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Nonparametric estimation of multifactor continuous time interest rate models

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  • Chris Downing

Abstract

This paper studies the finite sample properties of the kernel regression method of Boudoukh et al. (1998) for estimating multifactor continuous-time term structure models. Monte Carlo simulations are employed, with a grid-search technique to find the optimal kernel bandwidth. The estimator exhibits truncation and correlated residuals biases near the boundaries of the data. However, the variance of the estimator is so high that the biases are unlikely to be relevant from a hypothesis testing point of view. The performance of the estimator is also studied under model misspecification. Irrelevant regressors reduce efficiency and induce additional biases in the estimates. Using Treasury bill data, I test whether the estimates produced by the nonparametric estimator are statistically distinguishable from estimates obtained under a parametric model. The kernel regressions pick up nonlinearities in the data that the parametric model cannot capture.

Suggested Citation

  • Chris Downing, 1999. "Nonparametric estimation of multifactor continuous time interest rate models," Finance and Economics Discussion Series 1999-62, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:1999-62
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    Citations

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    Cited by:

    1. Antonio Mele, 2003. "Fundamental Properties of Bond Prices in Models of the Short-Term Rate," Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 679-716, July.
    2. Teresa Corzo Santamaría & Javier Gómez Biscarri, 2005. "Nonparametric estimation of convergence of interest rates: Effects on bond pricing," Spanish Economic Review, Springer;Spanish Economic Association, vol. 7(3), pages 167-190, September.
    3. Ang, Andrew & Bekaert, Geert, 2002. "Short rate nonlinearities and regime switches," Journal of Economic Dynamics and Control, Elsevier, vol. 26(7-8), pages 1243-1274, July.

    More about this item

    Keywords

    Interest rates; Econometric models; time series analysis;
    All these keywords.

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