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Explaining Systemic Risk in Latin American Banking Industry over 2002–2015

In: Asia-Pacific Contemporary Finance and Development

Author

Listed:
  • Carlo Bellavite Pellegrini
  • Laura Pellegrini
  • Emiliano Sironi

Abstract

Systemic risk has been one of the most interesting issues in banking and financial literature during the last years, particularly in evaluating its effects on the stability of the whole financial system during crises. Differently from other studies which analyze systemic risk focusing on European countries, we explore the determinant of systemic risk in other regional or continental banking systems, as Latin America. Using the CoVaR approach proposed by Adrian and Brunnermeier (2016), we study the impact of corporate variables on systemic risk on a sample of 30 Latin American banks belonging to seven countries, continuously listed from 2002Q1 to 2015Q4. We investigate the contribution of the corporate variables over different economic periods: the Subprime crisis (2007Q3–2008Q3), the European Great Financial Depression (2008Q4–2010Q2), and the Sovereign debt crisis (2010Q3–2012Q3).

Suggested Citation

  • Carlo Bellavite Pellegrini & Laura Pellegrini & Emiliano Sironi, 2019. "Explaining Systemic Risk in Latin American Banking Industry over 2002–2015," International Symposia in Economic Theory and Econometrics, in: Asia-Pacific Contemporary Finance and Development, volume 26, pages 287-309, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:isetez:s1571-038620190000026014
    DOI: 10.1108/S1571-038620190000026014
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    More about this item

    Keywords

    systemic risk; traditional banking system; global financial crises; Latin American countries; value at risk; CoVaR; panel data; G01; G15; G021; C23;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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