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Expectations of functions of stochastic time with application to credit risk modeling

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Abstract

We develop two novel approaches to solving for the Laplace transform of a time-changed stochastic process. We discard the standard assumption that the background process (Xt) is Levy. Maintaining the assumption that the business clock (Tt) and the background process are independent, we develop two different series solutions for the Laplace transform of the time-changed process X-tildet=X(Tt). In fact, our methods apply not only to Laplace transforms, but more generically to expectations of smooth functions of random time. We apply the methods to introduce stochastic time change to the standard class of default intensity models of credit risk, and show that stochastic time-change has a very large effect on the pricing of deep out-of-the-money options on credit default swaps.

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  • Ovidiu Costin & Michael B. Gordy & Min Huang & Pawel J. Szerszen, 2013. "Expectations of functions of stochastic time with application to credit risk modeling," Finance and Economics Discussion Series 2013-14, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2013-14
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    References listed on IDEAS

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    1. Hui Chen & Scott Joslin, 2012. "Generalized Transform Analysis of Affine Processes and Applications in Finance," Review of Financial Studies, Society for Financial Studies, vol. 25(7), pages 2225-2256.
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    Cited by:

    1. Tianyao Chen & Xue Cheng & Jingping Yang, 2019. "Common Decomposition of Correlated Brownian Motions and its Financial Applications," Papers 1907.03295, arXiv.org, revised Nov 2020.

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