Modeling Credit Spreads Using Nonlinear Regression
AbstractThe term structure of credit spreads is studied with an aim to predict its future movements. A completely new approach to tackle this problem is presented, which utilizes nonlinear parametric models. The Brain-Cousens regression model with five parameters is chosen to describe the term structure of credit spreads. Further, we investigate the dependence of the parameter changes over time and the determinants of credit spreads.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1401.6955.
Date of creation: Jan 2014
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Publication status: Published in Proceedings of IWSM 2013, Volume 2: 697-700. 2013
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Web page: http://arxiv.org/
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- Jarrow, Robert & Ruppert, David & Yu, Yan, 2004. "Estimating the Interest Rate Term Structure of Corporate Debt With a Semiparametric Penalized Spline Model," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 57-66, January.
- Herwig Friedl & Radoslava Mirkov & Ansgar Steinkamp, 2012. "Modelling and Forecasting Gas Flow on Exits of Gas Transmission Networks," International Statistical Review, International Statistical Institute, vol. 80(1), pages 24-39, 04.
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