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 |