地区收入差距的形成机理及实证

改革开放以来,我国地区收入差距不断扩大,成为一个突出的社会经济问题。文章从地区间经济实力差异、经济结构差异、基础设施条件差异、人力资本水平差异、对外开放水平差异、制度性差异等方面分析了地区收入差距形成机理,构建了地区收入差距paneldata模型,以浙江省为例,对地区收入差距形成机理进行实证研究。研究结果表明：地区间收入差距总体上呈上升趋势;地区人均GDP差异、城市化水平差异、人力资本差异对人均收入较低的地区收入差距具有显著的正向影响,对人均收入较高的地区收入差距影响不显著;基础设施条件差异对人均收入较低的地区收入差距具有正向影响,对人均收入较高的地区收入差距具有负向影响;而地区人均外商投资差异对人均收入较高的地区收入差距具有正向影响,对人均收入较低的地区收入差距具有负向影响;地区对外贸易依存度差异、民营企业效率对地区收入差距具有显著的正向影响。针对实证结果文章提出了相应的政策建议。     

浅议自然人资质信用评价

信用卡与银行卡 信用卡按是否向发卡银行交存备用金分为贷记卡、准贷记卡两类。贷记卡是指发卡银行给予持卡人一定的信用额度，持卡人可在信用额度内先消费、后还款的信用卡。准贷记卡是指持卡人须先按发卡银行要求交存一定金额的备用金，当备用金账户余额不足支付时，可在发卡银行规定的信用额度内透支的信用卡。 据国家统计局权威计报告显示，我国GDP近年来增速在7%以上。但是，近几年出现了一定程度的通货紧缩，急需扩大内需来为经济发展增强活力。 可是，我国的资本市场又极其不健全，尤其是金融信贷活动(特别是个人消费信贷…     

对信用卡风险管理的探讨

随着我国金融业的发展,信用卡产业也日渐走上正轨。但是由于信用卡存在一定风险,对信用卡风险的管理成为发卡机构首先要解决的问题。一、信用卡风险的类型及表现1.信用风险。因持卡人不能依约偿还本息的风险。发卡机构向客户发放信用卡的时候主要依据客户当时的经济状况和信誉状况,然而客户的具体情况是一个动态的过程,如果客户职业、收入等发生变动,经济状况恶化,无力还款,那么将引发信用风险。2.诈骗性风险。诈骗有真卡诈骗和伪卡诈骗两种。真卡诈骗大多是由于银行管理制度的问题造成的。伪卡诈骗是一种主要的信用卡犯罪方式,通常是犯罪团…     

经济地位、主观社会地位与居民自感健康

本文采用中国劳动力动态调查（CLDS）2012年首次全面调查数据,从经济地位和主观社会地位角度出发,并依次控制人际关系及情感因素、人口学特征和自然因素与特殊嗜好因素,系统考察其对我国居民自感健康的影响。结果发现,代表较高经济收入的社会经济地位因素对居民自感健康有积极影响;控制客观经济地位因素后,主观社会地位对各年龄层的居民自感健康的影响依然相当显著,当前社会等级和预期未来高的社会等级都能显著提高居民自感健康水平。     

银行信贷资产质量为何较低

银行信贷资产质量为何较低□文／￥￥南京市工商银行张同信当前，国有商业银行信贷资产质量较低．逾期呆滞贷款比例较高，严重地影响了国有商业银行信贷资产的正常运转。我国国有商业银行信贷资产质量较低，原因是多方面的，有外部原因，也有内部原因，有管理体制上的原因...     

城镇化对城乡收入差距的门槛效应分析

文章基于目前中国省级面板数据,运用门槛模型就我国城镇化对城乡收入差距的影响进行了研究,结果发现:(1)城镇化对城乡收入差距存在非线性特征,两个门槛值分别为0.312和0.471.(2)库兹涅茨“倒U型假说”在中国并不成立,只对处于特定城镇化水平的省份才成立.(3)随着城镇化水平的提高,政府通过干预经济和扶持农村来缩小城镇收入差距的作用不显著,且对处于较高城镇化水平的省份还起到了扩大收入差距的作用.     

中国省域信贷风险判别分析

现阶段，商业银行信贷仍是我国社会资金配置的主要方式。出于盈利和风险考虑，商业银行信贷行为天然具有顺周期特征。为实现稳增长目标，政府更倾向于逆周期调节。受到地方财政收支状况影响，省级地方政府会采取不同方式、不同程度地干预省域资金配置。十九大报告明确指出，要健全货币政策和宏观审慎政策双支柱调控框架。因此，省域信贷风险判别是一个动态过程，需在经济周期与宏观审慎政策框架下整体考虑。在此背景下，本文基于新古典经济学分析框架，建立了2008年以来省域信贷风险识别模型，研究发现，第一，地方财政支出收入比与不良贷款率存在正向影响关系，资本回报率与不良贷款率存在负向影响关系，且地方财政支出收入比对不良贷款率的影响程度更大；第二，依据分类准则，属于信贷高风险的省域分别是：河南，海南，重庆，四川，贵州，云南，陕西，甘肃，青海，宁夏，新疆，西藏；第三，在地方财政支出收入比、资本回报率的显著作用影响下，我国各省域不良贷款率呈现U型变化，不良贷款率阈值为1.49%，即当不良贷款率大于1.49%时，省域贷款风险较高；第四，当我国资本回报率处于企稳阶段，不良贷款率处于低于阈值的谷底阶段，且省域间风险差异性较小。当我国资本回报率处于下行阶段时，不良贷款率上升至阈值线以上，且省域间风险差异性较大。     

非线性定价、家庭人口特征和收入再分配效应调整

本文以分时阶梯定价为例,在人口老龄化及生育政策逐渐放宽的背景下,通过建立结构计量模型,实证分析了引入家庭人口特征后,非线性定价对收入再分配效应这一政策目标的影响。通过构建引入家庭人口特征的二次近乎理想需求（QUAIDS）函数,基于相对等价补偿方法建立收入再分配效应调整的测度模型。估计了为保持相同效用增加家庭不同类型的人口需要增加的电费补偿率及其金额。研究结论表明,引入家庭人口特征后收入再分配效应得到了强化;家庭人口特征对消费者行为选择有显著影响;不同人口规模的家庭的电费补贴结构存在明显差异;动态分析显示,随着预算水平的提高,家庭人口特征对收入再分配效应的影响在减小。     

我国居民收入与储蓄的分析研究

把握居民储蓄行为及其变动规律，是国家有关决策部门亟待研究的问题。一般认为，居民储蓄是居民可支配收入用于消费后的剩余部分，因此，居民可支配收入是影响居民储蓄行为的重要因素之一。本文拟考察我国居民收入对居民储蓄的影响，以便人们对收人与储蓄的关系有更清楚的认识。一、我国居民收入与储蓄的现状分析由于我国尚处于社会主义初期阶段，因此居民的储蓄倾向较低。从表1可以看出，1983－1994年我国居民的平均储蓄倾向为221％。但随着收入的稳步上升，其储蓄倾向也呈现出平稳上升的态势，由1983年的12．9％上升到1994年的36．0％。与…     

我国P2P平台网络借贷逾期行为和羊群行为研究

通过对P2P网贷平台人人贷数据的持续跟踪与抓取,对参与的借款人-平台-出借人的借贷逾期行为和羊群行为进行定量分析.研究发现,对逾期行为造成显著性影响的变量有借款金额、借款期限、借款利率、借款人性别、借款人收入、借款人受教育程度、借款人有无房贷、借款人婚姻状况、借款人信用等级、借款人已偿清比率、借款人成功借款比率等.P2P网贷存在显著的羊群行为.     

The neglog transformation and quantile regression for the analysis of a large credit scoring database

Summary.  A statistical analysis of a bank's credit card database is presented. The database is a snapshot of accounts whose holders have missed a payment on a given month but who do not subsequently default. The variables on which there is information are observable measures on the account (such as profit and activity), and whether actions that are available to the bank (such as letters and telephone calls) have been taken. A primary objective for the bank is to gain insight into the effect that collections activity has on on-going account usage. A neglog transformation that highlights features that are hidden on the original scale and improves the joint distribution of the covariates is introduced. Quantile regression, a novel methodology to the credit scoring industry, is used as it is relatively assumption free, and it is suspected that different relationships may be manifest in different parts of the response distribution. The large size is handled by selecting relatively small subsamples for training and then building empirical distributions from repeated samples for validation. In the application to the database of clients who have missed a single payment a substantive finding is that the predictor of the median of the target variable contains different variables from those of the predictor of the 30% quantile. This suggests that different mechanisms may be at play in different parts of the distribution.     

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。     

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。     

商业银行信用风险分析的主要技术

 加入WTO以后，国内商业银行面临着严峻的考验。大量的信贷资产质量低下，贷款不良率仍然很高。从1999年开始，国家对四大国有商业银行不良资产实行了剥离政策。但即便如此，中国建设银行、中国商业银行和中国银行不良贷款率仍比人民银行规定的15%的控制水平要高。剥离不良资产后，信贷资产质量仍然较差。这说明政策性的剥离不良资产措施只能解决不良资产的存在问题。要从本质上改善信贷资产的质量，商业银行就必须从自身的信贷风险管理的角度来采取强有力的措施。本文综述了国内外商业银行信用风险管理的技术，旨在为商业银行信用风险管理的研究提供借鉴。     

Variable selection in proportional hazards cure model with time-varying covariates,application to US bank failures*

From a survival analysis perspective, bank failure data are often characterized by small default rates and heavy censoring. This empirical evidence can be explained by the existence of a subpopulation of banks likely immune from bankruptcy. In this regard, we use a mixture cure model to separate the factors with an influence on the susceptibility to default from the ones affecting the survival time of susceptible banks. In this paper, we extend a semi-parametric proportional hazards cure model to time-varying covariates and we propose a variable selection technique based on its penalized likelihood. By means of a simulation study, we show how this technique performs reasonably well. Finally, we illustrate an application to commercial bank failures in the United States over the period 2006–2016.     

Model-based measurement of latent risk in time series with applications

Summary.  Risk is at the centre of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning countermeasures, the risk and severity of counterparty default concerns bank risk managers daily and the risk of infection has actuarial and epidemiological consequences. However, risk cannot be observed directly and it usually varies over time. We introduce a general multivariate time series model for the analysis of risk based on latent processes for the exposure to an event, the risk of that event occurring and the severity of the event. Linear state space methods can be used for the statistical treatment of the model. The new framework is illustrated for time series of insurance claims, credit card purchases and road safety. It is shown that the general methodology can be effectively used in the assessment of risk.     

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.     

Too Connected to Fail? Inferring Network Ties From Price Co-Movements

We use extreme value theory methods to infer conventionally unobservable connections between financial institutions from joint extreme movements in credit default swap spreads and equity returns. Estimated pairwise co-crash probabilities identify significant connections among up to 186 financial institutions prior to the crisis of 2007/2008. Financial institutions that were very central prior to the crisis were more likely to be bailed out during the crisis or receive the status of systemically important institutions. This result remains intact also after controlling for indicators of too-big-to-fail concerns, systemic, systematic, and idiosyncratic risks. Both credit default swap (CDS)-based and equity-based connections are significant predictors of bailouts. Supplementary materials for this article are available online.     

Quantification of model risk that is caused by model misspecification

In this paper, we suggest a technique to quantify model risk, particularly model misspecification for binary response regression problems found in financial risk management, such as in credit risk modelling. We choose the probability of default model as one instance of many other credit risk models that may be misspecified in a financial institution. By way of illustrating the model misspecification for probability of default, we carry out quantification of two specific statistical predictive response techniques, namely the binary logistic regression and complementary log–log. The maximum likelihood estimation technique is employed for parameter estimation. The statistical inference, precisely the goodness of fit and model performance measurements, are assessed. Using the simulation dataset and Taiwan credit card default dataset, our finding reveals that with the same sample size and very small simulation iterations, the two techniques produce similar goodness-of-fit results but completely different performance measures. However, when the iterations increase, the binary logistic regression technique for balanced dataset reveals prominent goodness of fit and performance measures as opposed to the complementary log–log technique for both simulated and real datasets.