OFR_2016_Financial-Stability-Report

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and contracts would be crucial. The QFC 24-hour rule could become a natural framework for regulatory fire drills to test data management readiness. Firms subject to the new rules will be required to report their LEIs. Otherwise, given the volume and variety of data, rapid resolution could falter because of untested processes and insufficient data standards. Still, as of mid-2016, the Federal Deposit Insurance Corporation does not require firms to use the LEI. Measuring Risk Concentrations. Risk concentrations in customized instruments — sometimes called bespoke instruments — can challenge supervisors. To evaluate risk concentrations, supervisors need to know if many firms all hold similar risk exposures at the same time or when a small aggregate exposure masks two extremely large, but offsetting, exposures at one firm. Risk concentrations tend to migrate to nonstandard contracts and novel venues where they are harder to measure. Using standardized data to evaluate customized instruments is difficult, as shown in Figure 71. The horizontal axis shows the value of market interest rates and the vertical axis shows the payout on individual credit default swap (CDS) contracts as a function of those interest rates. The dotted lines show the payouts on three types of CDS contracts. The standardized, vanilla CDS Figure 71. Customized Derivatives Can Mask Portfolio Risk 40 35 30 CDS aggregate risk profile, ignoring knock-in/knock-out clauses Payouts on contracts 25 20 15 10 Accurate aggregate risk profile, accounting for knock-in/knock-out clauses CDS with knock-out clause Vanilla CDS 5 0 CDS with knock-in clause -5 Source: OFR analysis 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 Interest rate 88 2016 | OFR 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
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OFFICE OF FINANCIAL RESEARCH 2016 F
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Contents Executive Summary ........
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Chapter 2 provides deeper analysis
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of sharp decreases in U.S. equity a
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1 Financial Stability Assessment Ov
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Figure 3. Real GDP Growth (year-ove
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Figure 8. Ten-Year U.S. Treasury Yi
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Monitoring Shadow Banking Risks The
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An earlier OFR paper proposed an ac
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Figure 17. Size, Liquidity, and Con
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Figure 19. Top Systemic Risk Scores
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The analysis pointed to seven key t
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could. In early 2016, investor conc
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earnings or share issuance. But red
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Fund Monitor, the OFR assessed expo
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2.2 Risks in U.S. Nonfinancial Corp
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Figure 31. U.S. Nonfinancial Corpor
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Figure 36. U.S. Corporate Bond Defa
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Commercial Real Estate Prices Have
Page 44 and 45: 2.3 Cybersecurity Incidents Affecti
Page 46 and 47: Hackers Breach Bangladesh’s Centr
Page 48 and 49: Figure 42. Banks that Include an Op
Page 50 and 51: The industry is working with the pu
Page 52 and 53: Figure 46. U.S. Financial Regulator
Page 54 and 55: 48 2016 | OFR Financial Stability R
Page 56 and 57: eleased the results of an unprecede
Page 58 and 59: Figure 49. CCP Default Waterfall ($
Page 60 and 61: Figure 50: CCP Ratios and Amounts f
Page 62 and 63: cause more-prudent CCPs to appear m
Page 64 and 65: 2.5 Pressure on U.S. Life Insurance
Page 66 and 67: The experience of Japan’s life in
Page 68 and 69: 2015). These benefits are effective
Page 70 and 71: Figure 55. Life Insurance Securitie
Page 72 and 73: Drivers of Insurers’ Systemic Ris
Page 74 and 75: 2.6 Systemic Footprints of Largest
Page 76 and 77: Interest Rates and Derivatives Expo
Page 78 and 79: Figure 63. Components of U.S. G-SIB
Page 80 and 81: Figure 66. Five-Year Changes to G-S
Page 82 and 83: 2.7 Deficiencies in Data and Data M
Page 84 and 85: Figure 68. Examples of Key Post-Cri
Page 86 and 87: Broader Adoption of Data Standards
Page 88 and 89: Better Regulatory Sharing Needed Th
Page 90 and 91: The U.S. Census Bureau surveys publ
Page 92 and 93: The group will also consider how th
Page 96 and 97: Figure 72. Scaling of Data Validati
Page 99 and 100: Glossary 10-Year, 10-Year Forward R
Page 101 and 102: Conditional Value-at-Risk (CoVaR) C
Page 103 and 104: Fire Sale Form N-MFP Form PF Fundin
Page 105 and 106: Liquidity Risk Liquidity Transforma
Page 107 and 108: Pension Funded Ratio Pension Risk T
Page 109 and 110: Stress Test Supplemental Leverage R
Page 111 and 112: Bibliography Adrian, Tobias, and Ma
Page 113 and 114: Board of Governors of the Federal R
Page 115 and 116: www.richmondfed.org/~/media/richmon
Page 117 and 118: Her Majesty’s Government and Mars
Page 119 and 120: Paul, Ruma. “Bangladesh hopes to
Page 121: Zetter, Kim. “That Insane, 18M Ba
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