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Macroeconomic Reverse Stress Testing: An Early-Warning System for Spanish Banking Regulators. Analysis Based on the 2008 Global Financial Crisis / Prueba de resistencia inversa Macroeconómica: una prueba de alerta temprana para los reguladores bancarios españoles. Análisis basado en la crisis financiera global de 2008

Author

Listed:
  • Cristófoli, María Elizabeth

    (Banco de España, Madrid, España)

  • García Fronti, Javier

    (Facultad de Ciencias Económicas, Córdoba, Argentina, Universidad de Buenos Aires.)

Abstract

This paper presents a methodology that helps regulators to identify early-warning alerts regarding the stability of the financial system. It is a macroeconomic Reverse Stress Testing analysis which examines the interrelationships between different factors in the financial system during an economic crisis period. Archimedean copulas (Gumbel copula) were applied in the modelling of these interactions, showing the interdependence of specific factors. The methodology is applied using four factors: Bank loans to the insurance sector, Spanish exports, the Energy Price Index in Spain, and the growth rate of the Stock Price Index. First, each factor was projected for three years into the future. After that, each factor was calculated to identify the probability distribution that best fitted its projected data. Copula parameters were computed, and each alert level parameter for scethe financial system was established. Finally, an exhaustive analysis of the results was conducted. / Este documento presenta una metodología que ayuda a los reguladores a identificar alertas tempranas sobre la estabilidad del sistema financiero. Es un análisis de Pruebas de Resistencia Inversa macroeconómica que examina las interrelaciones entre diferentes factores en el sistema financiero durante un período de crisis económica. Se aplicaron cópulas arquimedianas (cópula de Gumbel) en el modelado de estas interacciones, mostrando la interdependencia de factores específicos. La metodología se aplica utilizando cuatro factores: los préstamos bancarios al sector de seguros, las exportaciones españolas, el Índice de Precios de la Energía en España y la tasa de crecimiento del Índice de Precios de las Acciones. Primero, se proyectó el comportamiento futuro de cada factor por tres años. Luego, se analizó cada factor para identificar la distribución de probabilidad que mejor se ajustaba a los datos proyectados. Se calculó el parámetro de la cópula y se estableció el nivel de alerta de cada parámetro para el sistema financiero. Finalmente, se realizó un análisis exhaustivo de los resultados.

Suggested Citation

  • Cristófoli, María Elizabeth & García Fronti, Javier, 2019. "Macroeconomic Reverse Stress Testing: An Early-Warning System for Spanish Banking Regulators. Analysis Based on the 2008 Global Financial Crisis / Prueba de resistencia inversa Macroeconómica: una pru," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 9(2), pages 181-204, julio-dic.
    • Handle: RePEc:sfr:efruam:v:9:y:2019:i:2:p:181-204
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    References listed on IDEAS

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    1. Ruodu Wang & Liang Peng & Jingping Yang, 2013. "Bounds for the sum of dependent risks and worst Value-at-Risk with monotone marginal densities," Finance and Stochastics, Springer, vol. 17(2), pages 395-417, April.
    2. Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    3. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    4. Thomas Breuer & Martin Jandacka & Klaus Rheinberger & Martin Summer, 2009. "How to Find Plausible, Severe and Useful Stress Scenarios," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 205-224, September.
    5. Amira Dridi, 2015. "On Reverse Stress Testing for Worst Case Scenarios: An Application to Credit Risk Modeling of Tunisian Economic Sectors," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(2), pages 40-56, June.
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    More about this item

    Keywords

    Reverse Stress Testing; Financial Stability; Time Series; Copulas. / prueba de resistencia inversa; estabilidad financiera; series de tiempo; copulas.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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