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Jump-diffusion term structure and Ito conditional moment generator

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  • Hao Zhou

Abstract

This paper implements a Multivariate Weighted Nonlinear Least Square estimator for a class of jump-diffusion interest rate processes (hereafter MWNLS-JD), which also admit closed-form solutions to bond prices under a no-arbitrage argument. The instantaneous interest rate is modeled as a mixture of a square-root diffusion process and a Poisson jump process. One can derive analytically the first four conditional moments, which form the basis of the MWNLS-JD estimator. A diagnostic conditional moment test can also be constructed from the fitted moment conditions. The market prices of diffusion and jump risks are calibrated by minimizing the pricing errors between a model-implied yield curve and a target yield curve. The time series estimation of the short-term interest rate suggests that the jump augmentation is highly significant and that the pure diffusion process is strongly rejected. The cross-sectional evidence indicates that the jump-diffusion yield curves are both more flexible in reducing pricing errors and are more consistent with the Martingale pricing principle.

Suggested Citation

  • Hao Zhou, 2001. "Jump-diffusion term structure and Ito conditional moment generator," Finance and Economics Discussion Series 2001-28, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2001-28
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    References listed on IDEAS

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    Cited by:

    1. Qiang Dai & Kenneth Singleton, 2003. "Term Structure Dynamics in Theory and Reality," Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 631-678, July.
    2. Høg, Esben & Frederiksen, Per & Schiemert, Daniel, 2008. "On the Generalized Brownian Motion and its Applications in Finance," Finance Research Group Working Papers F-2008-07, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    3. Esben Hoeg & Per Frederiksen, 2006. "The Fractional OU Process: Term Structure Theory and Application," Computing in Economics and Finance 2006 194, Society for Computational Economics.

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