Regulatory Risk Management evolves…
The Basel Committee of supranational supervision was put in place to ensure the stability of the financial system. The Basel Accords are the frameworks that essentially govern the risk taking actions of a bank. To that end, minimum regulatory capital standards are introduced that banks must adhere to. The Bank of International Settlements (BIS) established 1930, is the world’s oldest international financial consortium. with 60+ member central banks, representing countries from around the world that together make up about 95% of world GDP. BIS stewards and maintains the Basel standards in conjunction with member banks.
The goal of Basel Committee and the Financial Stability Board (FSB) guidelines are to strengthen the regulation, supervision and risk management of the banking sector by improving risk management and governance. These have taken on an increased focus to ensure that a repeat of financial crisis 2008 comes to pass again. Basel III (building upon Basel I and Basel II) also sets new criteria for financial transparency and disclosure by banking institutions.
Basel III – the last prominent version of the Basel standards published in 2012 (named for the town of Basel in Switzerland where the committee meets) prescribes enhanced measures for capital & liquidity adequacy and were developed by the Basel Committee on Banking Supervision with voluntary worldwide applicability. Basel III covers credit, market, and operational risks as well as liquidity risks. As this is known, BCBS 239 – guidelines do not just apply to the G-SIBs (the Globally Systemically Important Banks) but also to the D-SIBs (Domestic Systemically Important Banks).Any important financial institution deemed ‘too big to fail” needs to work with the regulators to develop a “set of supervisory expectations” that would guide risk data aggregation and reporting.
Enter the FTRB (Fundamental Review of the Trading Book)…
In May 2012, the Basel Committee on Banking Supervision (BCBS) again issued a consultative document with an intention of revising the way capital was calculated for the trading book. These guidelines which can be found here in their final form  were repeatedly refined based on comments from various stakeholders & quantitative studies. In Jan 2016, a final version of this paper was released. These guidelines are now termed the Fundamental Review of the Trading Book (FRTB) or unofficially as some industry watchers have termed – Basel IV.
What is new with the FTRB …
The main changes the BCBS has made with the FRTB are –
- Changed Measure of Market Risk – The FRTB proposes a fundamental change to the measure of market risk. Market Risk will now be calculated and reported via Expected Shortfall (ES) as the new standard measure as opposed to the venerated (& long standing) Value At Risk (VaR). As opposed to the older method of VaR with a 99% confidence level, expected shortfall (ES) with a 97.5% confidence level is proposed. It is to be noted that for normal distributions, the two metrics should be the same however the ES is much superior at measuring the long tail. This is a recognition that in times of extreme economic stress, there is a tendency for multiple asset classes to move in unison. Consequently, under the ES method capital requirements are anticipated to be much higher.
- Model Creation & Approval – The FRTB also changes how models are approved & governed. Banks that want to use the IMA (Internal Model Approach) need to pass a set of rigorous tests so that they are not forced to used the Standard Rules approach (SA) for capital calculations. The fear is that the SA will increase capital requirements. The old IMA approach has now been revised and made more rigorous in a way that it enables supervisors to remove internal modeling permission for individual trading desks. This approach now enforces more consistent identification of material risk factors across banks, and constraints on hedging and diversification. All of this is now going to be done at a desk level instead of the entity level. FRTB moves the responsibility of showing compliant models, significant backtesting & PnL attribution to the desk level.
- Boundaries between the Regulatory Books – The FRTB also assigns explicit boundaries between the trading book (the instruments the bank intends to trade) and the bank book (the instruments held to maturity). These rules have been redefined in such a way that banks now have to contend with stringent rules for internal transfers between both. The regulatory motivation is to eliminate a given bank’s ability to designate individual positions as belonging to either book. Given the different accounting treatment for both, there is a feeling that bank’s were resorting to capital arbitrage with the goal of minimizing regulatory capital reserves. The FRTB also introduces more stringent reporting and data governance requirements for both which in conjunction with the well defined boundary between books. All of these changes should lead to a much better regulatory framework & also a revaluation of the structure of trading desks.
- Increased Data Sufficiency and Quality – The FRTB regulation also introduces Non-Modellable risk factors (NMRF). Risk factors are non modellabe if certain aspects that pertain to the availability and sufficiency of the data are an issue . Thus with the NMRF, Banks now need increased data sufficiency and quality requirements that go into the model itself. This is a key point, the ramifications of which we will discuss in the next section.
- The FRTB also upgrades its standardized approach to data structuring – with a new standardized approach (SBA) which is more sensitive to various risk factors across different asset classes as compared to the Basel II SA. Regulators now determine the sensitivities in the data. Approvals will also be granted at the desk level rather than at the entity level. The revised SA should provide a consistent way to measure risk across geographies and regions, giving regulatory a better way to compare and aggregate systemic risk. The sensitivities based approach should also allow banks to share a common infrastructure between the IMA approach and the SA approach. Thera are a set of buckets and risk factors that are prescribed by the regulator which instruments can then be mapped to.
- Models must be seeded with real and live transaction data – Fresh & current transactions will now need to be entered into the calculation of capital requirements as of the date on which they were conducted. Not only that, though reporting will take place at regular intervals, banks are now expected to manage market risks on a continuous basis -almost daily.
- Time Horizons for Calculation – There are also enhanced requirements for data granularity depending on the kind of asset. The FRTB does away with the generic 10 day time horizon for market variables in Basel II to time periods based on liquidity of these assets. It propose five different time horizons – 10 day, 20 day, 60 day, 120 day and 250 days.
Illustration: FRTB designated horizons for market variables (src – )
To Sum Up the FRTB…
The FRTB rules are now clear and they will have a profound effect on how market risk exposures are calculated. The FRTB clearly calls out the specific instruments in the trading book vs the banking book. With the new switch over to Expected Shortfall (ES) @ 97.5% over VaR @ 99% confidence levels – it should cause increased reserve requirements. Furthermore, the ES calculations will be done keeping liquidity considerations of the underlying instruments with a historical simulation approach ranging from 10 days to 250 days of stressed market conditions. Banks that use a pure IMA approach will now have to move to IMA plus the SA method.
The FRTB compels Banks to create unified teams from various departments – especially Risk, Finance, the Front Office (where trading desks sit) and Technology to address all of the above significant challenges of the regulation.
From a technology capabilities standpoint, the FRTB now presents banks with both a data volume, velocity and analysis challenge. Let us now examine the technology ramifications.
Technology Ramifications around the FRTB…
The FRTB rules herald a clear shift in how IT architectures work across the Risk area and the Back office in general.
- The FRTB calls for a single source of data that pulls data across silos of the front office, trade data repositories, a range of BORT (Book of Record Transaction Systems) etc. With the FRTB, source data needs to be centralized and available in one location where every feeding application can trust it’s quality.
With both the IMA and the SBA in the FRTB, many more detailed & granular data inputs (across desks & departments) need to be fed into the ES (Expected Shortfall) calculations from varying asset classes (Equity, Fixed Income, Forex, Commodities etc) across multiple scenarios. The calculator frameworks developed or enhanced for FRTB will need ready & easy access to realtime data feeds in addition to historical data. At the firm level, the data requirements and the calculation complexity will be even more higher as it needs to include the entire position book.
The various time horizons called out also increase the need to run a full spectrum of analytics across many buckets. The analytics themselves will be more complex than before with multiple teams working on all of these areas. This calls out for standardization of the calculations themselves across the firm.
Banks will have to also provide complete audit trails both for the data and the processes that worked on the data to provide these risk exposures. Data lineage, audit and tagging will be critical.
The number of runs required for regulatory risk exposure calculations will dramatically go up under the new regime. The FRTB requires that each risk class be calculated separately from the whole set. Couple this with increased windows of calculations as discussed in #3 above- means that more compute processing power and vectorization.
- FRTB also implies that from an analytics standpoint, a large number of scenarios on a large volume of data. Most Banks will need to standardize their libraries across the house. If Banks do not look to move to a Big Data Architecture, they will incur tens of millions of dollars in hardware spend.
The FRTB is the most pressing in a long list of Data Challenges facing Banks…
The FRTB is yet another regulatory mandate that lays bare the data challenges facing every Bank. Current Regulatory Risk Architectures are based on traditional relational databases (RDBMS) architectures with 10’s of feeds from Core Banking Systems, Loan Data, Book Of Record Transaction Systems (BORTS) like Trade & Position Data (e.g. Equities, Fixed Income, Forex, Commodities, Options etc), Wire Data, Payment Data, Transaction Data etc.
These data feeds are then tactically placed in memory caches or in enterprise data warehouses (EDW). Once the data has been extracted, it is transformed using a series of batch jobs which then prepare the data for Calculator Frameworks to which run the risk models on them.
All of the above applications need access to medium to large amounts of data at the individual transaction Level. The Corporate Finance function within the Bank then makes end of day adjustments to reconcile all of this data up and these adjustments need to be cascaded back to the source systems down to the individual transaction or classes of transaction levels.
These applications are then typically deployed on clusters of bare metal servers that are not particularly suited to portability, automated provisioning, patching & management. In short, nothing that can automatically be moved over at a moment’s notice. These applications also work on legacy proprietary technology platforms that do not lend themselves to flexible & a DevOps style of development.
Finally, there is always need for statistical frameworks to make adjustments to customer transactions that somehow need to get reflected back in the source systems. All of these frameworks need to have access to and an ability to work with terabtyes (TBs) of data.
Each of above mentioned risk work streams has corresponding data sets, schemas & event flows that they need to work with, with different temporal needs for reporting as some need to be run a few times in a day (e.g. Traded Credit Risk), some daily (e.g. Market Risk) and some end of the week (e.g Enterprise Credit Risk).
One of the chief areas of concern is that the FRTB may require a complete rewrite of analytics libraries. Under the FRTB, front office libraries will need to do Enterprise Risk – a large number of analytics on a vast amount of data. Front office models cannot make all the assumptions that enterprise risk can to price a portfolio accurately. Front office systems run a limited number of scenarios thus trading off timeliness for accuracy – as opposed to enterprise risk.
Most banks have stringent vetting processes in place and all the rewritten analytic assets will need to be passed through that. Every aspect of the math of the analytics needs to be passed through this vigorous process. All of this will add to compliance costs as vetting process costs typically cost multiples of the rewrite process. The FRTB has put in place stringent model validation standards along with hypothetical portfolios to benchmark these.
The FRTB also requires data lineage and audit capabilities for the data. Banks will need to establish visual representation of the overall process as data flows from the BORT systems to the reporting application. All data assets have to be catalogued and a thorough metadata management process instituted.
What Must Bank IT Do…
Given all of the above data complexity and the need to adopt agile analytical methods – what is the first step that enterprises must adopt?
There is a need for Banks to build a unified data architecture – one which can serve as a cross organizational repository of all desk level, department level and firm level data.
The Data Lake is an overarching data architecture pattern. Lets define the term first. A data lake is two things – a small or massive data storage repository and a data processing engine. A data lake provides “massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs“. Data Lake are created to ingest, transform, process, analyze & finally archive large amounts of any kind of data – structured, semistructured and unstructured data.
The Data Lake is not just a data storage layer but one that can allow different users (traders, risk managers, compliance etc) plug in calculators that work on data that spans intra day activity as well as data across years. Calculators can then be designed that can work on data with multiple runs to calculate Risk Weighted Assets (RWAs) across multiple calibration windows.
The below illustration is a depiction of goal is to create a cross company data-lake containing all asset data and compute applied to the data.
Illustration – Data Lake Architecture for FRTB Calculations
1) Data Ingestion: This encompasses creation of the L1 loaders to take in Trade, Position, Market, Loan, Securities Master, Netting and Wire Transfer data etc across trading desks. Developing the ingestion portion will be the first step to realizing the overall architecture as timely data ingestion is a large part of the problem at most institutions. Part of this process includes understanding examples of a) data ingestion from the highest priority of systems b) apply the correct governance rules to the data. The goal is to create these loaders for versions of different systems (e.g Calypso 9.x) and to maintain it as part of the platform moving forward. The first step is to understand the range of Book of Record transaction systems (lending, payments and transactions) and the feeds they send out. The goal would be to create the mapping to a release of an enterprise grade Open Source Big Data Platform e.g HDP (Hortonworks Data Platform) to the loaders so these can be maintained going forward.
2) Data Governance: These are the L2 loaders that apply the rules to the critical fields for Risk and Compliance. The goal here is to look for gaps in the data and any obvious quality problems involving range or table driven data. The purpose is to facilitate data governance reporting.
3) Entity Identification: This step is the establishment and adoption of a lightweight entity ID service. The service will consist of entity assignment and batch reconciliation.
4) Developing L3 loaders: This phase will involve defining the transformation rules that are required in each risk, finance and compliance area to prep the data for their specific processing.
5) Analytic Definition: Running the analytics that are to be used for FRTB.
6) Report Definition: Defining the reports that are to be issued for each risk and compliance area.