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Too big to fail? An analysis of the Colombian banking system through compositional data

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  • Vega Baquero, Juan David
  • Santolino, Miguel

Abstract

Although still incipient in economics and finance, compositional data analysis (in which relative information is more important than absolute values are) has become more relevant in statistical analysis in recent years. This article constructs a concentration index for financial/banking systems via compositional analysis to establish the potential existence of “too big to fail” financial entities. The intention is to provide an early warning tool for regulators about this type of institution, so they can define thresholds and measures depending on their risk appetite and the systems’ specificities. The index has been applied to the Colombian banking system and assessed over time with a forecast to determine whether the system is becoming more concentrated. Results found that the concentration index has been decreasing in recent years and the model predicts this trend will continue. Regarding the methodology used, compositional models were shown to be more stable and to lead to better prediction of the index compared to the classical multivariate methodologies.

Suggested Citation

  • Vega Baquero, Juan David & Santolino, Miguel, 2022. "Too big to fail? An analysis of the Colombian banking system through compositional data," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(2).
    • Handle: RePEc:eee:lajcba:v:3:y:2022:i:2:s2666143822000151
    DOI: 10.1016/j.latcb.2022.100060
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    References listed on IDEAS

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    1. Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
    2. Zheng, Tingguo & Chen, Rong, 2017. "Dirichlet ARMA models for compositional time series," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 31-46.
    3. Frederic S. Mishkin, 2006. "How Big a Problem is Too Big to Fail? A Review of Gary Stern and Ron Feldman's Too Big to Fail: The Hazards of Bank Bailouts," Journal of Economic Literature, American Economic Association, vol. 44(4), pages 988-1004, December.
    4. Li, Xiping & Tripe, David & Malone, Chris & Smith, David, 2020. "Measuring systemic risk contribution: The leave-one-out z-score method," Finance Research Letters, Elsevier, vol. 36(C).
    5. Chen Zhou, 2010. "Are Banks Too Big to Fail? Measuring Systemic Importance of Financial Institutions," International Journal of Central Banking, International Journal of Central Banking, vol. 6(34), pages 205-250, December.
    6. Nicola Cetorelli & Beverly Hirtle & Donald P. Morgan & Stavros Peristiani & João A. C. Santos, 2007. "Trends in financial market concentration and their implications for market stability," Economic Policy Review, Federal Reserve Bank of New York, vol. 13(Mar), pages 33-51.
    7. Stefanos Ioannou & Dariusz Wójcik & Gary Dymski, 2019. "Too-Big-To-Fail: Why Megabanks Have Not Become Smaller Since the Global Financial Crisis?," Review of Political Economy, Taylor & Francis Journals, vol. 31(3), pages 356-381, July.
    8. Petra Kynčlová & Peter Filzmoser & Karel Hron, 2015. "Modeling Compositional Time Series with Vector Autoregressive Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 303-314, July.
    9. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    10. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    11. Terence Mills, 2010. "Forecasting compositional time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(4), pages 673-690, June.
    12. Bernard Shull, 2010. "Too Big to Fail in Financial Crisis: Motives, Countermeasures, and Prospects," Economics Working Paper Archive wp_601, Levy Economics Institute.
    13. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
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    Cited by:

    1. Salvador Linares-Mustar'os & Maria `Angels Farreras-Noguer & N'uria Arimany-Serrat & Germ`a Coenders, 2022. "New financial ratios based on the compositional data methodology," Papers 2210.11138, arXiv.org.
    2. Germ`a Coenders & N'uria Arimany Serrat, 2023. "Accounting statement analysis at industry level. A gentle introduction to the compositional approach," Papers 2305.16842, arXiv.org, revised Sep 2024.

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    More about this item

    Keywords

    Simplex; Aitchison geometry; Systemically important banks; Vector autoregresion;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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