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The impact of FRTB

Of all the new incoming regulations, few will have a greater impact on financial institutions’ trading business than Fundamental Review of the Trading Book (FRTB).

This Basel Committee on Banking Supervision update to the current Basel 2.5 introduces stricter rules for the treatment of market risk across national jurisdictions and addresses some of the weaknesses of the current framework. In practice it could result in higher capital requirements to protect against trading book  positions.  These  requirements  were  finalised  in  January  2016  and  are  now  in the process of being adopted at a regional and local level.

FRTB highlights

Notable regulation changes include the following:

  • A stricter separation of the trading book and the banking book is required. Banks will need to review their portfolios to determine whether existing classifications of instruments  are  correct.
  • How models are governed has evolved. Banks that want to use internal models must pass a set of tests to bypass the standardised approach.
  • The current Basel 2.5 internal models approach has been made more rigorous. Regulators can now provide approval for internal models at the individual trading desk level instead of at the entity level.
  • Modeling risk for regulatory reporting has changed. FRTB mandates that core market risk calculations be based on Expected Shortfall (ES) as opposed to Value at Risk (VaR) methodology.

The need for data

Banks will  need  access  to significant  data and  analytics to  meet  requirements.  Most  large  banks  will  likely  use an internal model to access three sources of external data: time-series market data, reference data and “real” price data. Large quantities of internal data are also required as part of capital calculations.

For banks using internal models, time-series data is a critical input to the capital models. FRTB mandates that banks conduct ES and Default Risk Charge calculations based on data sets that go back to 2007 and 10-years, respectively.  Because  current  methodologies  use  a shorter calibration period,  many banks don’t  have access  to time-series data stores back this far. These banks will have extra work to do in order to collect  high-quality, time-series data.

Irrespective of whether a bank is using internal models or the standardised approach, accessing the right reference data is a critical requirement of its FRTB response. High-quality and complete instrument reference data allows banks to accurately classify their instruments with the regulatory framework.

Another large data challenge results from the introduction of the Non-Modellable Risk Factor (NMRF) requirements. This specifies that banks using internal models must pass the NMRF test if they want to calculate their capital based on an ES calculation. For this test, banks are required to determine whether they have continuously available “real” prices for a sufficient  set  of  representative transactions.

The requirement to conduct the NMRF test creates two challenges for banks. Firstly, for many OTC markets, executed  trade  and  committed  quote  data  may not be readily available. Secondly, once the data has been sourced, it needs to be organised into a consistent set of data models to be mapped into risk factors in order to  determine whether  they pass.

Synergy with MiFID II

FRTB compliance will have certain synergies with the upcoming European Union legislation, the Markets in Financial Instruments Directive II (MiFID II). This regulation comes into  effect  in January 2018, prior  to FRTB.

MiFID II mandates that trading platforms as well as buy- and sell-side firms  have  obligations  to  publish  trade details. The obligations cover cash and derivatives across all asset classes, subject to an exception for spot FX markets. It is this extensive source of information that could be used as a source of “real” price data under FRTB.

Despite the significant  benefits  that  MiFID  II  post-trade data should bring in determining the NMRF requirements, it is not a true solution for two reasons:

  1. Data models  in  MiFID  II  are  not  necessarily  aligned with those proposed for use within FRTB, so banks seeking to use MiFID II post-trade data for their “real” price  observations  may  need  to  map  the   data  into a new data model.
  1. Post-trade rules  require  each  trading  firm  to  publish trades  that  they  execute,  and  each  buy-  or  sell-side  firm to  report  through  a  new  type  of  regulated  entity  called an Approved Publication Arrangement (APA). The splintered nature of OTC markets in combination with the competition between different APA providers means that the MiFID II post-trade data will be fragmented.

Changing the nature of the game

No bank has ever before had to collect the wide-ranging type  of  data  for  this  type  of  regulatory  purpose.  Banks are responding to  FRTB  by  participating  in  Quantitative Impact Study and industry working groups as well as undertaking proof of concepts. Some more advanced banks have started their implementation phase through either undertaking request for proposals or implementing new systems.

Over  the  long  term,  banks  will  require  a  high-quality data vendor that can provide a broad range of market data, reference data and “real” price data. Taking these necessary actions to remain  compliant  will  help  banks avoid a negative impact on their business.

Download the white paper at: bit.ly/FRTB-data-demands

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