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Ambiguity in defaultable term structure models

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  • Tolulope Fadina
  • Thorsten Schmidt

Abstract

We introduce the concept of no-arbitrage in a credit risk market under ambiguity considering an intensity-based framework. We assume the default intensity is not exactly known but lies between an upper and lower bound. By means of the Girsanov theorem, we start from the reference measure where the intensity is equal to $1$ and construct the set of equivalent martingale measures. From this viewpoint, the credit risky case turns out to be similar to the case of drift uncertainty in the $G$-expectation framework. Finally, we derive the interval of no-arbitrage prices for general bond prices in a Markovian setting.

Suggested Citation

  • Tolulope Fadina & Thorsten Schmidt, 2018. "Ambiguity in defaultable term structure models," Papers 1801.10498, arXiv.org, revised Apr 2018.
  • Handle: RePEc:arx:papers:1801.10498
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    References listed on IDEAS

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    1. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
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    4. Chunsheng Zhou, 1997. "A jump-diffusion approach to modeling credit risk and valuing defaultable securities," Finance and Economics Discussion Series 1997-15, Board of Governors of the Federal Reserve System (U.S.).
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    6. Frank Riedel, 2015. "Financial economics without probabilistic prior assumptions," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 38(1), pages 75-91, April.
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    8. Neufeld, Ariel & Nutz, Marcel, 2014. "Measurability of semimartingale characteristics with respect to the probability law," Stochastic Processes and their Applications, Elsevier, vol. 124(11), pages 3819-3845.
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    Cited by:

    1. Maxim Bichuch & Agostino Capponi & Stephan Sturm, 2020. "Robust XVA," Mathematical Finance, Wiley Blackwell, vol. 30(3), pages 738-781, July.
    2. Maxim Bichuch & Agostino Capponi & Stephan Sturm, 2018. "Robust XVA," Papers 1808.04908, arXiv.org, revised Feb 2020.

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