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  • 1.
    Da Fermo, Carmine
    et al.
    Sella SGR, Milan, Italy.
    Tanzi, Paola Musile
    Department of Economics, University of Perugia, Perugia, Italy.
    Nicolosi, Marco
    Department of Human Sciences, Link Campus University, Rome, Italy.
    Stanghellini, Elena
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet. Department of Economics, University of Perugia, Perugia, Italy.
    On the relationship between financial and sustainable variables: insights from graphical gaussian model2024Ingår i: The Journal of Financial Management, Markets and Institutions, E-ISSN 2282-717X, artikel-id 2430001Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    In recent years, attention toward Environmental, Social and Governance (ESG) issues has become increasingly important in the investment decision-making process, prompting interest of investors, companies, regulators and researchers on the possible relationships between financial performances and sustainable variables. With the aim to increase our understanding of these relationships, we use a graphical modeling approach on the MSCI and Bloomberg sustainable dataset for years from 2017 to 2021. Our analysis shows that companies with a higher level of compliance with ESG standards have lower assets' volatility than others and are not penalized in terms of returns. Furthermore, the increasing level of mandatory disclosure within the European area, induced by the current regulation, has reduced the strength of the positive relationship between Disclosure Score and ESG Score. Moreover, the negative relationship between ESG Score and volatility remains consistent across temporal and geographic areas.

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  • 2.
    De Novellis, G.
    et al.
    SDA Bocconi School of Management, Milan, Italy.
    Musile Tanzi, P.
    SDA Bocconi School of Management, Milan, Italy; Department of Economics, University of Perugia, Perugia, Italy.
    Ranalli, M.G.
    Department of Political Sciences, University of Perugia, Perugia, Italy.
    Stanghellini, Elena
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet. Department of Economics, University of Perugia, Perugia, Italy.
    Leveraged finance exposure in the banking system: systemic risk and interconnectedness2024Ingår i: Journal of international financial markets, institutions, and money, ISSN 1042-4431, E-ISSN 1873-0612, Vol. 90, artikel-id 101890Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the post-pandemic era, the exposure to leveraged finance has emerged as a key factor of vulnerability for banks, coping with increasing inflation and interest rates. For this reason, the growth of the leveraged loans market is receiving significant attention from the Authorities (e.g. ECB, 2022). In this paper, we analyze an original sample of leveraged loans (1699) that combines instrument-specific information and the composition of the syndicates, with a specific focus on the G-SIBs participation from 2014 to 2021. The aim is to identify risk indicators that take into account the G-SIBs exposure to risky leveraged loans, the potential impact of the banks’ size and their interconnectedness. For this purpose, using M-Quantile regression for binary data, it is possible to obtain a first indicator measuring heterogeneity among banks in terms of credit risk exposure, a second indicator that combines the previous one with the banks’ size, and a third indicator as a measure of interconnectedness between banks.

  • 3.
    Doretti, Marco
    et al.
    Department of Statistics, Computer Science, and Applications, University of Florence, Florence, Italy.
    Genbäck, Minna
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Stanghellini, Elena
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet. Department of Economics, University of Perugia, Perugia, Italy.
    Mediation analysis with case–control sampling: identification and estimation in the presence of a binary mediator2024Ingår i: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 66, nr 1, artikel-id 2300089Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With reference to a stratified case–control (CC) procedure based on a binary variable of primary interest, we derive the expression of the distortion induced by the sampling design on the parameters of the logistic model of a secondary variable. This is particularly relevant when performing mediation analysis (possibly in a causal framework) with stratified case–control (SCC) data in settings where both the outcome and the mediator are binary. Despite being designed for parametric identification, our strategy is general and can be used also in a nonparametric context. With reference to parametric estimation, we derive the maximum likelihood (ML) estimator and the M-estimator of the joint outcome–mediator parameter vector. We then conduct a simulation study focusing on the main causal mediation quantities (i.e., natural effects) and comparing M- and ML estimation to existing methods, based on weighting. As an illustrative example, we reanalyze a German CC data set in order to investigate whether the effect of reduced immunocompetency on listeriosis onset is mediated by the intake of gastric acid suppressors.

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