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La curva de rendimientos: una revisión metodológica y nuevas aproximaciones de estimación

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  • Juan Camilo Santana
Abstract
La curva de rendimientos es una herramienta utilizada ampliamente, por quienes toman las decisiones de política monetaria o planifican sus inversiones, de acuerdo con la valoración, negociación o cobertura sobre instrumentos financieros. Debido a su importancia, el interés del artículo es evaluar el esempeno de un conjunto de modelos econométricos en el ajuste de la estructura a plazos de las tasas de interés (en el escenario del mercado de deuda pública en Colombia y en Estados Unidos), y en las distintas formas que pueden tomar las curvas de rendimientos. Los resultados revelan las bondades en el ajuste de las redes neuronales artificiales (RNA), la curva de Svensson, la curva de Nelson-Siegel y los polinomios locales. No obstante, se recomienda utilizar la curva de Svensson en la estimación de las tasas de interés, debido a la interpretabilidad de sus parámetros y a su superioridad sobre la Curva de Nelson-Siegel.

Suggested Citation

  • Juan Camilo Santana, 2008. "La curva de rendimientos: una revisión metodológica y nuevas aproximaciones de estimación," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, July.
    • Handle: RePEc:col:000093:004838
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    References listed on IDEAS

    as
    1. Alejandro Revéiz Hérault & Juan Manuel Julio & Silvia Juliana Mera, 2002. "La curva Spot (Cero Cupón), Estimación con splines cúbicos suavizados, usos y ejemplos," Lecturas en Finanzas 2961, Banco de la República.
    2. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    3. Ramesh Sharda, 1994. "Neural Networks for the MS/OR Analyst: An Application Bibliography," Interfaces, INFORMS, vol. 24(2), pages 116-130, April.
    4. Luis Eduardo Arango & Luis Fernando Melo & Diego Mauricio Vásquez, 2003. "Estimación de la estructura a plazo de las tasas de interés en Colombia," Coyuntura Económica, Fedesarrollo, vol. 33(1), pages 51-76, March.
    5. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    6. McCulloch, J Huston, 1971. "Measuring the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 44(1), pages 19-31, January.
    7. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    8. Ben Hunt, 1995. "Modelling the Yields on Australian Coupon Paying Bonds," Working Paper Series 50, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    9. Ben Hunt & Chris Terry, 1998. "Zero-Coupon Yield Curve Estimation: A Principal Component, Polynomial Approach," Working Paper Series 81, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    10. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    curva de rendimientos; Nelson-Siegel; Svensson; regresión Kernel; splines suavizados; polinomios locales; supersuavizador de Friedmann; polinomios trigonométricos; redes neuronales.;
    All these keywords.

    JEL classification:

    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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