OFR_2016_Financial-Stability-Report

(blue dotted line) has a constant payoff that doesn’t change with interest
rates. The customized CDS contracts (red and dark blue dotted lines) have
“knock-in” and “knock-out” clauses triggered by changes in interest rates. A
credit event triggers CDS protection. Like a burglar alarm that goes off only
when “armed,” a knock-in clause (or knock-out clause) defines a second
variable that controls whether the CDS’s credit trigger is active (or inactive).
The solid lines show the aggregate payout on a contract that has both
knock-in and knock-out clauses. The purple solid line shows the accurate
payouts with information about those clauses, and the blue solid line shows
what those payouts would look like without that information. Clearly,
the existence of knock-in/knock-out contingencies is critical information.
Unless regulators have a standardized way to collect contract terms about
such clauses, these differences may be ignored. Without information about
knock-in or knock-out clauses, regulators might be led to believe that aggregate
market exposures to a specific change in interest rates are benign (blue
line), when in fact those exposures are highly volatile (red line).
Scalability Challenges
The abundance of data highlights the need for strong data management.
Data management systems and practices are increasingly mismatched to the
scale of the four Vs of “big data”—volume, velocity, variety, and veracity.
Regulators risk being overwhelmed by the increasing volume, arrival rate
(velocity), and variety of data. New data collections will introduce new challenges
of data quality (veracity) (see Flood, Jagadish, and Raschid, 2016).
New approaches to data management are needed to meet these challenges.
Data volumes have grown exponentially in recent decades (see Figure
72). Legacy processes cannot simply scale up to collect, clean, integrate,
analyze, and share information by using bigger storage and faster processors.
Rather, supervisors and firms need new processes to address the challenges
of big data and to fully leverage the information big data can provide. Data
processes designed for firm-level supervision will face scalability challenges
as they are stretched to monitor the system as a whole.
One example is the need to accurately identify entities across different
sources and data systems to allow supervisors to assemble a picture of the
overall system. However, financial utilities, firms, and regulators use proprietary
identification schemes requiring processes to align entity data across
these systems. This situation is a big-data problem, because, without a coordination
mechanism, the number of alignments to manage grows much
faster than the number of identifier sets involved. Rather than continually
scaling legacy processes to link different identification schemes, a different
approach, such as the LEI, would better address the scalability challenge.
The LEI defines each legal entity only once, facilitating data-quality management
by eliminating different methods of referring to the same entity.
Key Threats to Financial Stability 89