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Basel IV and the structural relationship between SA and IMA

Author

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  • Adrián F. Rossignolo

    (University of Leicester, United Kingdom)

Abstract

El artículo evalúa la profunda revisión del Libro de Negociación o Basilea IV comparando los requerimientos de capital de los Enfoques Estandarizado y de Modelos Internos en un contexto de crisis de mercado. Mediante un análisis integral secuencial abarcando cada paso de los dos regímenes -incluyendo varias especificaciones para el segundo-, el estudio halla que la modificación radical consigue su objetivo principal: la elevación del capital para riesgos de mercado. Simultáneamente, el Enfoque Estandarizado aparece favorecido estableciendo un piso alto como respaldo creíble y los Modelos Internos son penalizados con estructuras complejas y tests de validación restrictivos. Con el propósito general aparentemente logrado, sería razonable efectuar una exploración del proceso general y conceder mayor flexibilización a los supervisores locales para su aplicación. La investigación se concentró en los mercados accionaros mexicanos durante el Covid-19, y se entiende que la extensión a más países podría reforzar los resultados. Este artículo se sitúa entre los primeros en estudiar los efectos de Basilea IV y resalta algunas de sus falencias, particularmente los niveles de capital probablemente excesivos y la campaña contra los Modelos Internos, lo cual podría mellar las ganancias, restringir la innovación y reducir el crédito.

Suggested Citation

  • Adrián F. Rossignolo, 2024. "Basel IV and the structural relationship between SA and IMA," 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. 19(2), pages 1-37, Abril - J.
  • Handle: RePEc:imx:journl:v:19:y:2024:i:2:a:1
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    References listed on IDEAS

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    1. Ahelegbey, Daniel Felix & Giudici, Paolo & Mojtahedi, Fatemeh, 2021. "Tail risk measurement in crypto-asset markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    2. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.
    5. Damilola Oyetade & Adefemi A. Obalade & Paul-Francois Muzindutsi, 2023. "Basel IV capital requirements and the performance of commercial banks in Africa," Journal of Banking Regulation, Palgrave Macmillan, vol. 24(1), pages 1-14, March.
    6. Sebastian Bayer & Timo Dimitriadis, 2022. "Regression-Based Expected Shortfall Backtesting [Backtesting Expected Shortfall]," Journal of Financial Econometrics, Oxford University Press, vol. 20(3), pages 437-471.
    7. Burzoni, Matteo & Munari, Cosimo & Wang, Ruodu, 2022. "Adjusted Expected Shortfall," Journal of Banking & Finance, Elsevier, vol. 134(C).
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    10. Viviana Fernandez, 2003. "Extreme Value Theory and Value at Risk," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 18(1), pages 57-85, June.
    11. Dennis S. Mapa, 2003. "A range-based GARCH model for forecasting financial volatility," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 40(2), pages 73-90, December.
    12. Johannes Langthaler & Gerald Lederer, 2022. "Finalisierung der Basel III – Reformen: Internationale Perspektive," Springer Books, in: Christian Cech & Silvia Helmreich (ed.), Meldewesen für Finanzinstitute, edition 2, chapter 0, pages 79-93, Springer.
    13. Fang, Sheng & Cao, Guangxi & Egan, Paul, 2023. "Forecasting and backtesting systemic risk in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
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    More about this item

    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F38 - International Economics - - International Finance - - - International Financial Policy: Financial Transactions Tax; Capital Controls
    • G01 - Financial Economics - - General - - - Financial Crises
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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