Constructing a climate change bank risk (CCBRω) index for the Italian banking system.

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dc.contributor.advisor Carraro, Carlo it_IT
dc.contributor.author Paliampelou, Ifigeneia <1993> it_IT
dc.date.accessioned 2020-02-15 it_IT
dc.date.accessioned 2020-06-16T05:57:38Z
dc.date.issued 2020-03-09 it_IT
dc.identifier.uri http://hdl.handle.net/10579/16528
dc.description.abstract Climate change is emerging as systemic risk with domino effects throughout the global banking system and economy. This study formulates a climate-change bank risk index (CCBRω) for the Italian banking system, with a probability-like mathematical framework limited to a range of 0 to 1, where 0% is no risk and 100% is certain risk. This index calculates climate-change transitional risk for a selected financial intermediary (SFI) by incorporating three types of factors: (i) the sum of green equities (Sgreen), (ii) the sum of brown equities (Sbrown) of a SFI in green and/or a brown company, (iii) and a mean default probability for green (GRCSω) and brown (BRCSω) companies inferred to a SFI with interval (0,1) and p ̅κ the mean probability of a set of twelve climate change risk factors (CCRF) κ, with interval (0,1). Financial data extracted from Bureau Van Dijk Orbis database for the year 2018 have been imported into a database management system (DBMS) to calculate the CCBRω index. This study concludes as follows for a banking transitional risk of a sample of 268 SFI with threshold at 5% to differentiate between Ho (ω-nth SFI without risk of default) and H1 (ω-nth SFI without risk of default): all top 30 SFI out of 268 SFI have a CCBRω index value above 5% varying with range CCBRω=7.12-15.68% and bottom 30 SFI out of 268 SFI have a CCBRω index value below 5% with range CCBRω = 0.55-3.38%. The biggest seven Italian banks of the 268 SFI sample have a CCBRω index value below 5%, apart from INTESA SANPAOLO with CCBRω=12.48%. Furthermore, the green and brown classification of results concludes that the CCBRω index for 30 SFI most exposed to the green sector with range CCBRω=15.68-4.13% indicates low risk, as nine SFI have CCBRω index below 5%. However, the CCBRω index for the 30 SFI most exposed to the brown sector with range CCBRω=14.35-5.61%, indicates high risk as all SFI indicate a CCBRω index above 5%. The 30 SFI most exposed to the green sector are all financial companies apart from one Italian commercial bank, while nine out of the 30 SFI most exposed to the brown sector are Italian commercial banks. Finally, the CCBRω index indicates higher risk for the 30 SFI least exposed to the green sector with range CCBRω = 0.7%-4.13%, and considerably lower risk for the 30 SFI least exposed to the brown sector with range CCBRω = 0.55-3.38%, which reflects that SFI have more appetite for a minimum green risk exposure than a minimum brown risk exposure. The 30 SFI least exposed to the green sector are all financial companies apart from one of the biggest Italian banks BANCA MONTE DEI PASCHI DI SIENA SPA with CCBRω =3.85%. Conversely, eight out of the 30 SFI least exposed to the brown sector are commercial banks including two of the biggest Italian banks UNICREDIT SPA and FINECOBANK BANCA FINECO SPA with CCBRω =0.71%. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Ifigeneia Paliampelou, 2020 it_IT
dc.title Constructing a climate change bank risk (CCBRω) index for the Italian banking system. it_IT
dc.title.alternative it_IT
dc.type Master's Degree Thesis it_IT
dc.degree.name Scienze ambientali it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Scuola in Sostenibilità dei sistemi ambientali e turistici it_IT
dc.description.academicyear 2018/2019, sessione straordinaria it_IT
dc.rights.accessrights closedAccess it_IT
dc.thesis.matricno 869479 it_IT
dc.subject.miur SECS-P/03 SCIENZA DELLE FINANZE it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend 10000-01-01
dc.provenance.upload Ifigeneia Paliampelou (869479@stud.unive.it), 2020-02-15 it_IT
dc.provenance.plagiarycheck Carlo Carraro (ccarraro@unive.it), 2020-03-02 it_IT


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