Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.     

基于整合治愈率模型的信贷违约时点预测

传统信用评分方法主要利用统计分类方法,只能预测借款人是否会发生违约,但不能预测违约发生的时点。治愈率模型是二分类和生存分析的混合模型,不仅可以预测是否会发生违约,而且可以预测违约发生的时点,比传统二分类方法可以提供更多的信息。另外,随着大数据的发展,数据源越来越多,针对相同或者相似任务,可以收集到多个数据集,本文提出了融合多源数据的整合治愈率模型,可以对多个数据集同时建模和估计参数,通过复合惩罚函数进行组间和组内双层变量选择,并通过促进两个子模型回归系数符号相同,提高模型的可解释性。通过数值模拟发现,所提方法在变量选择和参数估计上均有明显优势。最后,将所提方法应用于信用贷款的违约时点预测中,模型表现良好。     

市场竞争与银行流动性创造——基于分支机构的银行竞争指标构建

市场竞争对于金融稳定是一把“双刃剑”,监管部门为维持银行业稳定需要考虑竞争政策的平衡性问题。充足的流动性创造有助于为经济活动提供资金,促进经济主体之间的交易,而过剩或 缺乏流动性均会迫使银行业陷入困境甚至引发流动性危机。本文使用2003—2017年我国176家商业银行数据,构建了一种新的基于分支机构和引力模型的银行竞争指标,来识别市场竞争与银行流动性创造之间的关系。结果发现,市场竞争对银行流动性创造具有促进作用,其中对银行资产端流动性创造的正向影响较大,对负债端流动性创造的影响较小,对资产负债表表外流动性创造没有影响。同时,受到正向影响的主体是城市商业银行、农村商业银行等地方性银行和外资银行,对国有大型银行、股份制银行等全国性银行则没有影响或具有负向影响。上市银行、系统重要性银行和处在危机时期、经济繁荣时期或货币政策宽松时期的银行,随着市场竞争加剧均会创造更多流动性。中介效应分析发现,市场竞争通过刺激金融创新、提高经营效率和扩大流动性信贷等中介机制促进了银行流动性创造。本文不仅丰富了银行竞争策略的经济效果分析,也为银行流动性创造研究提供了重要补充。     

Knight不确定环境下中国上市银行存款保险定价

提出Knight不确定环境下的银行存款保险定价模型,在该模型下存款保费率不再是一个固定的值,而是一个区间。运用该模型真实测算了中国16家A股上市银行的存款保险费率区间,并利用数值分析的方法,研究不确定性参数对存款保费率区间的重要影响。结果表明:Knight不确定风险对中国银行保费的厘定影响显著,具体表现为随着不确定参数的增大,各银行保险费率区间长度都有增大的趋势,但增大幅度各不相同,因此在进行保费厘定时,不能一概而论,而要"因行而异"。     

Joint analysis of longitudinal measurements and survival times with a cure fraction based on partly linear mixed and semiparametric cure models

In a joint analysis of longitudinal quality of life (QoL) scores and relapse-free survival (RFS) times from a clinical trial on early breast cancer conducted by the Canadian Cancer Trials Group, we observed a complicated trajectory of QoL scores and existence of long-term survivors. Motivated by this observation, we proposed in this paper a flexible joint model for the longitudinal measurements and survival times. A partly linear mixed effect model is used to capture the complicated but smooth trajectory of longitudinal measurements and approximated by B-splines and a semiparametric mixture cure model with the B-spline baseline hazard to model survival times with a cure fraction. These two models are linked by shared random effects to explore the dependence between longitudinal measurements and survival times. A semiparametric inference procedure with an EM algorithm is proposed to estimate the parameters in the joint model. The performance of proposed procedures are evaluated by simulation studies and through the application to the analysis of data from the clinical trial which motivated this research.     

中国上市银行特许权价值和银行稳健性关系的实证研究

特许权价值等于银行在未来持续从事经营活动所得到的超额利润的现值,它的自律效应能够促进银行的稳健经营。在对特许权价值和银行稳健性关系的形成机理进行分析的基础上,构建特许权价值和银行稳健性的计量经济模型,并采用14家上市银行2000-2010年的非平衡面板数据进行实证检验。研究发现,银行的特许权价值越高,对银行的稳健性的促进作用越明显;隐性保险制度削弱了特许权价值对银行稳健的影响;银行规模越大稳健性越好;次贷危机影响了银行的稳健性。     

违约风险、房地产贷款市场博弈与房地产价格

本文从事后激励的角度，构建了一个关于房地产个人贷款违约与银行反应策略的博弈模型，对中国房地产价格下跌的诱发机制以及家庭和银行的最优决策进行了理论分析。在此基础上选择35个大中城市作为研究样本，利用面板数据回归模型对相关理论推论进行了实证检验。理论推演和实证研究表明，家庭收入下降和房地产贷款违约是诱发房地产价格下跌的关键因素。提高购房首付比，降低房地产贷款价值比以及保持房地产贷款市场结构的适度集中，既可以抑制房地产价格过快上涨，也可以预防房地产价格发生暴跌风险。当房地产贷款出现违约时，为了避免房地产价格进入下降螺旋，银行的最优策略不是取消房地产抵押品的赎回权，而是采取积极的信贷刺激措施以稳住房地产价格。贷款市场份额占比越高的银行越有激励这样做。     

COM–Poisson cure rate survival models and an application to a cutaneous melanoma data

In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow the Conway–Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discussed in the literature. Next, we discuss the maximum likelihood estimation of the parameters of this cure rate survival model. Finally, we illustrate the usefulness of this model by applying it to a real cutaneous melanoma data.     

中国银行业结构特征对银行信贷渠道的影响

选取中国57家商业银行1998—2010年的面板数据,建立动态面板模型,运用广义矩估计方法就中国银行业结构特征对货币政策银行信贷渠道的影响进行实证检验,着重考察中国银行业资本充足率、资本规模和流动性等结构特征对货币政策银行信贷传导渠道的影响效应。研究表明：在这三个结构特征指标中,资本充足率对货币政策传导的有着显著且最强烈的影响,其次为规模特征,最后是流动性。同时发现在资本管制下的外资银行并没有表现出与中资银行在货币政策传导效果上的差异。     

Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model

A pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of the event. The most widely used model to estimate the probability of default is the logistic regression model. Since the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks, for example, underestimation of the default probability, which could be very risky for banks. In order to overcome these drawbacks, we propose the generalized extreme value regression model. In particular, in a generalized linear model (GLM) with the binary-dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure used is the maximum-likelihood method. This model accommodates skewness and it presents a generalisation of GLMs with complementary log–log link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to empirical data on Italian small and medium enterprises.     

经济地位对信用卡逾期的影响

我国信用卡业务的迅猛发展助推了消费经济的快速发展，但信用卡的逾期行为不容忽视。收入代表了一个人的经济地位，是信用卡按时还款的保障。本文基于某商业银行信用卡客户的逾期数据，以持卡人的经济地位为视角，分析了经济地位对信用卡逾期行为的影响。研究结果表明，我国商业银行信用卡持卡人的逾期行为具有显著的经济特征，收入对信用卡逾期的影响呈“U”型的非线性特征，即收入较低和收入较高的持卡人逾期的可能性较高，收入处于中间的持卡人逾期的可能性较低。进一步的研究发现，中年群体、工作单位稳定者、有房者会降低经济地位对信用卡逾期行为的非线性影响，而账龄较长的持卡人提升了这种影响。本文的研究为全社会建立良好的信用卡用卡环境，商业银行高效处理信用卡逾期，改进和完善商业银行信用卡风险管理提供了关键证据。     

On Loss Distributions from Installment-Repaid Loans

The banks have been accumulating huge data bases for many years and are increasingly turning to statistics to provide insight into customer behaviour, among other things. Credit risk is an important issue and certain stochastic models have been developed in recent years to describe and predict loan default. Two of the major models currently used in the industry are considered here, and various ways of extending their application to the case where a loan is repaid in installments are explored. The aspect of interest is the probability distribution of the total loss due to repayment default at some time. Thus, the loss distribution is determined by the distribution of times to default, here regarded as a discrete-time survival distribution. In particular, the probabilities of large losses are to be assessed for insurance purposes.     

A flexible model for survival data with a cure rate: a Bayesian approach

In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real data set.     

基于行业冲击因子矩阵的宏观压力测试方法研究

目前,考虑行业违约相关的宏观压力测试方法较少,而两种主流方法风险传导过程都存在明显不足。因此,从理论上提出一个新的压力传导模型,通过冲击因子矩阵使偏离平均值的行业违约率与宏观经济冲击因子联系起来,将违约的顺周期性和行业违约相关性纳入统一框架内;在技术上避免了分行业多元线性回归方程,使分行业压力测试过程简易可行。实证结果表明:该方法能合理刻画宏观经济冲击对各行业违约率的影响,商业银行为提高抵御系统性风险的能力,可降低顺周期性强的行业贷款占比,或者调整贷款行业结构以避免行业集中度过高。     

Destructive weighted Poisson cure rate models

In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow a compound weighted Poisson distribution. This model is more flexible in terms of dispersion than the promotion time cure model. Moreover, it gives an interesting and realistic interpretation of the biological mechanism of the occurrence of event of interest as it includes a destructive process of the initial risk factors in a competitive scenario. In other words, what is recorded is only from the undamaged portion of the original number of risk factors.     

Semiparametric Estimation in Transformation Models with Cure Fraction

Semiparametric transformation model has been extensively investigated in the literature. The model, however, has little dealt with survival data with cure fraction. In this article, we consider a class of semi-parametric transformation models, where an unknown transformation of the survival times with cure fraction is assumed to be linearly related to the covariates and the error distributions are parametrically specified by an extreme value distribution with unknown parameters. Estimators for the coefficients of covariates are obtained from pseudo Z-estimator procedures allowing censored observations. We show that the estimators are consistent and asymptotically normal. The bootstrap estimation of the variances of the estimators is also investigated.     

The Geometric Birnbaum-Saunders regression model with cure rate

In this paper, we propose a cure rate survival model by assuming the number of competing causes of the event of interest follows the Geometric distribution and the time to event follow a Birnbaum Saunders distribution. We consider a frequentist analysis for parameter estimation of a Geometric Birnbaum Saunders model with cure rate. Finally, to analyze a data set from the medical area.     

Modeling categorical covariates for lifetime data in the presence of cure fraction by Bayesian partition structures

In this paper, we propose a Bayesian partition modeling for lifetime data in the presence of a cure fraction by considering a local structure generated by a tessellation which depends on covariates. In this modeling we include information of nominal qualitative variables with more than two categories or ordinal qualitative variables. The proposed modeling is based on a promotion time cure model structure but assuming that the number of competing causes follows a geometric distribution. It is an alternative modeling strategy to the conventional survival regression modeling generally used for modeling lifetime data in the presence of a cure fraction, which models the cure fraction through a (generalized) linear model of the covariates. An advantage of our approach is its ability to capture the effects of covariates in a local structure. The flexibility of having a local structure is crucial to capture local effects and features of the data. The modeling is illustrated on two real melanoma data sets.     

Latent cure rate model under repair system and threshold effect

In this paper, we formulate a simple latent cure rate model with repair mechanism for a cell exposed to radiation. This latent approach is a flexible alternative to the models proposed by Klebanov et al. [A stochastic model of radiation carcinogenesis: latent time distributions and their properties. Math Biosci. 1993;18:51–75], Kim et al. [A new threshold regression model for survival data with a cure fraction. Lifetime Data Anal. 2011;17:101–122], and is along the lines of the destructive cure rate model formulated recently by Rodrigues et al. [Destructive weighted Poisson cure rate model. Lifetime Data Anal. 2011b;17:333–346]. A new version of the modified Gompertz model and the promotion cure rate model that takes into account the first passage time of reaching the critical point are discussed, and the estimation of tumor size at detection is then addressed from the Bayesian viewpoint. In addition, a simulation study and an application to real data set illustrate the usefulness of the proposed cure rate model.     

The negative binomial–beta Weibull regression model to predict the cure of prostate cancer

In this article, for the first time, we propose the negative binomial–beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy.