Advertisement

Review of Derivatives Research

, Volume 21, Issue 1, pp 63–118 | Cite as

The determinants of CDS spreads: evidence from the model space

  • Matthias Pelster
  • Johannes Vilsmeier
Article

Abstract

We apply Bayesian model averaging and a frequentistic model space analysis to assess the pricing determinants of credit default swaps (CDSs). Our study focuses on the complete model space of plausible models and thus supports ultimate robustness. Using a large dataset of CDS contracts we find that CDS price dynamics can be mainly explained by factors describing firms’ sensitivity to extreme market movements. More precisely, our results suggest that dynamic copula based measures of tail dependence incorporate most essential pricing information, making other potential determinants such as Merton-type factors or linear variables measuring the systematic market evolution negligible.

Keywords

CDS Bayesian model averaging Crash aversion Tail risk Tail dependence Time-varying copulas 

JEL Classification

G12 C11 G01 

Supplementary material

References

  1. Ait-Sahalia, Y., & Lo, A. W. (2000). Nonparametric risk management and implied risk aversion. Journal of Econometrics, 94, 9–51.CrossRefGoogle Scholar
  2. Alexander, C., & Kaeck, A. (2008). Regime dependent determinants of credit default swap spreads. Journal of Banking & Finance, 32, 1008–1021.CrossRefGoogle Scholar
  3. Andersen, T. G., Bollerslev, T., Christoffersen, P. F., & Diebold, F. X. (2006). Handbook of economic forecasting. In G. Elliott, C. W. J. Granger, & A. Timmermann (Eds.), Volatility and correlation forecasting (pp. 778–878). Amsterdam: Elsevier.Google Scholar
  4. Arakelyan, A., Rubio, G., & Serrano, P. (2015). The reward for trading illiquid maturities in credit default swap markets. International Review of Economics and Finance, 39, 376–389.CrossRefGoogle Scholar
  5. Augustin, P., Subrahmanyam, M. G., Tang, D. Y., & Wang, S. Q. (2014). Credit default swaps—A survey. Foundations and Trends in Finance, 9, 1–196.CrossRefGoogle Scholar
  6. Augustin, P., & Tédongap, R. (2011). Common factors and commonality in sovereign CDS spreads: A consumption-based explanation. Working paper.Google Scholar
  7. Avramov, D. (2002). Stock return predictability and model uncertainty. Journal of Financial Economics, 64, 423–458.CrossRefGoogle Scholar
  8. Baele, L., De Bruyckere, V., De Jonghe, O., & Vander Vennet, R. (2015). Model uncertainty and systematic risk in US banking. Journal of Banking & Finance, 53, 49–66.CrossRefGoogle Scholar
  9. Bauwens, L., Laurent, S., & Rombouts, J. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics, 21, 79–109.CrossRefGoogle Scholar
  10. Benkert, C. (2004). Explaining credit default swap premia. The Journal of Futures Markets, 24, 71–92.CrossRefGoogle Scholar
  11. Berg, D. (2009). Copula goodness-of-fit testing: An overview and power comparison. European Journal of Finance, 15, 675–701.CrossRefGoogle Scholar
  12. Berndt, A., & Obreja, I. (2010). Decomposing European CDS returns. Review of Finance, 14, 189–233.CrossRefGoogle Scholar
  13. Bollerslev, T., & Todorov, V. (2011). Tails, fears, and risk premia. The Journal of Finance, 66, 2165–2211.CrossRefGoogle Scholar
  14. Bollerslev, T., Todorov, V., & Xu, L. (2015). Tail risk premia and return predictability. Journal of Financial Economics, 118, 113–134.CrossRefGoogle Scholar
  15. Bongaerts, D., de Jong, F., & Driessen, J. (2011). Derivate pricing with liquidity risk: Theory and evidence from the credit default swap market. The Journal of Finance, 66, 203–240.CrossRefGoogle Scholar
  16. Breusch, T. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17, 334–355.CrossRefGoogle Scholar
  17. Bujack, K. M., & Santamaria, M. T. C. (2016). Credit default swaps and financial risks in the 21st century. Working paper.Google Scholar
  18. Buocher, C., Daníelsson, J., Kouontchoub, P., & Mailleta, B. (2014). Risk models-at-risk. Journal of Banking & Finance, 44, 72–92.CrossRefGoogle Scholar
  19. Burnham, K . P., & Anderson, D . R. (2002). Model selection and multimodel inference: A practical information-theoretic approach. Berlin: Springer.Google Scholar
  20. Chabi-Yo, F., Ruenzi, S., & Weigert, F. (2014). Crash sensitivity and the cross-section of expected stock returns. Working paper.Google Scholar
  21. Chen, X., & Fan, Y. (2006). Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification. Journal of Econometrics, 135, 307–335.CrossRefGoogle Scholar
  22. Chen, X., Fan, Y., & Tsyrennikov, V. (2006). Efficient estimation of semiparametric multivariate copula models. Journal of the American Statistical Association, 101, 1228–1240.CrossRefGoogle Scholar
  23. Christoffersen, P., Errunza, V., Jacobs, K., & Langlois, H. (2012). Is the potential for international diversification disappearing? A dynamic copula approach. The Review of Financial Studies, 25, 3711–3751.CrossRefGoogle Scholar
  24. Christoffersen, P., Jacobs, K., Jin, X., & Langlois, H. (2014). Dynamic dependence and diversification in corporate credit. Working paper.Google Scholar
  25. Clark, T., & McCracken, M. (2001). Tests for equal forecast accuracy and ecompassing for nested models. Journal of Econometrics, 105, 85–110.CrossRefGoogle Scholar
  26. Clayton, D. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65, 141–151.CrossRefGoogle Scholar
  27. Collin-Dufresne, P., Goldstein, R. S., & Martin, J. S. (2001). The determinants of credit spread changes. The Journal of Finance, 61, 2177–2207.CrossRefGoogle Scholar
  28. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1, 223–236.CrossRefGoogle Scholar
  29. Cont, R., & Kan, Y. H. (2011). Statistical modeling of credit default swap portfolios. SSRN working paper.Google Scholar
  30. Coval, J. D., Jurek, J. W., & Stafford, E. (2009). Economic catastrophe bonds. American Economic Review, 99, 628–666.CrossRefGoogle Scholar
  31. Creal, D., Koopman, S. J., & Lucas, A. (2013). General autoregressive score models with applications. Journal of Applied Econometrics, 28, 777–795.CrossRefGoogle Scholar
  32. Cremers, K. M. (2002). Stock return predictability: A Bayesian model selection perspective. Review of Financial Studies, 15, 1223–1249.CrossRefGoogle Scholar
  33. Daníelsson, J. (2008). Blame the models. Journal of Financial Stability, 4, 321–328.CrossRefGoogle Scholar
  34. Demarta, S., & McNeil, A. J. (2005). The t copula and related copulas. International Statistical Review, 73, 111–129.CrossRefGoogle Scholar
  35. Derman, E. (1996). Model risk. Goldman Sachs Quantitative Strategies Research Notes.Google Scholar
  36. Diebold, F., Hahn, J., & Tay, A. (1999). Multivariate density forecast evaluation and calibration in financial risk management: High frequency returns on foreign exchange. Review of Economics and Statistics, 81, 661–673.CrossRefGoogle Scholar
  37. Diebold, F., & Mariano, R. (1995). Comparing predicitve accuracy. Journal of Business and Economic Statistics, 13, 253–263.Google Scholar
  38. Elliott, G., Gargano, A., & Timmermann, A. (2013). Complete subset regressions. Journal of Econometrics, 177, 357–373.CrossRefGoogle Scholar
  39. Ericsson, J., Jacobs, K., & Oviedo, R. (2009). The determinants of credit default swap premia. Journal of Financial and Quantitative Analysis, 44, 109–132.CrossRefGoogle Scholar
  40. Fernandez, C., Ley, E., & Steel, M. F. (2001). Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100, 381–427.CrossRefGoogle Scholar
  41. Frahm, G., Junker, M., & Schmidt, R. (2005). Estimating the tail-dependence coefficient: Properties and pitfalls. Insurance: Mathematics and Economics, 37, 80–100.Google Scholar
  42. Furnival, G., & Wilson, R. (1974). Regression by leaps and bounds. Technometrics, 16, 499–511.CrossRefGoogle Scholar
  43. Gârleanu, N., Pedersen, L. H., & Poteshman, A. M. (2009). Demand-based option pricing. Review of Financial Studies, 22, 4259–4299.CrossRefGoogle Scholar
  44. Garratt, A., Lee, K., Pesaran, M. H., & Shin, Y. (2003). Forecast uncertainties in macroeconomic modeling. Journal of the American Statistical Association, 98, 829–838.CrossRefGoogle Scholar
  45. Genest, C., Gendron, M., & Bourdeau-Brien, M. (2009). The advent of copulas in finance. The European Journal of Finance, 15, 609–618.CrossRefGoogle Scholar
  46. Genest, C., Ghoudi, K., & Rivest, L.-P. (1995). A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika, 82, 543–552.CrossRefGoogle Scholar
  47. Genest, C., & Rivest, L.-P. (1993). Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88, 1034–1043.CrossRefGoogle Scholar
  48. Godfrey, L. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46, 1293–1302.CrossRefGoogle Scholar
  49. Gourio, F. (2011). Credit risk and disaster risk. NBER working paper 17026.Google Scholar
  50. Green, T. C., & Figlewski, S. (1999). Market risk and model risk for a financial institution writing options. The Journal of Finance, 54, 1465–1499.CrossRefGoogle Scholar
  51. Greene, W . H. (2003). Econometric analysis (5th ed.). Upper Saddle River: Prentice Hall.Google Scholar
  52. Gumbel, E. (1960). Distributions des valeurs extrémes en plusiers dimensions. Publications de l’Institut de Statistique de l’Université de Paris, 9, 171–173.Google Scholar
  53. Han, N., & Zhou, Y. (2015). Understanding the term structure of credit default swap spreads. Journal of Empirical Finance, 31, 18–35.CrossRefGoogle Scholar
  54. Han, Y., Gong, P., & Zhou, X. (2015). Correlations and risk contagion between mixed assets and mixed-asset portfolio VaR measurements in a dynamic view: An application based on time varying copula models. Physica A, 444, 940–953.CrossRefGoogle Scholar
  55. Hansen, B. E. (2007). Least squares model averaging. Econometrica, 75, 1175–1189.CrossRefGoogle Scholar
  56. Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79, 453–497.CrossRefGoogle Scholar
  57. Hasan, I., Horvath, R., & Mares, J. (2016). What type of finance matters for growth? Bayesian model averaging evidence. World bank policy research working paper, 7645.Google Scholar
  58. Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning (2nd ed.). Heidelberg: Springer.Google Scholar
  59. Heinz, F. F., & Sun, Y. (2014). Sovereign CDS spreads in Europe—The role of global risk aversion, economic fundamentals, liquidity, and spillovers. IMF working paper.Google Scholar
  60. Hoerl, A., & Kennard, R. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67.CrossRefGoogle Scholar
  61. Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–417.CrossRefGoogle Scholar
  62. Hull, J., & Suo, W. (2002). A methodology for assessing model risk and its application to the implied volatility function model. The Journal of Financial and Quantitative Analysis, 37, 297–318.CrossRefGoogle Scholar
  63. Jackwerth, J. C., & Rubinstein, M. (1996). Recovering probability distributions from option prices. The Journal of Finance, 51, 1611–1631.CrossRefGoogle Scholar
  64. Joe, H. (1997). Multivariate models and dependence concepts. London: Chapman & Hall.CrossRefGoogle Scholar
  65. Joe, H. (2015). Dependence modelling with copulas. Boca Raton: CRC Press.Google Scholar
  66. Jondeau, E., & Rockinger, M. (2006). The Copula-GARCH model of conditional dependencies: An international stock market application. Journal of International Money and Finance, 25, 827–853.CrossRefGoogle Scholar
  67. Jorion, P., & Zhang, G. (2007). Good and bad credit contagion: Evidence from credit default swaps. Journal of Financial Economics, 84, 860–883.CrossRefGoogle Scholar
  68. Kapetanios, G., Labhard, V., & Price, S. (2008). Forecasting using bayesian and information theoretic model averaging: An application to UK inflation. Journal of Business and Economic Statistics, 26, 33–41.CrossRefGoogle Scholar
  69. Kass, R., & Raftery, A. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.CrossRefGoogle Scholar
  70. Keiler, S., & Eder, A. (2013). CDS spreads and systemic risk—A spatial econometric approach. Discussion paper Deutsche Bundesbank.Google Scholar
  71. Kita, A. (2015). Predicting credit default swap spreads: The role of credit spread volatility. Working paper.Google Scholar
  72. Kole, E., Koedijk, K., & Verbeek, M. (2006). Selecting copulas for risk management. Working paper.Google Scholar
  73. Koziol, C., Koziol, P., & Schön, T. (2015). Do correlated defaults matter for CDS premia? An empirical analysis. Review of Derivatives Research, 18, 191–224.CrossRefGoogle Scholar
  74. Kullback, S., & Leibler, R. (1951). Oeconometrics and sufficency. Annals of Mathematical Statistics, 22, 79–86.CrossRefGoogle Scholar
  75. Kumar, A., & Lee, C. M. (2006). Retail investor sentiment and return comovements. The Journal of Finance, 61, 2451–2486.CrossRefGoogle Scholar
  76. Laeven, L., & Valencia, F. (2012). Systemic banking crises database: An update. IMF working paper.Google Scholar
  77. Li, D. X. (2000). On default correlation: A copula function approach. The RiskMetrics Group working paper, 99-07.Google Scholar
  78. Longstaff, F. A., Mithal, S., & Neis, E. (2005). Corporate yield spreads: Default risk or liquidity? New evidence from the credit default swap market. The Journal of Finance, 60, 2213–2253.CrossRefGoogle Scholar
  79. Madigan, D., & Raftery, A. (1994). Model selection and accounting for model uncertainty in graphical models using occam’s window. Journal of the American Statistical Association, 89, 1535–1546.CrossRefGoogle Scholar
  80. Madigan, D., & York, J. (1995). Bayesian graphical models for discrete data. International Statistical Review, 63, 215–232.CrossRefGoogle Scholar
  81. Meine, C., Supper, H., & Weiß, G. N. (2015). Do CDS spreads move with commonality in liquidity? Review of Derivatives Research, 18, 225–261.CrossRefGoogle Scholar
  82. Meine, C., Supper, H., & Weiß, G. N. (2016). Is tail risk priced in credit default swap premia? Review of Finance, 20, 287–336.CrossRefGoogle Scholar
  83. Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29, 449–479.Google Scholar
  84. Moral-Benito, E. (2012). Determinants of economic growth: A Bayesian panel data approach. Review of Economics and Statistics, 94, 566–579.CrossRefGoogle Scholar
  85. Moral-Benito, E. (2015). Model averaging in economics: An overview. Journal of Economic Surveys, 29, 46–75.CrossRefGoogle Scholar
  86. Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49, 1417–1426.CrossRefGoogle Scholar
  87. Oh, D. H., & Patton, A. J. (2013). Time-varying systemic risk: Evidence from a dynamic copula model of CDS spreads. Economic Research Initiatives at Duke (ERID) working paper.Google Scholar
  88. Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International Economic Review, 47, 527–556.CrossRefGoogle Scholar
  89. Patton, A. J. (2009). Handbook of financial time series. In T. G. Andersen, R. A. Davis, J.-P. Kreiss, & T. V. Mikosch (Eds.), Copula-based models for financial time series. Berlin: Springer.CrossRefGoogle Scholar
  90. Qiu, J., & Yu, F. (2012). Endogenous liquidity in credit derivatives. Journal of Financial Economics, 103, 611–631.CrossRefGoogle Scholar
  91. Raftery, A. E., Madigan, D., & Hoeting, J. A. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 92, 179–191.CrossRefGoogle Scholar
  92. Rémillard, B. (2010). Goodness-of-fit tests for copulas of multivariate time series. Working paper.Google Scholar
  93. Rosenblatt, M. (1952). Remarks on a multivariate transformation. The Annals of Mathematical Statistics, 23, 470–472.CrossRefGoogle Scholar
  94. Rubinstein, M. (1994). Implied binomial trees. The Journal of Finance, 49, 771–818.CrossRefGoogle Scholar
  95. Sala-I-Martin, X., Doppelhofer, G., & Miller, R. I. (2004). Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach. The American Economic Review, 94, 813–835.CrossRefGoogle Scholar
  96. Samuels, J. D., & Sekkel, R. M. (2011). Forecasting with large datasets: Trimming predictors and forecast combination. Technical report, working paper.Google Scholar
  97. Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut Statistique de l’Université de Paris, 8, 229–231.Google Scholar
  98. Tang, D. Y., & Yan, H. (2008). Liquidity and credit default swap spreads. SSRN working paper.Google Scholar
  99. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B, 58(1), 267–288.Google Scholar
  100. Volinsky, C., Madigan, D., Raftery, E., & Kronmal, R. (1997). Bayesian model averaging in proportional hazard models: Assessing the risk of a stroke. Applied Statistics, 46, 433–448.Google Scholar
  101. Weiß, G. N., & Scheffer, M. (2015). Mixture pair-copula-constructions. Journal of Banking & Finance, 54, 175–191.CrossRefGoogle Scholar
  102. Wooldridge, J. (2003). Cluster-sample methods in applied econometrics. American Economic Review, 93, 133–138.CrossRefGoogle Scholar
  103. Wright, J. H. (2008). Bayesian model averaging and exchange rate forecasts. Journal of Econometrics, 146, 329–341.CrossRefGoogle Scholar
  104. Wright, J. H. (2009). Forecasting US inflation by Bayesian model averaging. Journal of Forecasting, 28, 131–144.CrossRefGoogle Scholar
  105. Zhang, B. Y., Zhou, H., & Zhu, H. (2009). Explaining credit default swap spreads with the equity volatility and jump risks of individual firms. The Review of Financial Studies, 22, 5099–5131.CrossRefGoogle Scholar
  106. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elatic net. Journal of the Royal Statistical Society, 67, 301–320.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Leuphana University LueneburgLüneburgGermany
  2. 2.Deutsche BundesbankFrankfurt am MainGermany

Personalised recommendations