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Proyección Markoviana para 2020 y 2021 de las Calificaciones Corporativas en México

Author

Listed:
  • David Conaly Martínez Vázquez

    (Universidad Autónoma del Estado de México, México)

  • Christian Bucio Pacheco

    (Universidad Autónoma del Estado de México, México)

  • Alejandra Cabello Rosales

    (Universidad Nacional Autónoma de México, México)

Abstract

Un elemento fundamental para la toma de decisiones en los mercados financieros y la economía, son las perspectivas crediticias de las empresas; sus calificaciones son una referencia clave ante la incertidumbre de los escenarios económicos y el movimiento de sus propios activos financieros en el mercado. Este trabajo analiza la dinámica a corto plazo de calificaciones de crédito de las principales corporaciones en México; examina la evolución de las calificaciones del sector corporativo en México y simula su comportamiento; se utiliza como base de datos los reportes de las Calificaciones Nacionales Corporativas de Fitch México 2002-2018. Se emplea la metodología de cadenas de Markov, primero se estiman las probabilidades de transición a través de Máxima Verosimilitud para confirmar la eficiencia metodológica y posteriormente se generan las proyecciones a 2020 y 2021. La evidencia empírica muestra que las calificaciones de crédito de las corporaciones en México presentan una tendencia decreciente pero estable en el corto plazo. Se recomienda que tanto las empresas como el gobierno profundicen la diversificación económica y mantengan una gestión disciplinada de sus operaciones como factores que coadyuven a sostener grados de inversión favorables.

Suggested Citation

  • David Conaly Martínez Vázquez & Christian Bucio Pacheco & Alejandra Cabello Rosales, 2021. "Proyección Markoviana para 2020 y 2021 de las Calificaciones Corporativas en México," 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. 16(1), pages 1-21, Enero - M.
  • Handle: RePEc:imx:journl:v:16:y:2021:i:1:a:3
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    References listed on IDEAS

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    1. Hernández Ángeles, Ignacio F. & Francisco-López Herrera & Luis Fernando Hoyos Reyes, 2015. "Análisis del efecto apalancamiento en los rendimientos del IPC mediante una Cadena de Markov Monte Carlo antes, durante y después de la crisis subprime./ Analysis of the leverage effect on the IPC ret," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 5(1), pages 43-64, enero-jun.
    2. McQueen, Grant & Thorley, Steven, 1991. "Are Stock Returns Predictable? A Test Using Markov Chains," Journal of Finance, American Finance Association, vol. 46(1), pages 239-263, March.
    3. Rosati, Nicoletta & Bellia, Mario & Matos, Pedro Verga & Oliveira, Vasco, 2020. "Ratings matter: Announcements in times of crisis and the dynamics of stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 64(C).
    4. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    5. Ryan, Terence M., 1973. "Security Prices as Markov Processes," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 8(1), pages 17-36, January.
    6. Dianfa Chen & Jun Deng & Jianfen Feng & Bin Zou, 2020. "A set-valued Markov chain approach to credit default," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 669-689, April.
    7. Masaaki Kijima, 1998. "Monotonicities in a Markov Chain Model for Valuing Corporate Bonds Subject to Credit Risk," Mathematical Finance, Wiley Blackwell, vol. 8(3), pages 229-247, July.
    8. S. Baena‐Mirabete & P. Puig, 2018. "Parsimonious higher order Markov models for rating transitions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 107-131, January.
    9. D. V. Boreiko & S. Y. Kaniovski & Y. M. Kaniovski & G. Ch. Pflug, 2019. "Identification of hidden Markov chains governing dependent credit-rating migrations," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(1), pages 75-87, January.
    10. Hernández, Onésimo & Venegas, Francisco, 2012. "Toma de decisiones de agentes racionales con procesos markovianos. Avances recientes en economía y finanzas," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(316), pages 733-779, octubre-d.
    11. G. dos Reis & M. Pfeuffer & G. Smith, 2020. "Capturing model risk and rating momentum in the estimation of probabilities of default and credit rating migrations," Quantitative Finance, Taylor & Francis Journals, vol. 20(7), pages 1069-1083, July.
    12. Marius Pfeuffer & Goncalo dos Reis & Greig smith, 2018. "Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations," Papers 1809.09889, arXiv.org, revised Feb 2020.
    13. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    14. Puneet Pasricha & Dharmaraja Selvamuthu & Viswanathan Arunachalam, 2017. "Markov regenerative credit rating model," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 18(3), pages 311-325, May.
    15. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    More about this item

    Keywords

    Cadenas de Markov; Calificadoras; Calificaciones Corporativas; Bolsa Mexicana de Valores;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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