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Medidas de riesgo financiero usando cópulas: teoría y aplicaciones

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  • Becerra, Oscar
  • Melo-Velandia, Luis Fernando

Abstract

Este documento realiza una descripción de las medidas de dependencia con sus principales ventajas y desventajas y presenta a la cópula como una estructura flexible que permite caracterizar diferentes tipos de dependencia. Adicionalmente, introduce el uso de la cópula en la medición de riesgo financiero, tomando como ejemplo un portafolio compuesto por tres activos representativos del mercado colombiano. Las pruebas de desempeño o de backtesting del valor en riesgo calculado por diferentes metodologías en los años 2006 y 2007 muestran que las mejores son aquellas que modelan la dependencia en media y varianza, tales como modelos VAR-GARCH-Cópula(t) y VAR-GARCH-Cópula(normal). Las técnicas con el peor desempeño son Riskmetrics y la basada en el supuesto de normalidad.

Suggested Citation

  • Becerra, Oscar & Melo-Velandia, Luis Fernando, 2019. "Medidas de riesgo financiero usando cópulas: teoría y aplicaciones," Working papers 7, Red Investigadores de Economía.
    • Handle: RePEc:rie:riecdt:7
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    Cited by:

    1. John Dairo Ramirez Aristizabal & Eduardo Alexander Duque Grisales, 2016. "Design Of A Investment Portfolio Using Non-Linear Programming: Case Of Colombia 2013-2014, Diseno De Un Portafolio De Inversion A Partir De Un Modelo De Programacion No Lineal: Caso Colombia 2013-2014," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 9(2), pages 31-47.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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