存款序列的长记忆性的实证研究

存款是银行评价业绩的一项重要指标,建立高精度的存款模型有利于银行的日常资金管理,能提高银行的资金利用率,降低成本等。本文以国内某国有商业银行储蓄存款和对公存款的月度数据为背景,讨论了存款序列的长记忆性问题,从而对于加深存款性质的认识,及此类时间序列建模具有借鉴意义。     

存款保险的博弈分析及其激励模型

在存款保险市场中,道德风险的产生增加了存款保险机构运行成本.本文运用相关知识建立了双方的博弈模型,讨论了均衡状态下双方的最优博弈策略,并在此基础上建立促使银行选择风险小的投资的激励模型.     

考虑银行破产外部效应的存款保险定价模型

运用存款保险的期望损失定价方法和Shapley值法,建立了考虑银行违约/破产外部效应的存款保险定价模型。模型中度量的破产成本不仅考虑了银行破产清算过程中其自身资产价值的损失,还考虑了银行违约/破产的负外部效应——可能增加其他银行的破产损失,据此确定的存款保险保费反映了各银行对系统总破产成本的边际贡献。为验证模型效果,构造了三种情景进行模拟分析,结果表明:存款保险保费与银行系统对破产银行资产的收购能力负相关,且负相关程度随经济形势的恶化而加剧;保费与整个银行系统参保银行数目之间也呈负相关关系。     

存款保险费率的敏感性分析及实证检验

资本充足率、存款保险费率是发达国家普遍采用的维持银行稳定的基本措施,一般来说资本充足率越高,则所需的存款保险费率越低,银行为减小成本,都希望在合理控制风险的前提下减少所缴纳的存款保险.本文使用根据期权思想建立的存款保险定价模型,推导了存款保险费率对资本充足率的敏感性系数;其次根据中国上市银行的数据进行测算,分别计算了14家银行每增加一个单位的资本充足率可降低存款保险费率的数额,并对实证的结果进行比较;最后给出相关结论.     

基金存储的一个优化模型

研究各期存款(包括购国库券)利率及银行取款政策后发现:每年奖金额均来自于存款的利息;若每笔本金到取款时能获得最大综合本利率,则奖金额就可获得最大值.据此,建立了基金使用模型,设计了一个有效的算法,并通过编程在计算机上实现了这个算法,成功地解决了三种情况下(①只存款不购国库券;②可存款也可购国库券;③第3年的奖金比其他年度多20%)最佳基金存储设计问题,并对每年奖金额不等、年数n任意等都有效.     

基于主成分-SFA的银行投入产出效率评价模型及实证

通过测算贷款、存款等投入要素对净利息收入的贡献,评价商业银行的投入产出效率,对银行的资本运营和监管机构的银行资本监管具有重要意义.原始投入变量过多和变量之间的高度相关都会对评价模型的估计和检验产生影响.创新和特色在于:一是通过提取互不相关的2个主成分,反映6个原始投入变量95%以上的信息.建立基于主成分的SFA模型,克服变量过多和变量高度相关对模型参数估计和检验的影响,解决原始投入变量高度相关导致的系数检验不显著和符号不正确问题.二是利用主成分回归,将主成分与投入变量的关系表达式代入基于主成分的SFA模型,进而确定投入变量的权重系数,建立银行的投入产出模型,反映6个投入变量对净利息收入的影响规律.实证研究结果表明:一是利用主成分建立的SFA模型系数检验显著,技术效率随时间增加.二是利息支出、贷款余额、总资产、存款总额、固定资产和员工人数产出弹性分别为0.287,0.272,0.254,0.086,0.072和0.053.因此影响银行净利息收入的主要因素为利息支出、贷款余额、总资产.存款总额、固定资产和员工人数对净利息收入的影响较小.三是18家商业银行的规模系数为1.025,银行的净利息收入表现出规模经济特征.     

银行存款波动模型的建立及应用

一、本文的目的 存款储蓄工作是银行职能作用的一个重要方面,搞好这一工作可以为银行筹集充足的资金,有计划地利用这些闲散资金向企业发放各项贷款,支持社会主义建设.为此,银行就要准确及时地做好存款计划工作.但是,现阶段银行的存款计划是这样进行的,根据前1～2年的储蓄存款额值,计算出储蓄额平均增减率,据此来计划下一年的储蓄存款.这样做,不能体现存款的固有变化规律,带有一定的盲目性.因此,计划值与实际值相差较大,相对误差有时超过2%,不能很好地指导储蓄工作.鉴此,本文试图以数理统计的方法,根据某县农行近几年来的储蓄存款额数据,见表…     

考虑银行监管资本和破产成本的存款保险定价研究（英文）

本文构建了基于银行监管资本和破产成本的存款保险定价模型.根据理论模型,本文测算了2011年到2017年中国16家上市银行的存款保险费率.结果表明,五大国有银行的存款保险费率要显著低于股份制商业银行和城市商业银行的存款保险费率,但是股份制商业银行的存款保险费率高于城市商业银行的存款保险费率.另外,本文对相关重要参数进行了数值模拟,模拟结果表明,存款保险费率随着监管资本比率和被保险存款比率的提高而下降,随着利率和破产成本的上升而上升.     

存款保险定价、额度与银行业道德风险分析

本文对存款保险定价、存款保险额度与银行业道德风险之间的关系进行了分析。在Merton（1977）的存款保险期权定价模型基础上进行扩展,分析存款保险定价与银行资产风险之间的关系;采用72个国家1983-2003年的面板数据建立计理经济学模型,进一步研究存款保险额度与存款保险定价之间的相关性。     

09-0519(四次修改稿)_陈磊 (存款保险费率的敏感性分析及实证检验) 2010-04-23

资本充足率、存款保险费率是发达国家普遍采用的维持银行稳定的基本措施,一般来说资本充足率越高,则所需的存款保险费率越低,银行为减小成本,都希望在合理控制风险的前提下减少所缴纳的存款保险。本文使用根据期权思想建立的存款保险定价模型,推导了存款保险费率对资本充足率的敏感性系数;其次根据中国上市银行的数据进行测算,分别计算了 14 家银行每增加一个单位的资本充足率可降低存款保险费率的数额,并对实证的结果进行比较;最后给出相关结论     

Modeling company failure: a longitudinal study of Turkish banks

Determining the factors related to the financial failure of a company is important. In this paper, we extend literature on bank failure prediction by modelling bank failures in Turkey from 1998 to 2000 using three statistical models combined with a principal component analysis on financial ratios. The three statistical models employed are a logistic regression, a logistic regression that takes serial correlation into account via generalized estimating equations and a marginalized transition model (MTM). Time and financial ratios that are related with capital adequacy and profitability, risk, non-interest income and Fx assets to Fx liabilities are found to be significant in classifying failed banks. Each of our methods achieves a correct classification rate of 93.3%. Among the three models, MTM, which is the soundest model in terms of statistical assumptions, shows slightly better model fit properties.     

A hybrid intelligent system for predicting bank holding structures

A composite model of neural network and rough sets components was constructed to predict a sample of bank holding patterns. The final model was able to correctly classify 96% of a testing set of four types of bank holding structures. Holding structure is defined as the number of banks under common ownership. For this study, forms of bank holding structure include: banks that are not owned by another company, single banks that are held by another firm, pairs of banks that are held by another enterprise, and three or more banks that are held by another company. Initially, input to the neural network model was 28 financial ratios for more than 200 banks in Arkansas for 1992. The 28 ratios are organized by categories such as liquidity, credit risk, leverage, efficiency, and profitability. The ratios were constructed with 70 bank variables such as net worth, deposits, total assets, net loans, total operating income, etc. The first neural network model correctly classified 84% of the testing set at a tolerance level of 0.20. Another artificial intelligence (AI) procedure known as two-dimensional rough sets was then applied to the dataset. Rough sets reduced the number of input variables from 28 to 18, a drop of 36% in the number of input variables. This version of rough sets also eliminated a number of records, thereby reducing the information system (i.e., matrix) on both vertical and horizontal dimensions. A second neural network was trained with the reduced number of input variables and records. This network correctly classified 96% of the testing set at a tolerance level of 0.20, an increase of 11% in the accuracy of the prediction. By applying two-dimensional reducts to the dataset of financial ratios, the predictive accuracy of the neural network model was improved substantially. Banking institutions that are prime candidates for mergers or acquisitions can then be more accurately identified through the use of this hybrid decision support system (DSS) which combines different types of AI techniques for the purposes of data management and modeling.     

基于平滑扩充原理的商业银行信用风险评级模型及实证

本文通过银行的资产质量方面、资本充足率方面、管控效能层面、盈利状态层面、流动性层面与社会敏感度层面等构建商业银行信用风险评价体系。根据平滑扩充原理模拟生成大样本数据,对评级得分进行扩充,进而根据扩充后的大样本数据划分银行的信用风险等级。解决了由于样本少、无法对信用等级合理划分的难题。通过实证分析可以了解到,本文得出的银行评级信息和标准普尔提供的评价结论存在共同的序关系状态。因此,可根据本模型对大多数未经过国际权威机构评级的银行进行风险评级。     

Measuring efficiency at U.S. banks: Accounting for heterogeneity is important

Estimates of bank cost efficiency can be biased if bank heterogeneity is ignored. I compare X-inefficiency derived from a model constraining the cost frontier to be the same for all banks in the U.S. and a model allowing for different frontiers and error terms across Federal Reserve Districts. I find that the data reject the single cost function model; X-inefficiency measures based on the single cost function model are, on average, higher than those based on the separate cost functions model; the distributions of the one-sided error terms are wider for the single cost function model than for the separate cost functions model; and the ranking of Districts by the level of X-inefficiency differs in the two models. The results suggest it is important when studying X-inefficiency to account for differences across the markets in which banks are operating and that since X-inefficiency is, by construction, a residual, it will be particulary sensitive to omissions in the basic model.     

基于零膨胀模型的我国银企关系规模影响因素研究

银企关系是学术界和实务界关注的焦点之一,然而,国内学者鲜有探讨银企关系数量的影响因素。本文使用我国A股上市公司2006-2013年的银企关系计数资料,利用零膨胀模型对企业建立银企关系规模的影响因素进行了分析。研究发现:规模大、资产负债率高、获利能力强的公司倾向于建立更多的银企关系;企业的长期负债率、第一大股东持股比例,是否是国有产权属性和企业的经营风险与银企关系的规模(数量)显著负相关;信贷合约的期限和信贷金额与银企关系的数量显著正相关;进一步比较了零膨胀模型与Poisson回归、负二项分布回归模型等计数模型,统计检验显示,零膨胀模型比较适合零值过多和过度离散的数据结构资料。     

Multicriteria approaches for ranking of efficient units in DEA models

Data envelopment analysis models usually split decision making units into two basic groups, efficient and inefficient. Efficiency score of inefficient units allows their ranking but efficient units cannot be ranked directly because of their maximum efficiency. That is why there are formulated several models for ranking of efficient units. The paper presents two original models for ranking of efficient units in data envelopment analysis—they are based on multiple criteria decision making techniques—goal programming and analytic hierarchy process. The first model uses goal programming methodology and minimizes either the sum of undesirable deviations or maximal undesirable deviation from the efficient frontier. The second approach is analytic hierarchy process model for ranking of efficient units. The two presented models are compared with several super-efficiency models and other approaches for ranking decision making units in DEA models including definitions based on distances from optimistic and pessimistic envelopes and cross efficiency evaluation models. The results of the analysis by all presented models are illustrated on a real data set—evaluation of 194 bank branches of one of the Czech commercial banks.     

银行网点规模对理财产品市场竞争的影响

网点是银行销售理财产品的主要渠道,网点的规模不仅直接影响着银行在理财产品市场的份额和收入,还通过风险行为影响着理财产品市场的竞争策略与格局。本文以权益类理财产品为研究对象,建立了银行间基于网点的理财产品竞争模型,通过竞争均衡的比较静态分析,发现理财产品的预期收益率受不同发行主体自身网点规模的影响,网点规模小的银行倾向于采取更激进的竞争策略,提供收益率更高的理财产品,以扩展自己的生存空间,即存在收益率溢价现象。然而,小规模银行的风险行为对竞争格局的影响有限,而大规模银行的行为却影响显著,即,理财产品所投资标的资产的风险水平对竞争的影响程度也同样受制于银行的网点规模。本文的分析和结论为我国银行理财产品市场的现状特征提供了理论解释。     

Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case

The objective of this paper is to propose a methodological framework for constructing the integrated early warning system (IEWS) that can be used as a decision support tool in bank examination and supervision process for detection of banks, which are experiencing serious problems. Sample and variable set of the study contains 40 privately owned Turkish commercial banks (21 banks failed during the period 1997–2003) and their financial ratios. Well known multivariate statistical technique (principal component analysis), was used to explore the basic financial characteristics of the banks, and discriminant, logit and probit models were estimated based on these characteristics to construct IEWS. Also, importance of early warning systems in bank examination was evaluated with respect to cost of failure. Results of the study show that, if IEWS was effectively employed in bank supervision, it can be possible to avoid from the bank restructuring costs at a significant amount of rate in the long run.     

改进型LOBNN & AR-GARCH模型在股票预测中的应用

为实现对股票价格的短期预测,本文在Laguerre正交基神经网络 (LOBNN)模型的基础上,提出了一种新的组合预测模型来预测短期股价的变化。该模型先通过改进LOBNN模型的权值求解算法,用以增强模型的通用性。接着在其基础上设计新的迭代算法,进一步提高模型的预测精度,进而得到新的LOBNN模型。之后将股价数据分别代入AR-GARCH模型和改进后的LOBNN模型,得到输入数据的两组预测值。最后通过不同的权重来组合两种预测结果,生成最终股价的预测结果。文末的仿真结果表明该组合模型在预测精度与通用性上较原始模型有较大的提升,是一种高效的预测模型。     

上市公司财务预警的BP神经网络模型的建立及其应用

将BP神经网络方法应用于上市公司的财务预警上,构建了上市公司财务预警模型,不仅能发现企业是否存在风险和企业经营是否偏离轨道,向经营者提出警示,以便及时采取相应管理对策,而且还为广大的投资者和银行在内的债权人判定上市公司质量和经营业绩提供科学的手段和可靠的依据.实例分析表明该模型有效、可行,为上市公司财务预警提供了新的途径.