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Construcción de portafolios de inversión usando el enfoque de paridad de riesgo

Author

Listed:
  • Carlos Andres Zapata Quimbayo

    (Universidad Externado de Colombia, Colombia)

  • Robinson Alexander Garcia Gaona

    (Universidad Minuto de Dios, Colombia)

Abstract

Este trabajo propone una metodología para aplicar el enfoque de paridad de riesgo (PR) como una alternativa al enfoque tradicional media-varianza (MV) de Markowitz. Para ello, se exploran los fundamentos del enfoque de PR, basado en la noción de contribución al riesgo, donde se busca que cada activo contribuya de manera igualitaria al riesgo total del portafolio, garantizando así una diversificación óptima del portafolio de inversión. Este enfoque se contrasta con el modelo MV, cuyo rendimiento se ve afectado por problemas de concentración y errores en la estimación de los parámetros, que pueden llevar a un riesgo excesivo. Para llevar a cabo su implementación, se construyen dos portafolios diferentes: uno en el mercado de valores estadounidense y otro internacional que incluye este mercado desarrollado y emergentes, como México y Brasil. Además, se utilizan métricas de concentración, como el índice de Herfindahl-Hirschman (HHI), para demostrar que los portafolios basados en PR son más consistentes y requieren menos rebalanceo. Finalmente, se señalan algunas limitaciones del enfoque PR y recomendaciones para su implementación.

Suggested Citation

  • Carlos Andres Zapata Quimbayo & Robinson Alexander Garcia Gaona, 2025. "Construcción de portafolios de inversión usando el enfoque de paridad de riesgo," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 20(1), pages 1-14, Enero - M.
    • Handle: RePEc:imx:journl:v:20:y:2025:i:1:a:4
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    References listed on IDEAS

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    1. Lee, Tae Kyun & Sohn, So Young, 2023. "Alpha-factor integrated risk parity portfolio strategy in global equity fund of funds," International Review of Financial Analysis, Elsevier, vol. 88(C).
    2. Roncalli, Thierry, 2013. "Introduction to Risk Parity and Budgeting," MPRA Paper 47679, University Library of Munich, Germany.
    3. repec:dau:papers:123456789/4688 is not listed on IDEAS
    4. Lorenzo Garlappi & Raman Uppal & Tan Wang, 2007. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," The Review of Financial Studies, Society for Financial Studies, vol. 20(1), pages 41-81, January.
    5. Benjamin Bruder & Nazar Kostyuchyk & Thierry Roncalli, 2022. "Risk Parity Portfolios with Skewness Risk: An Application to Factor Investing and Alternative Risk Premia," Papers 2202.10721, arXiv.org.
    6. Giorgio Costa & Roy H. Kwon, 2020. "Generalized risk parity portfolio optimization: an ADMM approach," Journal of Global Optimization, Springer, vol. 78(1), pages 207-238, September.
    7. Ana Lorena Jiménez-Preciado & Francisco Venegas-Martínez & Abraham Ramírez-García, 2022. "Stock Portfolio Optimization with Competitive Advantages (MOAT): A Machine Learning Approach," Mathematics, MDPI, vol. 10(23), pages 1-16, November.
    8. Michael J. Best & Robert R. Grauer, 1991. "Sensitivity Analysis for Mean-Variance Portfolio Problems," Management Science, INFORMS, vol. 37(8), pages 980-989, August.
    9. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Keywords

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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