As we have discussed in previous blogs, market data requirements are the foundation for compliance to the Fundamental Review of Trading Book (FRTB) rules.  Market data affects all areas of regulation and is essential to any bank’s compliance program. 

Banks should have observable real prices for risk factors if they are to be included in the internal model.  If real prices are not obtained, supervisors will reject the risk factor and add a Non-Modelable Risk Factor charge to the capital calculation. 

Market data is also needed for computation of risk sensitivities for a standardized approach (SA)-based capital calculation, meaning that the data used for computation of sensitivities is consistent with front office use of pricing information, as required by the regulations.  Costs for a market data utility can run to $15 million for initial setup, with additional costs for routine maintenance and global sourcing of data.1 

We see implementation challenges for banks in three main areas: 

  1. Risk Factor Pricing.  Banks should look at issues such as risk factor analysis, which entails obtaining risk factors for inclusion in the internal models to determine if each risk factor is modelable or non-modelable.  Banks should also manage the liquidity horizon, which is differentiated by which risk factor is necessary for the computation of the expected shortfall. Other areas of concern in risk factor pricing include the new level of segmentation of the different instruments and the assignment of different weights (for example, for creditworthiness of the issuer, or for currency) as prescribed in the rules.
  2. Profit & Loss (P&L) Attribution.  Issues in P&L attribution include integration of data and time series to secure the adequacy of the input data for the computation of the measures of risk and P&L, and changes in the workflow and the definitions of new processes of analysis for each trading desk. 
  3. Market Data Quality Issues.  A key concern here is the non-availability of market data for risk factors. Grouping these within the non-modelable risk factor category may increase the capital requirements due for these risk factors in case of error.

Banks are considering a number of solutions to deal with these challenges, including the pooling of data to overcome the lack of market data.  This approach, however, presents risks of its own, such as the potential for abuse of the framework if uncommitted quotes are provided; this could lead to regulatory sanctions on the entire initiative.  There are also concerns about collusion between financial institutions, which could lead to manipulation of market data.  Strong governance and controls will be needed to prevent any misuse or manipulation of the market data utility. 

In the next blog in this series, we will look at a third data challenge, the data gaps affecting risk calculators across risk factors and asset classes.  

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


  1. “Manipulation threat to FRTB data pooling,”, May 24, 2016. Access at:

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