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Calibración de parámetros de los modelos de tasas de interés NS y NSS para Colombia: una nota técnica

Author

Listed:
  • Velásquez, Mateo

    (Facultad de Ingeniería Matemática, Universidad EAFIT, Medellín, Colombia)

  • Gutiérrez , Juan

    (Departamento de Finanzas, Universidad EAFIT, Medellín, Colombia)

  • Almonacid , Paula

    (Departamento de Finanzas, Universidad EAFIT, Medellín, Colombia)

Abstract

La calibración del modelo Nelson-Siegel para ajustarse a curvas de rendimientos soberanas se ha encontrado problemática, pues presenta correlación entre sus factores y genera funciones objetivas con múltiples óptimos locales. Estos problemas no han sido profundizados en el contexto colombiano, dándole poca importancia al proceso de calibración. En el presente trabajo se evalúan dos métodos diferentes del gradiente para resolver el problema de ajuste por mínimos cuadrados no lineales: el metaheurístico de evolución diferencial y el método de búsquedas incrementales sobre los parámetros no lineales. Se realizan comparaciones entre los resultados en términos del ajuste logrado, consistencia en los resultados (para el metaheurístico) y formas obtenidas para la curva. El mismo procedimiento es realizado para el modelo Nelson-Siegel-Svensson, evaluando la pertinencia de su uso dentro del mercado local.

Suggested Citation

  • Velásquez, Mateo & Gutiérrez , Juan & Almonacid , Paula, 2016. "Calibración de parámetros de los modelos de tasas de interés NS y NSS para Colombia: una nota técnica," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 73-80.
  • Handle: RePEc:ris:joefas:0102
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    Keywords

    Búsqueda incremental; Calibración; Curva de rendimientos; soberanos; Evolución diferencial; Metaheurístico; Nelson-Siegel-Svensson;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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