我国商业银行流动性风险压力测试

商业银行流动性风险在2007年金融危机之后越来越受国际银行业的重视。我国自2009年3月正式加入巴塞尔银行监管委员会后,积极推进新的风险监管标准。流动性风险压力测试是一种前瞻性的定量分析方法,它能预测极端经济情景下,商业银行的流动性风险承受能力,并做出针对性的经营政策调整,预防流动性风险。虽然众多金融机构在金融危机后开始重视此测试,由于起步太晚,测试中在因子选择,模型构建和数据的完整性上与国际水平有一定差距。通过商业银行流动性风险压力测试的实证分析,提出了相应的政策建议。     

A Hybrid Geometric Phase II/III Clinical Trial Design based on Treatment Failure Time and Toxicity

The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial.     

Prediction of accrual closure date in multi-center clinical trials with discrete-time Poisson process models

In a phase III multi‐center cancer clinical trial or a large public health study, sample size is predetermined to achieve desired power, and study participants are enrolled from tens or hundreds of participating institutions. As the accrual is closing to the target size, the coordinating data center needs to project the accrual closure date on the basis of the observed accrual pattern and notify the participating sites several weeks in advance. In the past, projections were simply based on some crude assessment, and conservative measures were incorporated in order to achieve the target accrual size. This approach often resulted in excessive accrual size and subsequently unnecessary financial burden on the study sponsors. Here we proposed a discrete‐time Poisson process‐based method to estimate the accrual rate at time of projection and subsequently the trial closure date. To ensure that target size would be reached with high confidence, we also proposed a conservative method for the closure date projection. The proposed method was illustrated through the analysis of the accrual data of the National Surgical Adjuvant Breast and Bowel Project trial B‐38. The results showed that application of the proposed method could help to save considerable amount of expenditure in patient management without compromising the accrual goal in multi‐center clinical trials. Copyright © 2012 John Wiley & Sons, Ltd.     

Psychometric Testing of the FACES III with Rural Adolescents

This study tests the validity and reliability of the Family Adaptability and Cohesion Scale III (FACES III) in two samples of rural adolescents. The underlying theory is the linear 3-D circumplex model. The FACES III was administered to 1,632 adolescents in Grades 7 through 12 in two counties in a rural western state. The FACES III Scale and the Cohesion Scale demonstrated high internal consistency, although the Cohesion Scale was more consistently predictive of self-esteem and higher in internal consistency than was the Adaptability Scale. Findings confirmed previous methodological critiques regarding linearity, the correlation of factors, and internal consistency. The presence of two orthogonal factors was consistent with the original 3-D circumplex model, but items from the Adaptability Scale relating to changing of rules for children's roles and egalitarianism tended to be problematic.     

新巴塞尔协议中信用风险管理IRB法解析

为研究商业银行的信用风险管理问题,对新巴塞尔协议中信用风险管理IRB法进行系统分析,概括其基本框架,并通过详细解析其设定的4种风险要素函数来解释目前我国商业银行在运用各种信用风险管理模型时存在“黑箱”的原因,同时,探讨该方法与各种信用风险管理模型的兼容关系,通过解析信用风险管理模型中各种风险要素函数设定的内在逻辑关系,总结目前我国商业银行应用IRB法时存在的各种数据估计上的困难,从而得出应对各种困难的对策。     

Effects of Organizational Environment,Internal Structure,and Team Climate on the Effectiveness of Local Emergency Planning Committees1

This study proposed and tested a model of factors influencing the effectiveness of Local Emergency Planning Committees (LEPCs). Data were collected from the chairs of Michigan LEPCs, who were asked to provide information about their LEPCs’staffing, activities, resources, and team climate, as well as information about community hazards and relationships with other organizations in their communities. The model was partially supported. As hypothesized, team climate substantially mediated the relationships between an LEPCs effectiveness and many of the attributes of its internal structure and external environment. In addition, three (emergency planning resources, community support and LEPC staffing and structure) out of four categories of variables hypothesized to influence team climate were, in fact directly related to it. There were two major inconsistencies with the model. First, two types of emergency planning resources were found to have a direct influence on LEPC effectiveness rather than an indirect effect mediated by team climate. In addition, hazard vulnerability was hypothesized to have a direct effect on team climate, but the data showed this relationship was mediated by community support and by LEPC staffing and structure. A revised model was proposed that suggests ways in which members can change the structure of an LEPC and strategies to enhance its effectiveness.     

Estimating prediction error for complex samples

With a growing interest in using non-representative samples to train prediction models for numerous outcomes it is necessary to account for the sampling design that gives rise to the data in order to assess the generalized predictive utility of a proposed prediction rule. After learning a prediction rule based on a non-uniform sample, it is of interest to estimate the rule's error rate when applied to unobserved members of the population. Efron (1986) proposed a general class of covariance penalty inflated prediction error estimators that assume the available training data are representative of the target population for which the prediction rule is to be applied. We extend Efron's estimator to the complex sample context by incorporating Horvitz–Thompson sampling weights and show that it is consistent for the true generalization error rate when applied to the underlying superpopulation. The resulting Horvitz–Thompson–Efron estimator is equivalent to dAIC, a recent extension of Akaike's information criteria to survey sampling data, but is more widely applicable. The proposed methodology is assessed with simulations and is applied to models predicting renal function obtained from the large-scale National Health and Nutrition Examination Study survey. The Canadian Journal of Statistics 48: 204–221; 2020 © 2019 Statistical Society of Canada     

A pooled Bayes test of independence for sparse contingency tables from small areas

We study the association between bone mineral density (BMD) and body mass index (BMI) when contingency tables are constructed from the several U.S. counties, where BMD has three levels (normal, osteopenia and osteoporosis) and BMI has four levels (underweight, normal, overweight and obese). We use the Bayes factor (posterior odds divided by prior odds or equivalently the ratio of the marginal likelihoods) to construct the new test. Like the chi-squared test and Fisher's exact test, we have a direct Bayes test which is a standard test using data from each county. In our main contribution, for each county techniques of small area estimation are used to borrow strength across counties and a pooled test of independence of BMD and BMI is obtained using a hierarchical Bayesian model. Our pooled Bayes test is computed by performing a Monte Carlo integration using random samples rather than Gibbs samples. We have seen important differences among the pooled Bayes test, direct Bayes test and the Cressie-Read test that allows for some degree of sparseness, when the degree of evidence against independence is studied. As expected, we also found that the direct Bayes test is sensitive to the prior specifications but the pooled Bayes test is not so sensitive. Moreover, the pooled Bayes test has competitive power properties, and it is superior when the cell counts are small to moderate.