Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach

Focusing on credit risk modelling, this paper introduces a novel approach for ensemble modelling based on a normative linear pooling. Models are first classified as dominant and competitive, and the pooling is run using the competitive models only. Numerical experiments based on parametric (logit, Bayesian model averaging) and nonparametric (classification tree, random forest, bagging, boosting) model comparison shows that the proposed ensemble performs better than alternative approaches, in particular when different modelling cultures are mixed together (logit and classification tree). Copyright © 2016 John Wiley & Sons, Ltd.     

信用风险模型的贝叶斯改进研究

基于小样本数据和外部先验信息,本文运用贝叶斯(Bayes)估计量来改进信用风险模型的违约预测力。同时,运用中国上市公司财务数据,分别对贝叶斯估计量和标准Logit估计量进行了模拟估计,并通过统计量AUC值和布莱尔分数(Brier Score)对其预测精度进行比较。结果表明,贝叶斯估计量具有更高的预测精度和稳定性。     

Assessing the Performance of Classification Methods

A large number of measures have been developed for evaluating the performance of classification rules. Some of these have been developed to meet the practical requirements of specific applications, but many others—which here we call “classification accuracy” criteria—represent different ways of balancing the different kinds of misclassification which may be made. This paper reviews classification accuracy criteria. However, the literature is now so large and diverse that a comprehensive list, covering all the measures and their variants, would probably be impossible. Instead, this paper embeds such measures in general framework, spanning the possibilities, and draws attention to relationships between them. Important points to note are, firstly, that different performance measures, by definition, measure different aspects of performance; secondly, that one should therefore carefully choose a measure to match the objectives of one's study; and, thirdly, that empirical comparisons between instruments measuring different aspects are of limited value.     

变量序别化在信用评分模型中的应用研究

本文通过将连续数值变量进行序别化转换赋值,并基于这些变量建立Log- it信用评分模型,通过使用统计量AUC值与条件熵比率来检验序别化转换前后所建立回归模型的违约预测力。结果发现,连续数值变量经序别化转换后可提高模型的违约预测力及其韧性。     

Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data

Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this article, we compare different methods of constructing Wald‐type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find that constructing confidence intervals using multiple imputation based on logistic regression gives the most robust coverage probability and the choice of confidence interval method is less important. However, when missingness rate is less severe (e.g. less than 70%), we recommend using Newcombe's Wald method for constructing confidence intervals along with multiple imputation using predictive mean matching.     

信用评级体系的定量验证研究

对信用评级体系的验证已成为银行风险管理面临的重要挑战之一。参照新巴塞尔资本协议的要求,除对信用评级体系进行了违约预测力和违约拟合度统计检验外,还突出了样本分布稳定性的定量验证分析。同时,基于我国上市公司资料建立信用评级体系进行了应用研究,结果显示,所建立的信用评级体系具有较高的违约预测力、违约拟合度和样本稳定性。