Disentangling and Assessing Uncertainties in Multiperiod Corporate Default Risk Predictions


SpeakerDr Cheng Yong TANG

Site: B127

Time 1000-11:30  Tuesday Nov.21 2017

Abstract: Measuring credit risks for individual companies, industrial segments, and market systems is fundamentally and broadly important in economics, finance and beyond. For such a purpose, various quantitative methods have been developed to predicatively assess the probabilities of companies going default in future. However, as a more difficult yet crucial problem, evaluating the uncertainties associated with the default predictions remains little explored. In this paper, we develop, for the first time in the scenario of default predictions, a procedure for quantifying the level of associated uncertainties by carefully disentangling multiple contributing sources. Our framework effectively incorporates broad information from historical default data, financial records, and economic environmental conditions by a) characterizing the default mechanism, and b) capturing the future dynamics of various features contributing to the default mechanism. Our development of the framework overcomes major challenges in this tremendously large scale statistical inference problem and makes it practically feasible by using parsimonious models, innovative methods, and modern computational facilities. By appropriately predicting the market-wise total number of defaults and assessing the associated uncertainties, our method can effectively evaluate the aggregated market credit risk level. Upon analyzing a US market data set with our method, we demonstrate that the level of uncertainties associated with default risk assessments is indeed substantial. More importantly and 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 benefiting practical applications including improving the accuracy of default risk assessments. This is a joint work with Miao Yuan, Yili Hong, and Jian Yang.