多期公司违约风险预测中的不确定性分解与评估
Disentangling and Assessing Uncertainties in Multiperiod Corporate Default Risk Predictions
摘要：
企业违约风险的度量在经济学和金融学中具有广泛的重要意义。定量方法已经发展到预测性地评估未来的公司违约概率。然而，作为一个更困难，但关键的问题，评估与违约预测的不确定性仍然很少有人探讨。在本文中，我们试图填补这一空白，开发一个程序，量化相关的不确定性水平仔细解开多个贡献源。我们的框架有效地整合了来自历史违约数据、企业财务记录和宏观经济状况的广泛信息，方法是：a）表征违约机制，b）捕捉有助于违约机制的各种特征的未来动态。我们的程序克服了在这个大规模的统计推断问题的主要挑战，并通过使用简约的模型，创新的方法，和现代计算设施，使其切实可行。通过预测整个市场的违约总数并评估相关的不确定性，我们的方法还可以应用于评估总体市场信用风险水平。在分析美国市场的数据集，我们表明，与违约风险评估的不确定性水平确实是巨大的。更多的信息，我们还发现，与违约风险预测相关的不确定性的水平与违约风险的水平相关，表明在实际应用中的新的范围，包括提高违约风险评估的准确性的潜力。
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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