Abstract

This appendix contains the following supplements to the main text: (i) additional facts regarding the size and concentration of the CDS market; (ii) some analysis relating buyer and seller capital to the CDS-bond basis; (iii) a discussion and some support evidence as to why capital might be slow moving in the CDS market; (iv) supportive analysis of the impact of the 2011 Japanese tsunami on CDS markets; and (v) a discussion of why the role of dealers as prime brokers does not impact the stylized facts and results presented throughout the paper.

Highlights

  • How much credit risk is being transferred in this scenario? One potential answer is $70 million, since Counterparty A has sold $70 million to both B and C. Another potential answer is $105 million. To see where this number comes from, notice that $80 million has been transferred for GE and $25 of credit risk has been transferred for Apple; in total there is $105 million transferred

  • The reason is easy to see in an alternative example, which I depict below: Table 2: Summary of Exposures with Two Reference Entities: Example 2

  • Each counterparty has a net zero position (i.e. CRTtC = 0), but this does not mean there is no credit risk being transferred via CDS

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Summary

Motivating Discussion

Much of the data on CDS markets documents the size of the market according to gross notional. It is well-known that gross notional can be misleading. Consider a simple example involving Hedge Fund A and Hedge Fund B. Suppose further that in one transaction, Hedge Fund A sells a CDS on GE for $100 million to Hedge Fund B in one transaction. Hedge Fund B sells $100 million in CDS on GE to Hedge Fund A. Consider the following depiction of CDS positions (across all reference entities) in a fictional market with only three counterparties, denoted by A, B, and C:

A B C Total Exposure
Quantifying the Size of CDS Markets
Concentration Ratios
Herfindahl Analysis
Credit Risk Is Highly Common Across Counterparties
Empirical Results
Overall Net Sellers and Buyers
Seller and Buyers by Reference Entity
Creating Aggregate Indices
A Static View
Counterparty Network Dynamics
Defining Two Alternative Networks
Undirected and Unweighted Network Statistics
Rule of Thumb for Flows from ICE

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