Tim Lind, Managing Director at DTCC Data Services.
A decade on since the global financial crisis, large swathes of financial regulation aimed at addressing the weaknesses exposed during that time have come into force. Among many lessons learned in terms of market risk, there were two specific gaps that regulators wanted to address: ensure risk model sensitivities are appropriately calibrated to account for tail risk; and ensure banks are sufficiently capitalized to account for illiquidity in OTC asset classes. The Fundamental Review of the Trading Book (FRTB) published by the Basel Committee on Banking Supervision (BCBS) in January 2016 seeks to close those gaps.
FRTB represents the next phase of market risk capital rules and introduces a new set of data challenges that banks must overcome in order to avoid dramatic increases in capital allocation. The challenge for banks is that internal models used to assess risk will be dependent on trade data that is difficult to obtain, particularly for illiquid instruments.
This article outlines the data challenges that banks face and how best they can be addressed, as well as the imperative for market participants to start preparing now for the internal model application phase that will be introduced in 2019 to ensure readiness for full FRTB implementation in four years.
The fundamental aim of FRTB is to address issues in the market risk capital framework that surfaced during the global financial crisis. In short, banks will need to allocate more capital to less liquid instruments with higher risk profiles. The framework requires banks to provide evidence of sufficient liquidity across market risk factors related to the positions in their trading book, including those that are capitalized using approved internal models.
These models can only use risk factors that fit a certain real-price criteria, and each of these factors must be supported by a minimum of 24 ‘real price observations’ a year with a maximum of one-month between two observations. Real-price data is defined as such when a bank has conducted an actual trade between arms-length parties or from a committed quote.
Risk factors that cannot be incorporated into internal models, which can constitute more than 50% of all risk factors in some cases, are known as non-modellable risk factors (NMRFs). NMRF requirements under FRTB will potentially mandate large increases in capital that banks must maintain for market risk purposes (market risk capital).
Banks have an opportunity to reduce their market risk capital charges by using pooled observable transaction data to demonstrate that associated risk factors meet the real-price standards under FRTB. One of the unique factors of FRTB is that it will require unprecedented collaboration between banks and data suppliers to capture and normalize the maximum number of trade observations to reduce their overall number of NMRFs.
Concerns around NMRF requirements were raised by market participants during the BCBS consultation process. However, the latest update from the Committee, published in March 2018, states that there has been no compelling evidence in the form of actual data to support these concerns and that without this the BCBS does not propose revisions to the NMRF rule. Based on this stated position, market participants should start to prepare now for FRTB with the mindset that it is highly unlikely that the proposed risk factor rules will change significantly from where they stand currently.
"One of the unique factors of FRTB is that it will require unprecedented collaboration between banks and data suppliers to capture and normalize the maximum number of trade observations to reduce their overall number of NMRFs."
Any individual bank, regardless of its sophistication and risk infrastructure, cannot solve the challenge of NMRF requirements on its own. In 2017, DTCC conducted a new ‘Real Price Data Study’ which analyzed 10 billion over-the-counter (OTC) derivative transactions. It revealed that by using pooled observation data, dealers have the potential to realize a 50% or greater reduction in non-modellability (by notional) in credit, rates and FX; and a 20% or greater relative reduction in equity positions.
Further, the research found that industry data pools demonstrate significantly higher levels of modellability than individual firm data. Based on this clear evidence, the optimal model to mitigate the impact of FRTB market risk capital charges is a collective one where banks pool their data to prove that associated risk factors meet the real-price standards. No actual pricing of individual contracts will be exposed in this process, so pooling of trade data can be achieved while protecting the proprietary trading strategies of dealers.
Although the new implementation date for FRTB is 2022, market participants need to start preparing for FRTB now as 2019 marks the first phase of the thematic review of the internal model’s approach (IMA). That phase involves the consultation, planning and creation of actual internal model applications for which banks will need real-price observable data.
This process will also enable market participants to understand how increased capital allocations will impact their overall trading structure and understand their overall P&L attribution for individual trading desks. In some cases, the cost of capital may outweigh the revenue in trading certain asset classes or markets, which could lead to banks withdrawing liquidity from the market. These types of strategic decisions require time and implementation needs to be completed by the time the capital accord comes into effect, which is why we are now seeing FRTB programs at many banks in high gear mode.
If market participants are to properly calibrate market risk capital requirements under FRTB, they will need access to pooled price observation data which will maximize the levels of achievable modellability while minimizing non-modellability. Further, given that the FTRB rules have the potential to impact what products banks continue to make markets in there is a need for them to start the planning process now to ensure readiness for the internal model application phase. Without the requisite data and ample amount of preparation time, the challenges posed by FRTB may be hard to overcome.
FRTB represents a significant evolution in risk management to close gaps in existing methodologies. However, if not properly implemented it could have negative impacts not just on banks, but on the market and economy in general. If higher capital charges result in banks withdrawing liquidity from the market, the cost to manage credit, currency, interest rate, and commodity risk will increase for end users such as corporations, insurance companies, and asset managers.
The key lesson we should have learned 10 years ago is that the origin of crises begins when market participants withdraw liquidity from the market. Preparation, focus, and attention on data are going to be essential to ensure FRTB achieves the desired outcomes and doesn’t actually lead to higher costs to manage risk for the institutions that need it most. The stakes are high and the entire economy will certainly bear the consequences if FRTB is not appropriately applied and provisioned.
This article first appeared in Data Management Review on June 26, 2018.