DTCC released a new white paper that suggests ‘real’ price and non-modellable risk factors (‘NMRF’) requirements under the Fundamental Review of the Trading Book (FRTB) will pose significant challenges to market participants, potentially mandating large increases in the capital that banks must maintain for market risk purposes (market risk capital). However, banks have an opportunity to reduce their market risk capital charges by using ‘pooled’ observable transaction data to demonstrate that the associated risk factors meet the ‘real’ price standards under FRTB.
The FRTB, released by the Basel Committee on Banking Supervision on January 14, 2016, overhauls the market risk capital requirements to drive improvements to the Basel III framework. One of the most significant changes is the introduction of a new risk factor modellability assessment framework based on ‘real’ price criteria. The framework requires banks to provide evidence of sufficient liquidity across market risk factors related to the positions in their regulatory trading book, including those that are capitalized using approved internal models. Under FTRB, the demonstration of market liquidity must meet minimum standards with respect to actual transactions and committed quote volume. It is these ‘real’ price requirements to assess risk factor modellability that have raised concern among market participants.
The white paper analyzes the FRTB ‘real’ price criteria to determine risk factor modellability and its impact on banks that are subject to the market risk capital rules. It also provides insights on the key observations from DTCC’s ‘‘Real Price Data Study”, which internally analyzed over 10 billion over-the-counter (OTC) derivative transactions that flow through DTCC’s infrastructure, and examined the potential benefits to banks using DTCC’s pooled ‘real’ price data. This assessment revealed the following observations: large dealers would benefit from using industry-pooled data over and above their own data; industry data pools demonstrate significantly higher levels of modellability than individual firm data across credit, rates and FX; and dealers have the potential to see a relative reduction of non-modellability across multiple asset classes using pooled observation data.