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MUTOMBO_76911400_2020.pdf
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MUTOMBO_76911400_2020_annexe_1.pdf
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MUTOMBO_76911400_2020_annexe_2pdf.pdf
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- This thesis follows the paper of Bellavite Pellegrini & al. (2017). The aim is to study the link between systemic risk and shadow banking through a panel regression. The panel regression is applied because cross-section and temporal series are both present in the database. In order to measure systemic risk, the correlated value at risk (CoVaR) methodology is used. The values of this systemic risk are obtained by applying quantile regressions in the RStudio program. Furthermore, data between 2008 and 2019 from European and North American banks have been collected to quantify the shadow banking activity. A comparison of the results of different types of shadow banking data (aggregated levels and desegregated level) is made. Additionally, a robust testing is done by adding data from non quoted banks. Finally, the conclusion of the results shows that shadow banking is not statistically significant in any of the regression, and therefore, fails to show that it is associated to systemic risk. However, other variables show to have a correlation with systemic risk: the size, the leverage and the CoVaR.