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SRISK: una medida de riesgo sistémico para la banca colombiana 2005-2021

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  • Camilo Eduardo Sánchez-Quinto

Abstract

Una de las lecciones que dejó la crisis financiera de 2008 fue la importancia de monitorear el riesgo sistémico en la búsqueda de la estabilidad de los sistemas financieros. Al respecto se han desarrollado líneas de investigación que, tomando la mayor cantidad de información, tienen el objetivo de brindar métricas fiables y oportunas de este riesgo. Entre ellas se encuentra el SRISK (Brownlees & Engle, 2016), una medida que combina el comportamiento del mercado, la relación de solvencia, el nivel de apalancamiento y los resultados contables de las entidades financieras para hallar el riesgo sistémico bajo un escenario de crisis financiera. Este documento replica la metodología SRISKajustada para el sistema bancario colombiano a través de modelos GJR-GARCH-DCC. Los resultados indican que, si bien el riesgo sistémico en la banca ha sido históricamente bajo, este alcanzó su máximo histórico en 2020, mostrando el impacto de la crisis sanitaria del Covid-19. Adicionalmente, se encuentra que el SRISK se correlaciona con variables de la actividad productiva y financiera, además tener capacidad predictiva en sentido de Granger. **** ABSTRACT: One of the lessons we learned from the 2008 financial crisis was the importance of monitoring the systemic risk in the stability of financial systems. In this regard, lines of research have been developed with the aim to provide reliable and timely metrics on this risk, taking as much information as possible. Among these, SRISK(Brownlees & Engle, 2016) stands out, a measure that combines market behavior, capital ratio, leverage and balance sheet of financial institutions to find the systemic risk exposure under a sustained crisis scenario. This paper replicates the SRISKmethodology adjusted for the Colombian banking system using GJR-GARCH-DCC models. The results show that, although systemic risk of banks has been historically low, it reached its maximum in 2020, adding empirical evidence on the impact of Covid-19 crisis. Furthermore, it is found that SRISKcorrelates with leading indicators of economic and financial sectors, in addition to having predictive power in the sense of Granger causality.

Suggested Citation

  • Camilo Eduardo Sánchez-Quinto, 2022. "SRISK: una medida de riesgo sistémico para la banca colombiana 2005-2021," Borradores de Economia 1207, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:1207
    DOI: 10.32468/be.1207
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    References listed on IDEAS

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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