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The Determinants of Global Bank Credit-Default-Swap Spreads

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Abstract

Using a sample of 161 global banks in 23 countries, we examine the applicability of market-based structural models and accounting-based bank fundamentals to price global bank credit risk. First, we find that variables predicted by structural models are significantly associated with bank CDS spreads. Second, some CAMELS indicators contain incremental information for bank CDS prices. We find no evidence in favor of one model over the other, while the combined structural and CAMELS model performs better than each individual model. Moreover, leverage and asset quality have had a stronger impact on bank CDS since the onset of the recent financial crisis. Banks in countries with lower stock market volatility, fewer entry barriers, and/or more financial conglomerate restrictions tend to have lower credit risk. Deposit insurance appears to have an adverse effect on bank CDS spreads, indicating a moral hazard problem.

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Notes

  1. Augustin et al. (2014) provide a survey on issues related to CDS spread pricing. However, the referenced papers mainly focus on pricing of corporate CDS spreads, while the research on pricing of bank CDS spreads is limited.

  2. They note that the book-value based leverage in their study is not significant due to its high persistence and little variation across the LCFIs during the sample period.

  3. A growing literature analyzes the usefulness of accounting information in pricing corporate CDS spreads, e.g., Arora et al. (2014); Callen et al. (2009); Das et al. (2009); Shivakumar et al. (2011), and Zhang and Zhang (2013). See Augustin et al. (2014) for a survey of relevant studies. Also see, Peltonen et al. (2014) on the determinants of the CDS market.

  4. In a related paper, Eichengreen et al. (2012) apply the principal component analysis to CDS spreads of 45 large global financial institutions. They find that the share of the variance accounted for by the common components is quite high before the financial crisis.

  5. We also use 2-year and 10-year yields for robustness check. Results are similar.

  6. For example, loan-loss provisions to total loans and nonperforming loans ratios are both proxies for asset quality. Their correlations are 47.5 %, which is significant at the 1 % level. As the first variable has 707 observations and the second has only 630 observations, we use loan-loss provisions in our main analysis. As a robustness check, we also conduct analysis using an alternative set of CAMELS variables, including Tier 1 and Tier 2 capital ratios, share of nonperforming loans to total loans, the trading income to total revenue ratio, ROA, and the wholesale funds to total liabilities. The results are similar.

  7. We use Z-score in the baseline analysis because there are many missing observations for Tier 1 and Tier 2 capital. In the robustness check, we use Tier 1 and Tier 2 capital ratios to replace Z-score as a measure of capital adequacy and results are similar. For a related study, see, Barakova (2014).

  8. Considering that the Bankscope coverage increases over the sample period, the change in coverage might drive the change in concentration measure. To mitigate such biases, we use an alternative measure of concentration in an unreported test by averaging the annual concentration value over the sample period. The results remain robust. In addition, our results remain unaffected after using other measures of concentration, such as the fraction of bank deposits held by the three largest commercial banks or the HHI of bank assets (or deposits) in a given country.

  9. We appreciate the editor’s comments regarding currency risk.

  10. Matching global bank CDS and Bankscope data is based on bank name and a series of identification information, such as country, state, city, etc.

  11. Our analysis is conducted in bank-year observations because, unlike the Fed’s Call Report data, the BankScope dataset only has annual frequency. It therefore limits our key explanatory factors such as structural variables and CAMELS variables to a yearly basis.

  12. Neter et al. (1985) (page 392) stated that the rule-of-thumb cutoff value for the VIF is ten for multiple regression models. The VIF values in our models are substantially below the cutoff value, so providing evidence that multicollinearity problems are not present.

  13. As shown in Table 2, the average of leverage ratio for our sample banks is 0.90, with standard deviation of 0.09. The 1st and 99th percentile values are 0.56 and 0.99. This is in contrast to the wide leverage-ratio distribution for corporate entities. For example, Ericsson et al. (2009) report that the average leverage for the corporations in their sample is 0.52. Their 5th and 95th percentile values are 0.23 and 0.80, respectively.

  14. Similar comparison is conducted to compare the structural model with the combined model using the Vuong test for nested models. The Z-statistic is −4.9443 (Prob > F = 0.000), rejecting the null hypothesis in favor of the combined model. A similar test is conducted to examine whether the combined model performs better than the CAMELS model. The Z-statistic is −4.0284 with the p-value of 0.0001, showing that the combined model performs better than the CAMELS. So both F-test and the Vuong test yield consistent results.

  15. Beltratti and Stulz (2012) find that the banks in countries with a formal deposit insurance regime have higher idiosyncratic risk.

  16. We thank the referee for suggesting using the stepwise approach to select crucial determinants.

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Acknowledgments

We thank the insightful and constructive comments and suggestions from one anonymous referee, the associate Editor Prof. Sanjiv Das and Editor Haluk Ünal. Gaiyan Zhang acknowledges the funding from Office of International Studies and Programs of University of Missouri-St. Louis.

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Authors and Affiliations

  1. Fordham University and Bank of Finland, 5 Columbus Circle, 11th floor, New York, NY, 10019, USA

    Iftekhar Hasan

  2. College of Business, Bowling Green State University, 214 Business Administration, Bowling Green, OH, 43403, USA

    Liuling Liu

  3. College of Business Administration, University of Missouri-St. Louis, One University Blvd., St. Louis, MO, 63121, USA

    Gaiyan Zhang

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  1. Iftekhar Hasan
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  2. Liuling Liu
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Correspondence to Gaiyan Zhang.

Appendix

Appendix

Table 10 Variable definitions and data sources
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Hasan, I., Liu, L. & Zhang, G. The Determinants of Global Bank Credit-Default-Swap Spreads. J Financ Serv Res 50, 275–309 (2016). https://doi.org/10.1007/s10693-015-0232-z

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