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dc.contributor.author黃木楠zh_TW
dc.contributor.author李漢星zh_TW
dc.contributor.authorHuang, Mu-Nanen_US
dc.contributor.authorLee, Han-Hsingen_US
dc.date.accessioned2018-01-24T07:40:14Z-
dc.date.available2018-01-24T07:40:14Z-
dc.date.issued2017en_US
dc.identifier.urihttp://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070453923en_US
dc.identifier.urihttp://hdl.handle.net/11536/141094-
dc.description.abstract過去文獻指出跨產業的報酬與尾端風險具可預測性。因此本研究使用Duan et al. (2012)提出的遠期強度模型,來分析產業報酬與尾端風險對違約預測的影響。除控制違約預測文獻中常使用的總體與公司變數外,我們加入了產業位置(中心性)、產業連結性(體系風險)與產業報酬,來探究這些產業層級變數是否影響公司之違約機率。整體而言,我們的實證結果支持加入上述產業變數能幫助解釋公司之違約機率。zh_TW
dc.description.abstractPrevious literature has documented the cross-industry return and tail risk predictability, especially during financial crisis. This study investigates the effects of inter-industry return and tail risk in the context of default prediction using forward intensity approach of Duan et al. (2012). Controlling for commonly used macro and firms-specific variables in default analysis, we incorporate industries’ position, connectedness and return to examine whether these industry-level variables affect corporate default probabilities. Overall, our empirical results support that these industry variables can help explain corporate default probabilities.en_US
dc.language.isoen_USen_US
dc.subjectCoVaRzh_TW
dc.subject違約預測zh_TW
dc.subject特徵向量中心性zh_TW
dc.subject遠期強度模型zh_TW
dc.subject體系風險zh_TW
dc.subjectCoVaRen_US
dc.subjectDefault predictionen_US
dc.subjectEigenvector centralityen_US
dc.subjectForward intensity modelen_US
dc.subjectSystemic risken_US
dc.title產業網絡風險與違約預測zh_TW
dc.titleIndustry Network Risk and Corporate Default Predictionen_US
dc.typeThesisen_US
dc.contributor.department財務金融研究所zh_TW
Appears in Collections:Thesis