Partially as a result of recent regulatory policy, financial regulators1 have been tasked with increased levels of market surveillance and oversight, aimed at ensuring healthy market liquidity and identifying potentially destabilising risks. Included within this mandate is promoting financial stability, mitigating risks which may be of a nature to harm the market as a whole. One of the more prominent areas of concern during the 2008-2009 financial crisis was swaps linked to credit risks, including credit default swap (CDS) contracts. These swaps can be especially difficult to monitor given that they embed multiple risk dimensions, including risks to trade counterparty and to reference entity. Because of a general lack of both regulatory and public data prior to the crisis, international policymakers have called for increased data transparency, and, subsequently, effective methods to use this data for robust financial oversight.
Literature/ Background Risk
Regulatory literature over the last few years has expanded its use of network analysis, and its associated metrics, in order to assess the means by which a bank engages in risk transfer not just between itself and counterparties, but how it fits within the risk transfer network as a whole. Some examples of this use of network tools include the Fed’s monitoring of credit risks passed between the bank dealer network, an IMF working paper on systemic risk as implied by FDIC swap exposures, and the Bank of England’s work on cross-border balance sheet exposures. However, many commonly used network metrics, such as measures of centrality and aggregated accounts of risk flows, are not always well calibrated to the specifics of a given financial market (e.g. jump-to-default risks in credit swaps are not shared by products like interest rate or FX swaps). Results or risk rankings generated by network metrics often only portray one of a number of concerns that may arise from bank activity. In their most general forms, these measures typically focus on a single risk dimension, such as risk to market movements or risk to market volatility, rather than on the relationships between risk dimensions. As seen recently, these relationships can often have the most prominent effects in the area of system stability2.
One approach in addressing some of the oversight gaps resulting from a reliance on metrics alone has been to integrate then with visual analytics. This technique merges analytical reasoning with interactive visual interfaces. A key advantage of using visual techniques is the transformation of multidimensional data into a visual representation which can clarifies important relationships inherent to the network structure and to the idiosyncratic characteristics of nodes/edges. When done well, Perer and Shneidermen (2008) have suggested that integration of these tools can dramatically speed up insight for visualisation users, in our case regulators and policy-makers. For the case study included in this paper, risk transfers effected by individual financial entities along with entity classes (at a determined aggregate level), will be matched with network measures which proxy for entity importance to the network on a global level.
CDS instruments provide insurance against the default of a given institution (commonly known as the reference entity), and require regular payment streams from the counterparty purchasing protection. After an event of company default, where the company cannot fully satisfy it debt obligations, the CDS seller agrees to make the purchaser “whole,” paying against losses accrued on a given set of bonds or the equivalent bond notional. Historically, most of these transactions have been bilateral, with both counterparties having present and/or future, state-contingent, payment obligations. This system of relationships is one of the largest examples of credit intermediation within the financial world3, and takes three primary forms: the assumption of credit risk by dealers from their clients, the distribution of risks between dealers, and the reallocation by dealers of risks back to their clients. In the visualisations developed in the paper, the focus will be on interdealer trading, as reported to data repositories4, including how client risks get intermediated between dealers.
Given that CDS contracts have historically relied on bilateral relationships, network visualisations, when used, have typically focused on how trade relationships and exposures distribute risk by way of ones counterparties (see Yellen, 2013 and Haldane, 2009). Corresponding network measures often then capture, quantitatively, the risks depicted in the associated network diagrams. These measures encapsulate individual forms of risk transfer such as the most prominent vectors of risk between entities (betweenness centrality), entities with greatest distribution of risk (closeness centrality) or even measures of an entity’s importance to risk at a global level (eigenvector centrality). Credit risk summaries at the level of a node, on the other hand, depend critically on a counterparty’s concentration and default correlation to a specific reference entity, risk that is sourced by the product itself. Though market participants rely on contractual valuation models that take into account the joint risk of counterparty and credit risk default, often with difficulty5, this problem is even more acute for regulators who must be able to grasp these joint exposures not just for a single financial firm, but for firms which differ by size, geographical reach and business practices. Establishing a concise method of depicting these variations across firms, while also prioritising risk as it may affect financial stability, is a crucial product of regulatory oversight.
In this paper, we propose a less-commonly used graphical representation, the hive plot (Krzywinski, 2012), matched with numerical summaries, as one way of achieving this efficient rendering. Figure # provides a sample hive plot, where axes consolidate entities by business practices (dealers versus clients) and by counterparty risk versus product risk (the latter by reference entity axis). The individual nodes and edges in the chart display the relationship between counterparties, and can be filtered by a selection of reference entities. To clarify with an example, a buy-side institution (for example, a hedge fund) may purchase CDS protection from a dealer, who then offsets this position through a trade with a second dealer. Both dealers are shown twice, to allow for explicit depiction of the interdealer trade. Finally, the second dealer “closes the loop” by buying protection from a second end user outside the interdealer core of the market (perhaps an asset manager). Detailed depictions like this provide valuable context for an analyst to understand market behaviour and evaluate risks, namely counter party and reference entity, in unison.
In our paper we will outline how network risks of multiple types (risks within the inter-dealer network, risks between dealers and their clients, risks due to reference entity characteristics) evolve during crisis events, during natural periods of risk re-allocation (such as the credit index roll) and how margin and clearing may mitigate some of these concerns.
Our paper suggests that the use of visual tools can play a significant role when integrated with traditional network analytical techniques; this integration is especially powerful when a network visualisation is especially attuned to the idiosyncratic characteristics of a market structure – in our case the hive plot and bilateral CDS relationships. This visual technique allows for the integration of several risk dimensions, such as distinguishing between type and importance of counterparty, along with the importance of product risk, and therefore clarifies financial stability weaknesses. In order to demonstrate the value and potential uses of these visual techniques, we have shown how application to the bilateral CDS market, especially during periods of market instability, can focus regulators on the sources and vectors of potentially cascading failures, along with ways of mitigating these concerns.
1 By “regulator” we implicitly encompass a number of different parties, the most obvious being that of public government market regulators like the SEC, CFTC or FSA. In addition, however, this could include SRO’s such as FINRA or NFA within the US, and exchanges tasked with market oversight.
2 For instance, risk associated to AIG’s CDS position was exacerbated by the positive relationship between negative market movements in its CDS sales and its own credit-worthiness, requiring unfulfillable margin calls by its counterparties.
3 The most recent estimate of global credit derivatives notional exposure (H1 2013) by the Bank of International Settlements is approximately $24tr.
4 The data is sourced from the largest credit swap data repository during the period of study, the Depository Trust and Clearing Corporation (DTCC).
5 See the controversies regarding the use of Gaussian copulas to estimate correlation risks in credit markets.
Paper co-authored by:
- Sriram Rajan, Office of Financial Research
- Richard Haynes, US Treasury Department
- Mark Paddrik, Office of Financial Research