A quantitative approach to forecast US high yield corporate bonds default rates

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dc.contributor.advisor Billio, Monica it_IT
dc.contributor.author Vanin, Gregorio <1993> it_IT
dc.date.accessioned 2019-02-16 it_IT
dc.date.accessioned 2019-06-11T08:43:34Z
dc.date.issued 2019-03-21 it_IT
dc.identifier.uri http://hdl.handle.net/10579/14701
dc.description.abstract The main ambition of this work is to present the current dynamics of the US high yield corporate bond market, providing readers with a detailed analysis of the historical evolution of the market and the potential future developments. Primary emphasis is placed on understanding the default rate of the US high yield corporate bond market and the relative underlying drivers, with the ambition to build an effective and efficient quantitative model to forecast its evolution in the short run. A comparative analysis of the estimates obtained through the application of a panel of different econometric models is left as conclusion. Considering the growing interest demonstrated by investors towards the high yield corporate bond market, the thesis strives to contribute to the increasing literature in this field by presenting a new and original approach to model the default rates. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Gregorio Vanin, 2019 it_IT
dc.title A quantitative approach to forecast US high yield corporate bonds default rates it_IT
dc.title.alternative A Quantitative Approach to Forecast US High Yield Corporate Bonds Default Rates it_IT
dc.type Master's Degree Thesis it_IT
dc.degree.name Economia e finanza - economics and finance it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Economia it_IT
dc.description.academicyear 2017/2018, sessione straordinaria it_IT
dc.rights.accessrights closedAccess it_IT
dc.thesis.matricno 843260 it_IT
dc.subject.miur SECS-P/05 ECONOMETRIA 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 Gregorio Vanin (843260@stud.unive.it), 2019-02-16 it_IT
dc.provenance.plagiarycheck Monica Billio (billio@unive.it), 2019-03-04 it_IT


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