基于平衡计分卡的保险公司全面风险管理绩效评估

本文论述了以RAROC为主的财务导向的风险管理绩效评价方法在评估全面风险管理时的不足,提出了基于平衡计分卡（BSC）的风险管理绩效评估框架,并论述了此种评估方法对实施全面风险管理（ERM）所具有的重要意义.     

Using ROC techniques to measure the effectiveness of foreign exchange market interventions

Using as an example data on official Japanese interventions in the yen/dollar market, I show that techniques developed to analyse relative operating characteristic (ROC) curves can be useful to study the forecasting value and effectiveness of interventions.     

基于非线性主成分分析的信用评估模型研究

传统的主成分分析（PCA）本质上是一种线性映射算法,无法有效处理非线性关系的数据。本文在分析自联想神经网络（AANN）的基础上,借鉴传统PCA方法中的序数主成分概念,提出了基于顺序自联想神经网络（SAANN）的非线性主成分分析法（NLPCA）。进一步,结合神经网络（NN）和Logisitic模型,以我国上市公司为研究对象,分别构建了基于NLPCA-NN和NLPCA-Logisitic的信用评估模型。实证结果及ROC曲线分析表明,本文构建的NLPCA相比传统的线性PCA方法能有效地实现数据的非线性特征提取与降维,提高模型预测性能。此外,实证结果还表明,在相同PCA方法处理数据的条件下,神经网络模型的信用评估效果要好于Logisitic模型。     

Measuring Diagnostic Accuracy of Statistical Prediction Rules

Many different statistical methods have been developed for predicting the disease classes of patients. However, in order to have confidence in the results of such methods, their performance needs to be assessed. Different performance measures are reviewed and the circumstances in which they are relevant are described. Subtleties exist which must be taken into account to ensure that the measure chosen matches the objectives. Examples are given showing different interpretations of future diagnostic performance.     

经济周期、经济转型与企业信用风险评估——基于系统性风险的Logistic模型改进

笔者针对传统Logistic模型在信用风险评估时忽略系统性风险的不足,根据我国国情,从经济周期和经济转型两个方面引入系统性风险因子,结合财务因子,构建了新的Logistic模型。笔者采用上市公司的数据对新的Logistic模型进行分行业检验,发现模型具有较好的拟合效果,经济周期和经济转型因子均对企业信用风险有重要影响。新的Logistic模型比传统的Logistic模型具有更高的准确度,能有效提高对企业信用风险的判别能力。     

Over the ROC methodology: Productivity,economic size and firms’ export thresholds

We identify the minimum combinations of productivity and “economic size” that Italian manufacturing firms need to achieve in order to access international markets. These “export thresholds” are estimated by applying, for the first time in economics, the ROC (receiver operating characteristics) methodology. In this way, we detect a model‐based (rather than a subjectively determined) cut‐off that allows to identify exporters and nonexporters and provides a measure of each firm’s distance from the export threshold. This methodology also paves the way to investigate other determinants of thresholds, thus helping to design more effective policy interventions to reduce barriers to trade.     

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.     

Comparison of three‐dimensional ROC surfaces for clustered and correlated markers,with a proteomics application

We propose a non‐parametric test to compare two correlated diagnostic tests for a three‐category classification problem. Our development was motivated by a proteomic study where the objectives are to detect glycan biomarkers for liver cancer and to compare the discrimination ability of various markers. Three distinct disease categories need to be identified from this analysis. We therefore chose to use three‐dimensional receiver operating characteristic (ROC) surfaces and volumes under the ROC surfaces to describe the overall accuracy for different biomarkers. Each marker in this study might include a cluster of similar individual markers and thus was considered as a hierarchically structured sample. Our proposed statistical test incorporated the within‐marker correlation as well as the between‐marker correlation. We derived asymptotic distributions for three‐dimensional ROC surfaces and subsequently implemented bootstrap methods to facilitate the inferences. Simulation and real‐data analysis were included to illustrate our methods. Our distribution‐free test may be simplified for paired and independent two‐sample comparisons as well. Previously, only parametric tests were known for clustered and correlated three‐category ROC analyses.     

Scoring bank loans that may go wrong: a case study

A bank employs logistic regression with state-dependent sample selection to identify loans that may go wrong. The data consist of some 20 000 loans for which a number of conventional accounting ratios of the debtor firm are known; after two years just over 600 have gone wrong. Inspection shows that the state-dependent sampling technique does not work because the data do not satisfy the standard logit model. Several variants on this model are considered, and it is found that a bounded logit with a ceiling of (far) less than 1 fits the data better. When it comes to their performance in an independent data-set, however, the differences between the various methods of analysis are negligible.