Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 黃木楠 | zh_TW |
dc.contributor.author | 李漢星 | zh_TW |
dc.contributor.author | Huang, Mu-Nan | en_US |
dc.contributor.author | Lee, Han-Hsing | en_US |
dc.date.accessioned | 2018-01-24T07:40:14Z | - |
dc.date.available | 2018-01-24T07:40:14Z | - |
dc.date.issued | 2017 | en_US |
dc.identifier.uri | http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070453923 | en_US |
dc.identifier.uri | http://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.abstract | Previous 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.iso | en_US | en_US |
dc.subject | CoVaR | zh_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.subject | CoVaR | en_US |
dc.subject | Default prediction | en_US |
dc.subject | Eigenvector centrality | en_US |
dc.subject | Forward intensity model | en_US |
dc.subject | Systemic risk | en_US |
dc.title | 產業網絡風險與違約預測 | zh_TW |
dc.title | Industry Network Risk and Corporate Default Prediction | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | 財務金融研究所 | zh_TW |
Appears in Collections: | Thesis |