During MF Global’s debacle, the Trustee reported it transferred 10,000 commodities customer accounts representing $1.5 billion in collateral supporting three million open positions having a notional value of $100 billion. One could think small potatoes when looking at trillion dollar notional values in OTC derivatives markets. However, the Trustee went on to say this represented 40% of all commodity futures exchange activity in US markets and had they not acted swiftly it could have caused serious market disruption. While this says much about quick regulatory response it says a lot more about SIFI and Global-SIFI (G-SIFI) designations based on asset size that doesn’t yet accommodate all significant financial institutions failures.
There is yet another SIFI designation, one based on the criteria of “interconnectedness” yet to be defined. Presumably when the final SIFI criteria is fleshed out firms with 40% market share in financial markets, whether capital, contract or currency markets, will fall within such a SIFI designation. When this occurs we can all take comfort that governments now have the right financial institutions on their radar screens ---- each perhaps like the dots representing airplanes in flight on an air traffic controllers screens. What is even more important is that members of the financial industry gain access to such data, for competitive reasons in redacted form, so they can protect themselves from their neighbors’ excesses. Like members of the airlines industry, each airline needs to know the flight patterns of its friendly competitors in order not to collide.
Aviation as an example
There is a mechanism that the aviation regulator in the US, the FAA (Federal Aviation Administration) uses in a collaborative government and industry program known as the Aviation Safety Information Analysis and Sharing program (ASIAS), which aggregates national aviation incident reports and safety data from different global and local data sources. The program provides a comprehensive and consistent data environment that enables systemic issue analysis and identification. It also complements incident and flight operation data with contextual information on weather, terrain and air traffic control. By combining isolated data sources, the ASIAS program provides a comprehensive and consistent data environment that enables systemic issue analysis and identification. This ultimately helps detect systemic issues earlier and more effectively.
FAA, NASA, engine and airframe manufacturers, airlines and pilot unions flag specific issues and employ analysts to dive into data in search of answers. Ultimately the goal is to more effectively detect potential systemic safety issues before they occur and to mitigate them through more extensive data sharing and better safety information extraction from the data.
Financial Industry as a trial
In financial services, a large amount of data is already collected from financial institutions, but this data is not aggregated in any timely way nor captured at a granular enough level to be used for systemic risk detection. On its own, the aggregation of data will not increase the resilience of the system. Therefore, the industry should first focus on “asking the right questions” in order to determine a set of critical systemic stability indicators that could contribute to an efficient early-warning mechanism. Development of these indicators should be adaptive to keep pace with technological and financial innovation. These indicators should work alongside efforts to enhance data mining and systemic risk management techniques in the new Office of Financial Research, a creation of the Dodd-Frank Act housed in the US Treasurer’s office. Such indicators should also benefit from the G20 Financial Stability Board’s efforts toward a global identification system now being planned to uniquely label all financial market participants and the products they transact.
Application to Financial Services
There are clear differences between aviation and financial services from a risk management perspective. Most significantly, taking risk is undesirable in aviation, whereas it is a vital part of the business model in many areas of financial services. However, both industries generate large amounts of data in their operations and rely on it to manage risk.
Financial services firms already process significant data volumes and share them with their regulators and other parties (e.g., market and pricing providers). Similar to the aviation industry pre-ASIAS, this information is currently for the most part fragmented and not consistently structured, ultimately preventing a coherent view of risk across the various business units of each firm, and certainly between each firm and the overall financial system. During the recent crisis, as with MF Global, it was not possible to get a precise business-wide or system-wide reading on key parameters of systemic stability, such as leverage, liquidity and counterparty connectedness.
The direct ASIAS analogy in financial services would be to create and/or have access to a complete system-wide database of transaction level data for every trade made. While such a deep and detailed data repository in the financial system is technically feasible and may be desirable, the need for centralizing such data is less obvious given new techniques of cloud computing and search technology. However, the broader analogy has some merit, particularly with regards to how the aviation industry uses the aggregated data for developing watchful trigger points for regulatory oversight.
Some regulators are already going down this route in certain instances – e.g., the UK FSA is mandating near-live reporting of banks’ liquidity. The Financial Stability Board is engaging in an exercise that is similar in spirit to what is described here. By getting the indicators (the ‘questions to ask’) right and moving away from blanket data dumps, it is their hope that such efforts can be made both less cumbersome and more effective.
Ultimately, the information should be gathered and monitored at a global level, due to the global nature of financial markets. But starting the effort at a national level with all relevant institutions (particularly collecting ‘near misses’ in a systematic fashion) will be initially more feasible and still be an improvement from today’s situation.
The practical details of such a scheme need to be further worked through and the industry’s concerns over implementation need to be addressed. However, the experience in aviation (and that of the recent financial crisis, where system-level data were often unavailable at crucial moments) suggests strongly that such a repository, if properly constructed, will be of great value to maintaining systemic stability in financial services.
When doing this, it needs to be understood that simple product-level data composition will not enhance stability on its own – the data need to be complemented with systemic understanding (as expressed in “asking the right questions”).
The possibility exists that a common taxonomy as is being pursued by regulators today (i.e., the CFTC’s legal entity identification and product classification system and the ECB’s common reference data concept for financial products) can allow uniquely identified and structured data to be aggregated and analyzed “on-the-fly” leaving the data in place at its point of origination (i.e., at a SIFI or exchange or clearing house). Through indexing and search technology it may be possible to eliminate the actual and continual transmission of large amounts of data to a central data base which, itself, could introduce significant operational risks related to reconciliation issues, even if the data is only at the position level and only sent periodically.
It could be argued that crashing an individual plane is unlikely to cause a systemic issue in aviation, whereas failure of a single financial institution can more easily be systemic (as proven by Lehman and almost again by MF Global). So the ASIAS analogy needs to be applied carefully – it is relevant where it identifies common components or procedures that, if left faulty, could cause a plethora of crashes and thus destroy confidence in air travel. In that spirit, the ‘near miss’ concept in particular is very pertinent. Black swans might then become more apparent, at least some indication of them appearing on the radar screen as with the early warning systems of the aviation industry.
Allan D. Grody is president of Financial InterGroup Holdings