浅议佳木斯市农村信用社资本管理

本文分析了辖区农村信用社资本结构以及资本管理现状,同时对资本管理现状产生的原因进行了分析,在此基础上,提出完善资本管理的对策.     

论应收账款的催收管理

应收帐款是企业为了稳定销售渠道，面向客户提供信用业务所累积的款项。它是企业顺利运营的前提，但也增加了企业的经营风险。本文首先通过对应收账款成因的分析，提出了企业应怎样加强应收账款的管理；接着阐述了企业应如何根据自己的实际情况制定良好的信用政策；最后详细介绍了应收账款的催收方案、方法和技巧，以及坏账的预防措施。     

平衡计分法在企业信用评级中的应用

基于平衡计分卡基本原理、现代信用理论和财务分析理论,从被评企业战略层面出发,对被评企业从财务、客户、内部运营和学习成长四个方面开展全方位的分析评判,设计了平衡计分法下信用评级指标体系和信用评分方法,并将其应用于一个实例分析。     

Correlation smile matching for collateralized debt obligation tranches with α-stable distributions and fitted Archimedean copula models

As an extension of the standard Gaussian copula model to price collateralized debt obligation (CDO) tranche swaps we present a generalization of a one-factor copula model based on stable distributions. For special parameter values these distributions coincide with Gaussian or Cauchy distributions, but changing the parameters allows a continuous deformation away from the Gaussian copula. All these factor copulas are embedded in a framework of stochastic correlations. We furthermore generalize the linear dependence in the usual factor approach to a more general Archimedean copula dependence between the individual trigger variable and the common latent factor. Our analysis is carried out on a non-homogeneous correlation structure of the underlying portfolio. CDO tranche market premia, even throughout the correlation crisis in May 2005, can be reproduced by certain models. From a numerical perspective, all these models are simple, since calculations can be reduced to one-dimensional numerical integrals.     

农村信用体系建设研究——百色视角

农村信用体系建设是社会信用体系建设的重要组成部分。目前我国农村信用体系建设严重滞后,已阻碍了社会主义新农村建设的步伐。本文以百色市为视角,结合广西田东县农村信用体系建设实践,深入分析农村信用体系建设现状,找出存在问题及原因,并采取积极有效措施,进一步推进农村信用体系建设。     

欠发达地区金融支持农民增收问题研究

运用协整检验和格兰杰因果检验对广西1995-2008年农民收入和农村信贷投入之间的关系进行实证分析,结果表明农村信贷投入是农民收入增长的格兰杰原因,并存在长期均衡关系,由此提出了加大农村信贷投入的对策建议。     

一种全新的两阶段个人信用评分方法探究

目前我国还没有一套规范的个人信用评分指标体系和方法。本文利用真实的个人消费信贷数据,首先建立了个人信用评分的多元线性判别分析模型和BP神经网络模型,然后将线性判别分析模型的结果与其它变量一起作为输入变量建立了混合两阶段个人信用评分模型。实证研究表明,混合两阶段个人信用评分模型相对于前两种单一模型能同时满足预测精度和稳健性的双重要求,从而,突破了通常应用单一模型于个人信用评分领域的局限。     

A framework to measure integrated risk

A framework underlying various models that measure the credit risk of a portfolio is extended in this paper to allow the integration of credit risk with a range of market risks using Monte Carlo simulation. A structural model is proposed that allows interest rates to be stochastic and provides closed-form expressions for the market value of a firm's equity and its probability of default. This model is embedded within the integrated framework and the general approach illustrated by measuring the risk of a foreign exchange forward when there is a significant probability of default by the counterparty. For this example moving from a market risk calculation to an integrated risk calculation reduces the expected future value of the instrument by an amount that could not be calculated using the common pre-settlement exposure technique for estimating the credit risk of a derivative.     

Does scoring a subpopulation make a difference

Scorecards used by consumer credit providers to assess the probability that an applicant will default are usually built for the population of potential applicants as a whole. This paper investigates whether it is permissible and worth-while to build a separate scorecard for each subpopulation of applicants. We review the legal requirements to find that it is permissible to use separate scorecards for many, but not all, personal characteristics. Second, using data supplied by a credit card organization separate scorecards were built for several subpopulations for each of twelve personal characteristics. The predicted performance of each was compared with that gained form estimating a scorecard for the full population using three methods for setting the cut-off scores in an `independent' way. These methods differ in the degree to which the cut-off scores are independent of information about other subpopulation, in the level of discrimination achieved between likely good payers and defaulters and in the degree to which each method is robust to new data. We conclude, first, that creating scorecards using subpopulations does not necessarily give better discrimination between likely good payers and defaulters. Second, none of the three methods examined to set the cut-off scores dominates the others using the three desirable properties described; trade-offs are required. Finally, subpopulation scorecards lead to the rejection of fewer applicants than scorecards built on full populations.