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Trading Credit Default Swaps via Interdealer Brokers

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Abstract

Credit default swaps (CDSs) are among the most successful financial innovations of recent years, which is reflected in the rapidly expanding market. CDS trading occurs in the over-the-counter market, which relies heavily on broker intermediation to arrange trades. We provide empirical evidence that liquidity in the voice brokered market varies with the particulars of the CDS contracts and that the differences in market structure is reflected in the costs of liquidity. Moreover, the brokered and direct interdealer trading markets seem to be well integrated; thus the higher liquidity costs in the brokered market may reflect the value of intermediation. Hybrid market structures, which combine voice brokerage with an electronic platform, are discussed as a viable alternative to fully automated trading systems.

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Notes

  1. Rime (2003) mentions that active currencies in the FX market such as USD/EUR and USD/Yen have shifted almost completely to electronic trading, while less active currencies have not.

  2. While some studies with other markets present a favorable view of the transparency that came with automation, others hold the opposite opinion (see O’Hara 1995; Madhavan 1996; Pagano and Roell 1996; Bloomfield and O’Hara 2000; and Madhavan et al. 2005 for discussion).

  3. Within the last decade, alternative credit derivative products have been developed to satisfy the different needs of counterparties. The basic choices of credit derivatives comprise credit default products, credit spread products, and total return products. Credit default products are commonly used to offset default risk, whereas credit spread products offset the whole credit risk, i.e. the risk of increasing or decreasing spreads. The third type, total return products, transfers both credit and market risk between counterparties. More advanced forms of credit derivatives include credit-linked notes, basket credit derivatives, and asset-backed securities.

  4. Price discovery can be defined as ‘the efficient and timely incorporation of the information implicit in investor trading into market prices’ (Lehmann 2002).

  5. See Duffie (1999) and Hull and White (2000, 2001).

  6. Bessembinder and Venkataraman’s (2004) empirical results support the work of Grossman (1992), who suggests that an upstairs market serves as a pool for unexpressed large orders.

  7. For example, T-zero, a recent spin-off from Creditex, offers standardized electronic post-trade messaging and increased system-to-system connectivity between market participants. The system is also linked to DTCC and automatically sends affirmed trades for same-day legal execution. Thomson’s TradeWeb also offers an STP platform with a direct link to DTCC’s Deriv/SERV trade affirmation and matching service. In addition, TradeWeb CDS users are able to confirm trades seamlessly, view trade confirmation status, and validate open net CDS positions. SwapWire provides a network for trade confirmation in the OTC market and recently updated its system to offer support for ISDA’s novation protocol of September 2005. For instance, this functionality can be used by buy-side clients, who often use deal novation as a mechanism for the early exit of derivative positions.

  8. Markit RED (Reference Entity Database) currently serves as the industry standard and confirms the legal relationship between reference entities that trade in the OTC CDS market and their associated reference obligations (so-called pairs). Markit creates a unique alpha-numeric key called CLIPS (CUSIP linked entity Markit code) to identify both the entity and the pair. RED is seen as a necessary ingredient for the facilitation of electronic trading of CDSs because it helps to minimize the operational risks inherent in the matching/clearing process. As CLIPS have been mandatory since June 2005 for dealers matching on DTCC, increasingly more IDBs will use RED to generate DTCC-eligible trade data.

  9. In July 2002, the National Association of Securities Dealers (NASD) began to report transactions in around 500 bonds through Trade Reporting and Compliance Engine (TRACE), which constituted a major step towards the market’s transparency.

  10. The iTraxx index consists of the 125 most liquid single name CDSs and was exclusively licensed to Eurex by the International Index Company (IIC) in July 2005.

  11. Recent literature has investigated the liquidity premium in bond spreads (Janosi et al. 2002; Houweling et al. 2005), while credit default swaps are modeled to have no liquidity premium due to several reasons. Longstaff et al. (2005) provide the most comprehensive discussion on the issue, noting that CDS are contracts that can be set up arbitrarily, while securities are fixed in supply. This makes CDSs invulnerable to the ‘squeezing’ effects applicable to bonds. Furthermore, if liquidation is wanted, the counterparty simply enters into a new CDS in the opposite direction. Finally, it is quite easy to sell and buy protection with CDS, while it is difficult and costly to short bonds (pp. 2219–2220). Despite these reasons, which neglect the presence of a liquidity premium in prices for modeling purposes, it should be realized that these points show only a relative unimportance with respect to bonds.

  12. Bid-ask spreads have obvious limitations. However, it is difficult to define a single measure that reflects all dimensions of liquidity, leaving the bid-ask spread as a good proxy for analysis.

  13. The restructuring dummy has a value of ‘0’ for the Old Restructuring (OR) clause where minimum restrictions on delivery option are present; ‘1’ for Modified Modified Restructuring (MMR), the valid clause for Europe after June 2003, which constrains the old clause; and ‘2’ for Modified Restructuring (MR), the valid clause for North American entities. The latter is the most restrictive clause overall and reduces the value of the delivery option.

  14. Robustness checks executed by taking the log values of ratings and maturity lead to similar results.

  15. In order to check for robustness, we have also tested regressions with the relative bid-ask spread as the dependent variable. The relative spreads are calculated by dividing the absolute spread by the midpoint of the quotes. In our regression, this affects the seniority, maturity, and rating variables. Due to the fact that subordinated CDSs have higher midpoints that enter into this calculation than the senior CDSs, we have reached parameter estimates with a reverse sign for the rank variable. The same is true for the rating variable in regression (1), since the lower ratings simply indicate a higher midpoint division. Similarly, these regressions had a negative estimate for the maturity variable, which is most likely not because of changing absolute spreads but instead due to increasing midpoints for higher maturities.

  16. In order to eliminate month-end effects, mid-month data are used.

  17. Significance is also reached when IDB midpoints are used as the explanatory variable.

  18. A similar analysis has been carried with relative spreads. Due to the fact that relative spreads are computed by a division of the midpoints, the significance of credit quality is in the opposite direction for Tables 4 and 7. This is most likely due to midpoints increasing stronger than the absolute spreads for low credit quality entities.

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Authors and Affiliations

  1. Institute of Finance, Banking, and Insurance, University of Karlsruhe (TH), 76128, Karlsruhe, Germany

    Yalin Gündüz, Torsten Lüdecke & Marliese Uhrig-Homburg

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  1. Yalin Gündüz
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  2. Torsten Lüdecke
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  3. Marliese Uhrig-Homburg
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Correspondence to Yalin Gündüz.

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Gündüz, Y., Lüdecke, T. & Uhrig-Homburg, M. Trading Credit Default Swaps via Interdealer Brokers. J Finan Serv Res 32, 141–159 (2007). https://doi.org/10.1007/s10693-007-0012-5

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