标题: 产业关联风险、财务危机与公司债收益率价差
Industry Correlation Risk, Financial Distress, and Corporate Bond Yield Spreads
作者: 李汉星
Lee Han-Hsing
国立交通大学财务金融研究所
关键字: 产业关联风险;违约丛集;公司债收益率价差;违约预测;Industry Correlation Risk;Default Clustering;Corporate Bond Yield Spreads;Default Prediction
公开日期: 2013
摘要: 计画名称:产业关联风险、财务危机与公司债收益率价差
研究者:李汉星
经费来源:行政院国家科学委员会
近十來的金融危机已提升了信用事件预测与发展制定适切系统风险衡量指标的重
要性。先前的研究已指出资产关聯性在金融危机与熊市时增强 (Longin and Solnik, 2001;
Chordia, Goyal, and Tong (2011)),虽然此现象于文献已充记载,但对其背后所产生的原
因仍未有一致结論,金融危机时攀升的报酬相关性并无法被基本因子如总体经济与公
司特征所完整解释。因此,学者们提出在机构处于财务危机条件下的系统风险的衡量
指标(Adrian and Brunnermeier, 2011; Acharya et al., 2010)。过去文献亦已指出产业特性
可影响证券报酬与信用传递的方式(Hou and Robinson, 2006; Acharya, Bharath, and
Srinivasan, 2007)。然而,仅有非常少的研究导入产业内的相关性风险进行公司债收益
率价差与信用分析。因此,在本研究中,我们尝试研究产业内的相关性风险对公司债
收益率价差与破产预测影响。
本研究将首先进行理論与实证的文献探讨。我们将检验产业内关聯风险对公司债收
益率价差与违约预测的影响性。由于公司同时可能有发行超过一种以上的债券,我们
采用Petersen(2009)与Cameron, Gelbach and Miller (2011)等学者提出的方式,计算兩个
维度丛集的稳健标准误以处裡时间序列以及異质变異问题。接下來,我们采用Duan et
al. (2012) 的远期违约强度模型,检验产业内关聯风险是否可在其他控制变數存在下仍
显着影响公司违约风险。最后,我们将进行样本外破产预测绩效之分析,以检验加入
产业关聯风险因素可否增进破产预测之正确性
Title: Industry Correlation Risk, Financial Distress, and Corporate Bond Yield Spreads
Principal Investigator: Han-Hsing Lee
Sponsor:National Science Council

The financial crises in the last decade have raised the importance of the forecast of credit
events and the need for devising appropriate measures of systematic risk. Previous empirical
evidence documented that asset correlations increase during financial crisis and bear markets
(Longin and Solnik, 2001; Chordia, Goyal, and Tong (2011)). Although it is well-documented
in the literature that asset returns become more correlated during financial crisis, debate is still
ongoing about the explanation of the increasing comovement in asset returns. The heightened
correlation during crisis seems not be fully explained by the fundamentals such as macro
factors or firm characteristics. As such, researchers have proposed measures for systemic risk
conditional on institutions being under distress (Adrian and Brunnermeier, 2011; Acharya et al.,
2010). Previous literature indicated that industry attributes can affect the security returns and
how credit risks propagate (Hou and Robinson, 2006; Acharya, Bharath, and Srinivasan, 2007);
however, there are few studies of corporate bond yield spreads and credit analyses
incorporating the correlation risk among firms in an industry. Therefore, in this study, we
attempt to approach this issue by investigating the impact of industry correlation risk on
corporate bond yield spreads and default prediction.
We will first review theoretical and empirical studies of industry correlation risk, asset
correlation, corporate bond yield spreads, and default prediction. In our empirical test, we
will examine the importance of industry correlation risk on corporate bond yield spreads and
try to disentangle the effects of aggregate market risk and industry risk using several
measures of industry risk. Given that each issuer may have more than one bond outstanding
at any given point in time, we calculate two-dimensional cluster robust standard errors
(Petersen, 2009; Cameron, Gelbach and Miller, 2011) to correct for time-series effect and
heteroskedasticity. Next, we will use the forward intensity approach of Duan et al. (2012) to
examine if industry correlation risk can affect default risk. This is the attempt to establish a
linkage between industry correlation risk and credit risk. Finally, we will perform
out-of-sample default prediction to examine if the inclusion of variables regarding market
correlation risk can enhance the default prediction accuracy.
官方说明文件#: NSC102-2410-H009-008
URI: http://hdl.handle.net/11536/91294
https://www.grb.gov.tw/search/planDetail?id=3098842&docId=418596
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