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CDO Surfaces Dynamics

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Author Info

  • Barbara Choroś-Tomczyk
  • Wolfgang Karl Härdle
  • Ostap Okhrin

Abstract

Modelling the dynamics of credit derivatives is a challenging task in finance and economics. The recent crisis has shown that the standard market models fail to measure and forecast financial risks and their characteristics. This work studies risk of collateralized debt obligations (CDOs) by investigating the evolution of tranche spread surfaces and base correlation surfaces using a dynamic semiparametric factor model (DSFM). The DSFM offers a combination of flexible functional data analysis and dimension reduction methods, where the change in time is linear but the shape is nonparametric. The study provides an empirical analysis based on iTraxx Europe tranches and proposes an application to curve trading strategies. The DSFM allows us to describe the dynamics of all the tranches for all available maturities and series simultaneously which yields better understanding of the risk associated with trading CDOs and other structured products.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2013-032.

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Length: 33 pages
Date of creation: Jul 2013
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2013-032

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Keywords: base correlation; collateralized debt obligation; curve trade; dynamic factor model; semiparametric model;

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  1. Park, Byeong U. & Mammen, Enno & Härdle, Wolfgang & Borak, Szymon, 2009. "Time Series Modelling With Semiparametric Factor Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 284-298.
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  3. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2012. "Dynamic factor models with macro, frailty and industry effects for US default counts: the credit crisis of 2008," Working Paper Series 1459, European Central Bank.
  4. Enzo Giacomini & Wolfgang Härdle & Volker Krätschmer, 2009. "Dynamic semiparametric factor models in risk neutral density estimation," AStA Advances in Statistical Analysis, Springer, vol. 93(4), pages 387-402, December.
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  6. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
  7. Matthias R. Fengler & Wolfgang K. H�rdle & Enno Mammen, 0. "A semiparametric factor model for implied volatility surface dynamics," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(2), pages 189-218.
  8. Xueli Liu & Hans-Georg Muller, 2004. "Functional Convex Averaging and Synchronization for Time-Warped Random Curves," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 687-699, January.
  9. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
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