10:25 pm - Monday December 18, 2017

Next Level for Trade Repositories – Part II

QUANT MEASURES

Size refers to the scale of activity (eg volumes) or positions (eg outstanding notional or mark-to-market amounts) in a defined population. Financial institutions with large notional or mark-to-market amounts or volumes may pose greater scope for systemic disruptions, ceteris paribus, than similar institutions with smaller notional or mark-to-market amounts and volumes when trading in the same OTC Derivative classes. Size is the area that may require the least level of data detail. In terms of depth, it may be sufficient to have aggregated data, but broken down to a level of sufficient detail to make the aggregation meaningful. A minimum would be the OTCD class, product type, underlying risk type, currency denomination, origin country of underlier and counterparties, and maturity. TR data can provide insight into positions built up by one or more entities that may include SIFIs.

Concentration refers to the relative role of individuals or groups of financial institutions within a market segment. The build-up of relatively large volumes of activity or relatively large positions (as measured by notional or mark-to-market amounts outstanding) in some defined population could increase systemic risk. In terms of depth, authorities may want to see position-level data. Additionally, some high-level questions can be answered using aggregate data. Authorities may wish to see position-level data to analyse the concentration of exposures among the institutions holding the relevant OTCD positions. Aggregate data, which lack information on bilateral relationships or identities of the counterparties, are only useful when analysing the concentration of positions by underlier, but they do not reveal who holds those positions. Authorities may also need position-level data to answer any questions on concentration of exposures among financial institutions, both to (i) counterparties and to (ii) reference entities. Regarding the breadth of data needed, an authority may have an interest in data for counterparties and underliers within its legal jurisdiction. An authority may have an interest in data for all market participants globally. The identity may be needed for the analysis of certain issues. An authority interested in examining the relative concentration of exposures of a particular entity in a particular market segment will need named data.

Interconnectedness refers to the nature, scale and scope of obligations that arise between and among institutions. Analysis of interconnectedness involves describing and analysing the network of links across participants within a segment of the OTC Derivatives market, and/or across different segments. It shows who the central players are, where the vulnerable links are and how the shape and characteristics of the network change over time. Analysis of the network complements the information on concentration, and underpins the assessment of how far market participants are exposed to common shocks. Understanding interconnectedness is crucial for assessing the likelihood and extent of contagion in the financial system. For example, it may help to signal the potential for so-called liquidity spirals, where margin calls in one market segment may affect financial institutions liquidity needs in other, related markets. To analyse interconnectedness, authorities would need to be able to construct a complete network of exposures or positions. As in the case of concentration analysis, authorities would require position-level data in terms of depth. In terms of breadth, authorities would typically require data for counterparties and underliers within its legal jurisdiction. An authority may also have an interest in all institutions globally and all underliers regardless of the country of origin, given the global nature of the exposures.

A disruption in an area of the market that supports financial activity such as trade or post-trade infrastructure can be a significant source of systemic risk, both for financial institutions who rely on these markets for their funding and risk management activities, and for other, related financial markets. Sound market infrastructure policy, to the extent practicable, should be informed by data on the underlying market structure. It follows that a well functioning market needs a robust structure (pre-trade and posttrade) supported by liquidity providers such as large global dealers. A high level of detail would be needed to analyse the structure of markets. Here the depth provided by transaction-level data would be necessary. Monitoring changes in the total trading volumes, the trading frequency or the role of certain liquidity providers could alert authorities to potential changes in liquidity in those markets and to changes in their systemic importance that might call for market structure changes. In terms of breadth, data on all counterparties globally would typically be needed, as the aim is to understand the structure of the market as a whole. This insight could not be gained if the authority only had access to data on the institutions operating within its own legal jurisdiction, which is the current status in most, if not all, jurisdictions where cooperation arrangements are required for data transfer.

RISK REFLECTIONS

Systemic risk refers to the potential that an event, action or series of events or actions will have a widespread adverse effect on the financial system and, in consequence, on the economy. Authorities with systemic risk mandates are concerned about systemic risk because it not only has the potential to harm a large number of investors and market participants, but because it also can have a widespread negative effect on financial markets and the economy

TR data should facilitate authorities’ systemic risk analysis by providing information that can be used to study size, concentration, interconnectedness and structure with respect to institutions (including systemically important financial institutions (SIFIs) and systemically important financial markets and infrastructures). The precise data requirements are likely to depend on the particular question at hand, and may also differ across authorities.

Trade Repositories face risks that, if not controlled effectively, could have a material negative impact on the markets they serve. The primary risk to a Trade Repository is operational risk, although other risks may hamper its safe and efficient functioning. As part of its core recordkeeping function, a Trade Repository must ensure that the data it maintains is accurate and current in order to serve as a reliable central data source. The continuous availability of data stored in a Trade Repository is also essential. Specific operational risks that a Trade Repository must manage include risks to data integrity, data security, and business continuity. Because the data recorded by a Trade Repository may be utilised as an input by the Trade Repository’s participants, relevant authorities, and other parties, including other stake holders and service providers, all trade data collected, stored, and disseminated by a Trade Repository should be protected from corruption, loss, leakage, unauthorised access, and other processing risks. In addition, a Trade Repository may be part of a network linking various entities (such as CCPs, dealers, custodians, and service providers) and could transmit risk or cause processing delays to such linked entities in the event of an operational disruption.

Conclusion

By centralising the collection, storage and dissemination of data, TRs can play an important function in providing information that supports risk reduction, operational efficiency and cost savings for both individual entities and the market as a whole. In addition, TRs can serve to enhance the transparency of information to relevant authorities and the public, promote financial stability and assist in the detection and prevention of market abuse.

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