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Nonparametric Estimation of Multifactor Continuous Time Interest-Rate Models

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  • Christopher T. Downing

    (Board of Governors, Federal Reserve)

Abstract

In this paper we study the finite sample properties of the nonparametric method developed by Stanton and later extended by Boudoukh, et al. for the estimation of the drifts and diffusions of multifactor continuous-time term-structure models. Monte Carlo simulations from a known parametric model are employed to calculate the performance of the estimator. The paper focuses on the issue of optimal bandwidth selection. The results suggest that, for persistent data-generating processes exhibiting stochastic volatility, such as interest rate data, a bandwidth function that varies over the surface of the data is optimal. The paper also presents a computationally intensive bandwidth-selection procedure that uses dynamic graphics, combining the computational power of the machine with the pattern-recognition abilities of the human brain. The Monte Carlo simulations require the numeric solution of a system of stochastic differential equations. The paper also presents a nonparametric test for the validity of the solutions. This test is useful in other estimation algorithms, such as the efficient method of moments, where numeric solutions of stochastic differential equations are required. The test is also useful as a tool for understanding how the length of the time step used in the numeric solution of the stochastic differential solutions affects the accuracy of the solution.

Suggested Citation

  • Christopher T. Downing, 1999. "Nonparametric Estimation of Multifactor Continuous Time Interest-Rate Models," Computing in Economics and Finance 1999 111, Society for Computational Economics.
    • Handle: RePEc:sce:scecf9:111
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    References listed on IDEAS

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    1. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(4), pages 657-681, October.
    2. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    3. Andersen, Torben G. & Lund, Jesper, 1997. "Estimating continuous-time stochastic volatility models of the short-term interest rate," Journal of Econometrics, Elsevier, vol. 77(2), pages 343-377, April.
    4. Pritsker, Matt, 1998. "Nonparametric Density Estimation and Tests of Continuous Time Interest Rate Models," The Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 449-487.
    5. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    6. Ait-Sahalia, Yacine, 1996. "Testing Continuous-Time Models of the Spot Interest Rate," The Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 385-426.
    7. David A. Chapman & Neil D. Pearson, 1998. "Is the Short Rate Drift Actually Nonlinear?," Finance 9808005, University Library of Munich, Germany.
    8. repec:cup:etheor:v:12:y:1996:i:4:p:657-81 is not listed on IDEAS
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    Cited by:

    1. Antonio Mele, 2003. "Fundamental Properties of Bond Prices in Models of the Short-Term Rate," The 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.

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