the supervisors to avoid delays related to incorrect implementation of the P&L implementation models in the banks. 

In the next two blogs in this series, we will look at how banks can address FRTB data challenges, using tools such as sound data sourcing, calculation and management strategy.  

For more information see SlideShare deck: “Fundamental Review of the Trading Book (FRTB) – Data Challenges


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