英文标题：
《Entropy and credit risk in highly correlated markets》
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作者：
Sylvia Gottschalk
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最新提交年份：
2016
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英文摘要：
We compare two models of corporate default by calculating the Jeffreys-Kullback-Leibler divergence between their predicted default probabilities when asset correlations are either high or low. Our main results show that the divergence between the two models increases in highly correlated, volatile, and large markets, but that it is closer to zero in small markets, when asset correlations are low and firms are highly leveraged. These findings suggest that during periods of financial instability the single-and multi-factor models of corporate default will generate increasingly inconsistent predictions.
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中文摘要：
我们通过计算资产相关性高或低时，两种公司违约模型的预测违约概率之间的Jeffreys-Kullback-Leibler差异来比较两种公司违约模型。我们的主要结果表明，在高度相关、波动性大的市场中，这两个模型之间的差异会增加，但在资产相关性较低且企业杠杆率较高的小市场中，这一差异更接近于零。这些发现表明，在金融不稳定时期，企业违约的单因素和多因素模型将产生越来越不一致的预测。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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