Title: Disentangling and Assessing Uncertainties in Multiperiod Corporate Default Risk Predictions,
Abstract: 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.