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2016 | OriginalPaper | Buchkapitel

11. Does the Tail Wag the Dog? The Effect of Credit Default Swaps on Credit Risk

verfasst von : Marti G. Subrahmanyam, Dragon Yongjun Tang, Sarah Qian Wang

Erschienen in: Development in India

Verlag: Springer India

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Abstract

We use credit default swaps (CDS) trading data to demonstrate that the credit risk of reference firms, reflected in rating downgrades and bankruptcies, increases significantly upon the inception of CDS trading, a finding that is robust after controlling for the endogeneity of CDS trading. Additionally, distressed firms are more likely to file for bankruptcy if they are linked to CDS trading. Furthermore, firms with more “no restructuring” contracts than other types of CDS contracts (i.e., contracts that include restructuring) are more adversely affected by CDS trading, and the number of creditors increases after CDS trading begins, exacerbating creditor coordination failure in the resolution of financial distress.

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Vorheriges Kapitel Experimental Exploration into Macro Economics

Nächstes Kapitel Financial Crisis and Liquidity Trap Some Theoretical and Policy Perspectives

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The invention of CDS is attributed to J.P. Morgan, which sent to Exxon Mobil in the aftermath of the Exxon Valdez oil spill in 1994 (Tett 2009). In this pioneering transaction, J.P. Morgan bought protection from the European Bank for Reconstruction and Development against the default of Exxon Mobile.

Empirical evidence regarding the above theories appears inconclusive. Ashcraft and Santos (2009) find that, after CDS inception, the cost of debt increases for low-quality firms and decreases for high-quality firms. Hirtle (2009) finds no significant increase in the bank credit supply after CDS inception.

The recent decline in the absolute priority deviation during bankruptcy resolution, documented by Jiang and Wang (2012) and Bharath et al. (2010), is consistent with tougher creditors and coincides with the development of the CDS market.

There could be other reasons why lenders may be unwilling to restructure the debt of a firm in financial distress. For example, they may believe that the borrower could eventually go bankrupt, even following a debt restructuring.

Acharya and Johnson (2007) suggest that bank lenders engage in insider trading in the CDS market. Therefore, borrowers may also wish to broaden their lender base if they anticipate that the lenders can exploit informational advantages via their CDS positions.

Markit provides end-of-day “average” indicative quotes from contributing sell-side dealers using a proprietary algorithm. In contrast, both CreditTrade and GFI report trades and binding quotes.

Most CDS firms in our sample are in the manufacturing (SIC 2, 3), the transportation, communications, and utilities (SIC 4), and the finance, insurance, and real estate (SIC 6) sectors. We control for industry fixed effects throughout our empirical analysis.

See Siegel and Castellan (1988) for the construction of the test and Busse and Green (2002) for a discussion and applications of the test.

Both the odds ratio and marginal effect provide more intuitive interpretations of the logistic regression coefficients. However, these two numbers can differ significantly: the odds ratio is calculated as the exponential of the coefficient estimates (e.g., \(\exp (2.373) = 10.730\)), whereas the marginal probability is calculated using the event probability at the chosen setting of the predictors (\(p\)) and the parameter estimate for CDS Active, (\(\beta_{CDSActive}\)), that is, the marginal effect = \(p(1 - p)\beta_{CDSActive}\).

Saretto and Tookes (2013) also use the first of these instrumental variables, Lender FX Hedging, which is motivated by the findings of Minton et al. (2009). We have also considered two other instrumental variables: TRACE Coverage and Post CFMA. The likelihood of CDS trading increases after the implementation of TRACE and after the enactment of the Commodity Futures Modernization Act (CFMA). The CDS effect is also significant using those instruments, although those two instrumental variables are not our first choices.

Because we are using average FX hedging activity across all lenders and underwriters for a firm, the potential selection effect due to the possibility that “bad banks switch their lending to bad borrowers” at the individual bank level will be mitigated considerably. Furthermore, this selection effect is likely to be small because firms’ banking relationships are generally stable and do not change dramatically over time. The same argument regarding the mitigation of the selection effects due to aggregation across all bank lenders in the case of Lender FX Hedging also applies in the case of Lender Tier 1 Capital.

The \(\chi^{2}\) test statistic is 2.436 (p-value = 0.119 for one degree of freedom) in our sample for the two instrumental variables with one endogenous variable, CDS Active.

We thank an anonymous referee for suggesting this method. Edmans et al. (2012) and Hochberg and Lindsey (2010) provide thorough discussions of the FIML estimation approach. We implement the FIML estimation using Stata.

The LIML estimator is more efficient than the two-stage least squares estimator but is not as sensitive to model specification. In addition, LIML is a special case of FIML in which only a single equation is overidentified. LIML estimation is used and discussed in detail by Hochberg and Lindsey (2010). The Heckman two-stage analysis is also a LIML. The results reported in Table A18 of the Internet Appendix demonstrate that the effect of CDS Active is significant. However, the estimation results could be inconsistent because the outcome variable in our bankruptcy analysis is binary. Therefore, we focus on other econometric approaches.

The outstanding positions include both buying and selling positions of all market participants. Ideally, we should use buying positions of the lenders, but our data do not distinguish those positions. The maximum value of CDS Notional Outstanding/Total Debt is 4.14, which is suggestive of overinsurance for such firms and the potential presence of “empty creditors.” We thank an anonymous referee for observing that this measure could be contaminated by the positions of speculators in the CDS market. Unfortunately, we cannot consider this issue in this analysis because we do not have access to investor-level data.

CDS firms can also become more exposed to credit risk if they take on more debt after CDS trading begins. Indeed, Saretto and Tookes (2013) demonstrate that firm leverage increases following the inception of CDS trading. We also confirm that firms exhibit higher leverage after the start of CDS trading (see Table A21 of the Internet Appendix). We control for firm leverage before and after CDS trading begins in our regressions.

We also find that firms are less likely to encounter financial distress after the inception of CDS trading (Table A22 of the Internet Appendix). However, this finding is not robust. Although the coefficient estimate for CDS Active is always negative, it becomes less significant when we cluster standard errors by firm or use an instrumented measure.

The construction of the dataset is detailed by Chava and Roberts (2008). We thank Michael Roberts for providing the DealScan-Compustat linking file.

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Titel: Does the Tail Wag the Dog? The Effect of Credit Default Swaps on Credit Risk
verfasst von: Marti G. Subrahmanyam
Dragon Yongjun Tang
Sarah Qian Wang
Verlag: Springer India
Buch: Development in India
Print ISBN: 978-81-322-2540-9

Electronic ISBN: 978-81-322-2541-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-81-322-2541-6_11

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