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Operational Risk Measured by Bayesian Networks with a Poisson-Gamma Joint Distribution in a Financial Firm

Author

Listed:
  • Griselda Dávila-Aragón

    (Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales)

  • Salvador Rivas-Aceves

    (Universidad Panamericana, Escuela de Gobierno y Economía)

  • Francisco Ortiz-Arango

    (Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales)

Abstract

El objetivo es cuantificar requerimientos de capital y riesgo operacional mediante inferencia bayesiana, mediante un modelo de distribución conjunta Poisson-Gamma alimentado por información de expertos para una institución financiera mexicana. Simulaciones Monte Carlo basadas en intervalos del valor esperado del evento de pérdida muestran que: 1) El valor del riesgo operacional se puede obtener con información insuficiente con 95% de confianza, 2) las pérdidas esperadas tienden a aumentar cuando los sucesos que esperan los expertos también se incrementan, 3) hay una correlación positiva entre el riesgo operativo y los eventos esperados por los expertos, 4) la frecuencia y severidad de las pérdidas son más pequeñas al principio y luego crecen conforme el valor en riesgo operacional se acerca al óptimo, después ambos disminuyen nuevamente. Los resultados descritos dependen de los supuestos del modelo así como de la opinión de los expertos y la información disponible al interior de la firma. La metodología propuesta proporciona una medición avanzada del riesgo operativo, por lo que se puede formular una estrategia específica para que una empresa financiera evite pérdidas y asuma riesgo operacional.

Suggested Citation

  • Griselda Dávila-Aragón & Salvador Rivas-Aceves & Francisco Ortiz-Arango, 2017. "Operational Risk Measured by Bayesian Networks with a Poisson-Gamma Joint Distribution in a Financial Firm," 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. 12(4), pages 351-363, Octubre-D.
    • Handle: RePEc:imx:journl:v:12:y:2017:i:4:p:351-363
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    References listed on IDEAS

    as
    1. Xiaoping Zhou & Rosella Giacometti & Frank J. Fabozzi & Ann H. Tucker, 2014. "Bayesian estimation of truncated data with applications to operational risk measurement," Quantitative Finance, Taylor & Francis Journals, vol. 14(5), pages 863-888, May.
    2. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
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    More about this item

    Keywords

    Bayesian Analysis; Gamma and Poisson Distributions; Operational Risk;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • G19 - Financial Economics - - General Financial Markets - - - Other

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