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Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe

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Abstract

Employing main and sector-specific investment-grade CDS indices from the North American and European CDS market and performing mean-variance out-of-sample analyses for conservative and aggressive investors over the period from 2006 to 2014, this paper analyzes portfolio benefits of adding corporate CDS indices to a traditional financial portfolio consisting of stock and sovereign bond indices. As a baseline result, we initially find an increase in portfolio (downside) risk-diversification when adding CDS indices, which is observed irrespective of both CDS markets, investor-types and different sub-periods, including the global financial crisis and European sovereign debt crisis. In addition, the analysis reveals higher portfolio excess returns and performance in CDS index portfolios, however, these effects clearly differ between markets, investor-types and sub-periods. Overall, portfolio benefits of adding CDS indices mainly result from the fact that institutional investors replace sovereign bond indices rather than stock indices by CDS indices due to better risk-return characteristics. Our baseline findings remain robust under a variety of robustness checks. Results from sensitivity analyses provide further important implications for institutional investors with a strategic focus on a long-term conservative portfolio management.

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Notes

  1. The final recovery rate is a fundamental part of the CDS index return calculation (Eq. 10 in Sect. 3.4). Using two-stage auctions to settle CDS contracts ensures transparency and leads to a standardized recovery rate of the underlying debt of the defaulting reference entity. We refer to Helwege et al. (2009) for further detailed information on the auction process of CDS contracts.

  2. We additionally perform an in-sample procedure. However, this approach is based on the less realistic assumption that the investor is able to perfectly forecast future portfolio returns, return volatilities and return correlations, i.e. estimation errors are not considered. As a consequence, we observe overestimated portfolio benefits of adding CDS indices when employing the in-sample procedure, which is in line with empirical findings provided by Bessler and Wolff (2015) and Bessler et al. (2017). Therefore, we do not report empirical results from the in-sample procedure in this paper but provide them on request.

  3. Following Bessler et al. (2017), we implement a 12-months rolling window since the return and the covariance matrix estimations yield to the best performance of the mean-variance approach in terms of the Sharpe Ratio when using this rolling window length. Bessler et al. (2017) further show that a lag longer than 12-months has almost no explanatory power for the return estimation, and that shorter rolling windows (1–6 months) cause a major increase in the portfolio turnover rate. In addition, employing shorter rolling windows, the number of observations, which is used for the estimation of returns, declines. Consequently, estimated returns are more volatile and more prone to outliers.

    Nevertheless, as we analyze portfolio benefits from adding CDS indices during two crisis periods (global financial crisis and European sovereign debt crisis), we employ shorter rolling windows of 9, 6 and 3 months as a sensitivity analysis. Setting shorter rolling windows in this context provokes that the portfolio optimization process reacts more sensitively to return and risk changes of portfolio assets (e.g., Bessler et al. 2017), which is especially observed during crisis periods. Indeed, we find that portfolio effects are sensitive to changes in the rolling window lengths, however, our baseline findings (Sect. 4.1) are generally reiterated, even under shorter rolling window lengths. We do not provide the results in this paper but provide them on request.

  4. We perform a robustness check and vary the risk aversion coefficients in Sect. 4.2.

  5. We present a comprehensive analysis of varying transaction costs and allow for short sales during later robustness checks in Sect. 4.2.

  6. The upper volatility bound is calculated by employing the MSCI World Index and the Barclays Global Government Bond Index as independent benchmark indices with a proportion of \(80\%\) (\(0\%\)) stocks and \(20\%\) (\(100\%\)) bonds for the aggressive (conservative) investor. We perform a robustness check and vary the volatility bounds (together with the risk aversion coefficients) in Sect. 4.2.

  7. Since we define the VaR as ‘loss’, the sign of the computed VaR is positive.

  8. Note that \(\omega _{c,j,t_+}\) differs from \(\omega _{c,j,t}\) due to asset price changes during time t and \(t+1\).

  9. Corporate CDS indices as employed in our analysis approximately cover 73% of the outstanding gross notional of multi-name CDS for the corporate sector. The coverage (ratio) is calculated by means of the Depository Trust & Clearing Corporation (DTCC) database.

  10. Two defaults, Fannie Mae and Freddie Mac, affected the CDX.NA.IG and CDX.NA.IG.Fin during our sample period. Therefore, supplementary data is retrieved from Creditex, which provides cash settlement values for credit derivative trades, data of credit event auctions and final prices with regard to ISDA settlement protocols and in cooperation with Markit (www.creditfixings.com).

  11. The CDXN includes the 125 most liquid North American entities with an investment-grade rating and comprises various segments, e.g., Consumer Cyclical, Energy, Financials, Industrial and Telecom, and Media and Technology. The CDXNF includes sub-sector indices as listed above, except for the financial sector. This sector is separately included in the CDXF. The CDXHV contains 30 entities of the main index with the highest 5-year CDS spreads average over the last 90 days prior to the initiation date of the high volatility index composition (Markit 2015).

  12. The iTrE includes 30 Autos & Industrials, 30 Consumers, 20 Energy, 20 Technology, Media, and Telecom, and 25 Financials. The iTrNF comprises the sub-sectors as listed before, except for the 25 entities from the financial sector, which are included in the iTrF. The iTrHV contains 30 entities of the main index with the highest 5-year CDS spread average over the last 90 days prior to the initiation date of the high volatility index composition (Markit 2015).

  13. Note that CDS indices are always used ‘on-the-run’ since the newly introduced ‘on-the-run’ series is more liquid than the old ‘off-the-run’ series. We roll-over the CDS contracts if (1) the regular index roll-over process is carried out (semi-annually) or (2) a new version of a series (e.g., due to a credit rating-shift outside the investment-grade, liquidity deterioration or a default of one or more reference entities) is introduced.

  14. Coupling smooth transition functions with structural GARCH, Dungey et al. (2015) develop an empirical method to identify the transition dates between crisis and non-crisis periods endogenously.

  15. Note that the post-GFC/ESDC period exhibits the highest number of observations (67) and thus, should have a greater influence on the results of the entire sample period than the pre-GFC and GFC period, which combine a total of 41 observations.

  16. We observe a violation of the volatility constraint and a recovery through adding CDS indices to the European conservative investor’s benchmark portfolio in every sub-period.

  17. We discuss and explain extreme portfolio (re-) allocations in very detail when analyzing the three sub-periods separately and perform robustness checks in Sect. 4.2.

  18. We additionally perform a robustness check and vary both, the volatility bound and the risk aversion coefficient for both aggressive investors (during the pre-GFC period). We allow for a more conservative investment behavior and increase the risk aversion coefficient from the initial value of 2 to the value of 5 while fixing the volatility bound to a steady 15% p.a. In addition, we decrease the volatility bound to 10% p.a. while holding the risk aversion coefficient constant at the initial value of 2. Overall, allocation structures in portfolios from both aggressive investors do not remarkably differ even when varying these parameters. We provide the results from this robustness check in the Online Appendix (Tables 3a-OA till 3d-OA and Figs. 3a-OA till 3d-OA).

  19. It could be argued that the stock index-CDS index portfolios may be under-diversified since these two asset classes share similar corporate fundamentals. However, descriptive statistics from Table 1 clearly reveal that stock indices and CDS indices are two different asset classes. While stock indices are classified as a risky asset class with a high potential for a portfolio-return enhancement, CDS indices are much more conservative instruments with high risk-reduction attributes. Furthermore, both indices are (well-) diversified assets and thus, only include the average of corporate risk of the underlying reference entities. Moreover, the number of underlying entities is different between stock indices and CDS indices in our study. Finally, as presented and discussed in Sect. 1, a huge strand of empirical studies provide evidence for significant differences between the CDS, corporate bond and stock market.

  20. The ‘flight to quality’ (Chan et al. 2011) and the ‘safe-haven’ characteristics of US (treasury) government bonds (Flavin et al. 2014) are well documented in the literature.

  21. Dufour et al. (2017) suggest several arguments indicating that European sovereign bonds may not be described as ‘safe-haven’ assets during the ESDC period. First, the investment behavior among capital market investors has changed since investors penalize increasing sovereign default risk through a significant disinvestment of capital. Ultimately, especially sovereign bonds from high-risk countries are considered as risky assets (like stocks) by capital markets investors during the ESDC period. Second, sovereign bond indices from high-risk countries are affected by increasing liquidity risk indicating that capital market investors have ‘fled to safety’ under a more severe sovereign default risk. Third, it is shown that sovereign credit rating downgrades result (1) in lower sovereign bond prices due to weakened fiscal conditions and (2) in credit rating downgrades among the corporate sector (lower stock prices) due to the Sovereign Ceiling Channel (Almeida et al. 2017).

  22. We additionally consider transaction costs during two further robustness checks (relaxation of short sale constraints and Black-Litterman asset allocation model). However, since results do not remarkably differ from respective versions without transaction costs, we do not report them in this paper but provide them on request.

  23. See Rockafellar and Uryasev (2000) as well as Scherer (2010) for a more detailed discussion in this context.

  24. Note that the VaR in Eq. 5 is defined as loss and hence, exhibits a positive sign.

  25. We use the average excess return of the MSCI World Index as an independent benchmark for this parameter.

  26. Following Tütüncü et al. (2003), the constraints for the auxiliary variable \(z_s\) and the minimization of the problem ensure that \(z_s\) has exactly the same value as the maximum function \(\max (0,-VaR_{1-\alpha }(X)-\omega 'r_s)\).

  27. Note that the two investor-types only differ with regard to the values of the volatility bound and the minimum desired return \(r_{min}\). As the implementation of a risk aversion coefficient is not reasonable for a mean-CVaR optimization approach.

  28. Note that further asset allocation models, such as strategic weights or minimum-variance, could be implemented as alternative Black-Litterman benchmark portfolios. However, employing strategic weights is challenging for our analysis since reliable data on strategic weights of CDS indices in an investor’s portfolio is not yet available. As regards minimum-variance, we perform Black-Litterman out-of-sample estimations employing this asset allocation model. However and as expected, since each CDS index in our sample exhibits a lower risk exposure than respective North American and European stock and sovereign bond indices, we observe a much stronger risk-diversification effect as compared to the 1/N-Black-Litterman and the mean-variance approach. We provide results on request.

  29. We vary this parameter in an unreported sensitivity analysis. Corresponding to results provided by Bessler et al. (2017), results from the 1/N-Black-Litterman model converge to results from our baseline mean-variance approach for higher values of \(\tau \), and converge to the 1/N benchmark portfolio for very low values of \(\tau \).

  30. The S&P GSCI Light Energy is a sub-index of the regular world production weighted S&P GSCI. It includes the same five main commodity groups (energy, industrial metals, precious metals, agriculture and livestock), but is more diversified than the S&P GSCI since weights are set lower for the energy group, which in turn increases the relative weights of the other four groups. The dollar weights of the GSCI (GSCI Light Energy) in 2014 were 69.8% (39.2%) for energy, 6.7% (13.6%) for industrial metals, 3.2% (6.0%) for precious metals, 5.0% (12.6%) for livestock, and 15.3% (28.7%) for agricultural commodities.

  31. In a prior analysis, we directly compare the stock-bond-commodity index portfolios with stock-bond-CDS index portfolios and find that including a commodity index results in a smaller number of portfolio benefits for all investors. Generally, the GSCI LE more strongly enhances portfolio returns and performance as compared to the CDS indices. This is true for aggressive investors and the crisis period. However, we still observe a portfolio (downside) risk-reduction potential only for CDS-portfolios. Moreover, while both aggressive investors usually more strongly invest in the (more risky) commodity index, CDS indices exhibit higher weights in portfolios from conservative investors (see Tables 3, 9a-OA).

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Authors and Affiliations

  1. Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany

    Benjamin Hippert, André Uhde & Sascha Tobias Wengerek

Authors
  1. Benjamin Hippert
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  2. André Uhde
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  3. Sascha Tobias Wengerek
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Correspondence to André Uhde.

Additional information

We are grateful to an anonymous referee, Gurdip Bakshi (the editor), Wolfgang Bessler and Edith X. Liu as well as conference participants of the Hypovereinsbank Ph.D. Workshop 2015 in Kiel, the Paderborn University Faculty Research Workshop 2015 in Bad Arolsen, the International Rome Conference on Money, Banking and Finance 2016, the 2016 Paris Financial Management Conference and the annual meeting of the Midwest Finance Association 2017 in Chicago. Finally, we thank Helena Becker, Franziska Beckmann, Sarah Herwald, Marco Kerkemeier, Marcel Lengacher and Carina Uhde for outstanding research assistance.

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Hippert, B., Uhde, A. & Wengerek, S.T. Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe. Rev Deriv Res 22, 203–259 (2019). https://doi.org/10.1007/s11147-018-9148-8

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