英文标题：
《Disentangling and Assessing Uncertainties in Multiperiod Corporate
Default Risk Predictions》
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作者：
Miao Yuan, Cheng Yong Tang, Yili Hong, Jian Yang
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最新提交年份：
2018
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英文摘要：
Measuring the corporate default risk is broadly important in economics and finance. Quantitative methods have been developed to predictively assess future corporate default probabilities. However, as a more difficult yet crucial problem, evaluating the uncertainties associated with the default predictions remains little explored. In this paper, we attempt to fill this blank by developing a procedure for quantifying the level of associated uncertainties upon carefully disentangling multiple contributing sources. Our framework effectively incorporates broad information from historical default data, corporates\' financial records, and macroeconomic conditions by a) characterizing the default mechanism, and b) capturing the future dynamics of various features contributing to the default mechanism. Our procedure overcomes the major challenges in this large scale statistical inference problem and makes it practically feasible by using parsimonious models, innovative methods, and modern computational facilities. By predicting the marketwide total number of defaults and assessing the associated uncertainties, our method can also be applied for evaluating the aggregated market credit risk level. Upon analyzing a US market data set, we demonstrate that the level of uncertainties associated with default risk assessments is indeed substantial. More informatively, we also find that the level of uncertainties associated with the default risk predictions is correlated with the level of default risks, indicating potential for new scopes in practical applications including improving the accuracy of default risk assessments.
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中文摘要：
衡量公司违约风险在经济和金融领域具有广泛的重要性。已经开发出定量方法来预测未来公司违约概率。然而，作为一个更为困难但至关重要的问题，评估与违约预测相关的不确定性仍然很少有人探讨。在本文中，我们试图通过开发一种程序来填补这一空白，该程序用于在仔细分离多个贡献源后量化相关不确定性的水平。我们的框架有效地整合了历史违约数据、公司财务记录和宏观经济状况中的广泛信息，方法是a）描述违约机制，b）捕捉违约机制各种特征的未来动态。我们的程序克服了这一大规模统计推断问题中的主要挑战，并通过使用精简模型、创新方法和现代计算设施使其切实可行。通过预测市场范围内的违约总数和评估相关的不确定性，我们的方法也可以用于评估总的市场信用风险水平。通过分析美国市场数据集，我们证明，与违约风险评估相关的不确定性水平确实很大。更具信息性的是，我们还发现，与违约风险预测相关的不确定性水平与违约风险水平相关，表明在实际应用中可能出现新的范围，包括提高违约风险评估的准确性。
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分类信息：

一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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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