Review of Derivatives Research

, Volume 17, Issue 1, pp 39–78 | Cite as

Does modeling framework matter? A comparative study of structural and reduced-form models

  • Yalin Gündüz
  • Marliese Uhrig-Homburg


This study provides a rigorous empirical comparison of structural and reduced-form credit risk frameworks. The literature differentiates between structural models that are based on modeling of the evolution of the balance sheet of the issuer, and reduced-form models that specify credit risk exogenously by a hazard rate process. Until now, there has been no common agreement in academia and practice on which model framework better captures credit risk. As major difference we focus on the discriminative modeling of the default time. In contrast to the previous literature, we calibrate both approaches to the same data set, apply comparable estimation techniques, and assess the out-of-sample prediction quality on the same time series of credit default swap prices. As our empirical implementations of both approaches rely on the same market information we are able to judge whether empirically the model structure itself makes an important difference. Interestingly, our study shows that the models’ prediction power are quite close on average indicating that for pricing purposes the modeling type does not greatly matter compared to the input data used. Still, the reduced-form approach outperforms the structural for investment-grade names and longer maturities. In contrast the structural approach performs better for shorter maturities and sub-investment grade names.


Credit risk Structural models Reduced-form models  Default intensity Stationary leverage Credit default swaps 

JEL Classification




We thank Michael Brennan, Wolfgang Bühler, Jean Helwege, David Lando, Markus Konz, Natalie Packham, Christoph Memmel, participants of the International conference on price, liquidity, and credit risks 2008 Konstanz, the Campus for Finance 2009 Vallendar and seminar participants at Aarhus School of Business for helpful comments and suggestions. Financial support by the Deutsche Forschungsgemeinschaft (DFG) through the Graduate School IME Information Management and Market Engineering is gratefully acknowledged. The views expressed herein are our own and do not necessarily reflect those of the Bundesbank.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Deutsche BundesbankFrankfurtGermany
  2. 2.Institute of Finance, Banking, and InsuranceKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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