The Application of Genetic Algorithms and Multidimensional Distinguishing Model in Forecasting and Evaluating Credits for Mobile Clients

To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algorithm and multidimensional distinguishing model. In the end of this paper, a result of a testing application in Zhuhai Branch, GMCC was provided. The precision of the forecasting and evaluation of the client’s credit is near 90%. This study is very significant to the mobile communication corporation at all levels. The popularization of the techniques and the result would produce great benefits of both society and economy.

Effects of formal credit on pastoral household expense: Evidence from the Qinghai-Xizang Plateau of China

Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.However,a limited understanding remains of how formal credit affects herders'household expenses.Based on a survey of 544 herders from the Qinghai-Xizang Plateau of China,this study adopted the propensity score matching approach to identify the effect of formal credit on herders'total household expenses,daily expenses,and productive expenses.The results found that average age,grassland mortgage,and other variables significantly affected herders'participation in formal credit.Formal credit could significantly improve household expenses,especially productive expenses.A heterogeneity analysis showed that formal credit had a greater impact on the household total expense for those at higher levels of wealth;however,it significantly affected the productive expense of herders at lower wealth levels.Moreover,the mediating effect indicated that formal credit could affect herders'household income,thus influencing their household expenses.Finally,this study suggests that policies should improve herders'accessibility to formal credit.

Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation

To explore the green development of automobile enterprises and promote the achievement of the“dual carbon”target,based on the bounded rationality assumptions,this study constructed a tripartite evolutionary game model of gov-ernment,commercial banks,and automobile enterprises;introduced a dynamic reward and punishment mechanism;and analyzed the development process of the three parties’strategic behavior under the static and dynamic reward and punish-ment mechanism.Vensim PLE was used for numerical simulation analysis.Our results indicate that the system could not reach a stable state under the static reward and punishment mechanism.A dynamic reward and punishment mechanism can effectively improve the system stability and better fit real situations.Under the dynamic reward and punishment mechan-ism,an increase in the initial probabilities of the three parties can promote the system stability,and the government can im-plement effective supervision by adjusting the upper limit of the reward and punishment intensity.Finally,the implementa-tion of green credit by commercial banks plays a significant role in promoting the green development of automobile enter-prises.

Research on Early Warning of Banking Crises from the Perspective of Credit Structures

The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System

Spatiotemporal information,positioning and navigation services have become important elements of new type infrastructure.The rapid development of global digital trade provides a large-scale application scenario for the use of Beidou Navigation Satellite System(BDS)spatiotemporal information to support the certification of origin of agricultural products.The BDS spatiotemporal information agricultural product digital credit system uses such modules as BDS,spatiotemporal information collection,spatiotemporal coding,and spatiotemporal blockchain.It incorporates multi-level joint supervision mechanisms such as government,associations,and users.Starting from the initial production link of agricultural products,it realizes the correspondence and locking of online and offline products,effectively improves the integrity and credibility of information in the production process,finished product quality and circulation process of products,effectively manages the green production and anti-channel conflicts of producers,and provides credible information for consumers,thus realizing the digital credit certification of products.The successful practice of characteristic agricultural products in Yunnan Province has verified the application ability of the BDS spatiotemporal information agricultural product digital credit system.This system has played a significant role in promoting the online and offline locking,credible information,effective supervision and high quality and high price of characteristic agricultural products from the field.The BDS provides services for global digital trade and contributes to the further enhancement of the global application scale of GNSS.

基于Credit Metrics的城投公司结构性风险评价研究

随着我国城市基础设施建设规模的快速扩大,城市基础设施建设投资公司项目类型多元化与融资方式多元化匹配造成的结构性风险已经成为影响其健康发展的主要问题.在分析了城投公司结构性风险的发生机理及传导机制的基础上,针对城投公司债务的信用转移问题,运用Credit Metrics模型对城投公司结构性风险进行评价研究.结果表明,城投公司存在结构性风险且隐患巨大.一旦资金链出现问题,城投公司的债务将出现严重的违约情况.

模糊Credit Metrics模型及其在信用风险评估中的应用

在经典的Credit Metrics模型中,通常假设具有相同信用评级的债务人的贷款是同质的,即具有相同的信用等级转移概率,且在实际应用中这种概率难以精确估计。这些局限性大大限制了该方法在实际中的应用。为了更好地描述和处理关于信用等级转移的模糊信息,借鉴经典Credit Metrics模型的思想,提出了模糊Credit Metrics模型,通过将传统的确定的信用转移矩阵模糊化,从而利用二型模糊变量来对信贷资产的远期价值进行描述和刻画,在此基础上对信贷资产的期望值和在险价值等量化指标进行计算,为基于不确定信息或专家主观判断的信用风险评估提供了一种系统的有效方法。

基于Credit Portfolio View的信用风险度量模型研究

结合我国贷款企业的特点,Credit Portfolio View模型的转移矩阵中信用等级违约概率除了受宏观经济因素影响外,还受到行业因素、地区因素、规模因素以及企业所有制性质等因素影响,这些因素使得同一信用等级下的企业历史违约率统计出现差异。笔者对Credit Portfolio View模型违约因素做了宏观、行业、地区三个维度的扩展,并采用Logit模型与随机模拟相结合的方法,对模型参数进行了估计。

基于Credit Risk+模型的互联网金融信用风险估计

文章基于Credit Risk+模型,使用互联网信贷平台四个行业的贷款数据,在不同置信水平下,对互联网金融的信用风险水平进行比较分析。结果表明,行业风险因子间协方差相等时,复合伽玛Credit Risk+模型和多元系统风险Credit Risk+模型计算结果几乎一致,与CSFB Credit Risk+模型和两阶段Credit Risk+模型相比能更好地反映贷款组合的非预期损失。行业风险因子间协方差不等时,多元系统风险Credit Risk+模型能克服其他Credit Risk+模型的缺陷,综合考量系统风险和行业风险的影响,能更好地估计贷款组合的信用风险水平。

Credit Metrics模型计算信用风险的实例分析

CreditMetrics作为计算资产组合信用风险的模型,是一个联系信用和证券市场的简单、动态的架构。本文从此模型出发,分别讨论了单个贷款和资产组合基于违约率,信用迁移概率的计算原理和实例,并对违约率的测算作了进一步的分析和讨论。

基于Credit Metrics模型的汽车消费贷款业务信用风险管理分析

汽车消费贷款业务的蓬勃发展要求商业银行加强对该业务的信用风险管理。Credit Metrics模型是世界上第一个评估信用风险的量化度量模型,并为改善商业银行被动的信用风险管理提供了一种有效的管理思路和基础。

基于CreditMetrics模型评估银行信贷的信用风险

随着市场竞争日趋激烈,金融风险管理显得越来越重要。文章首先论述CreditMetrics模型的建模逻辑过程及其特点;基于风险价值(var)概念进行蒙特卡罗模拟,计算得出某商业银行信贷数据的核心参数:信用风险转移矩阵、门槛率、违约回复率以及最终的风险价值,进而利用这些参数测算出该商业银行贷款的风险等级及其分布。

基于改进Credit Metrics模型的商业银行信用风险度量及实证研究

信用风险是我国商业银行面临的主要风险。随着美国金融危机的蔓延,各国银行业已意识到加强信用风险监管的必要性。研究信用风险特征,建立合适的度量模型准确地度量我国商业银行的信用风险,是降低信用风险的必然要求,而Credit Metrics模型以其擅长度量非交易性资产的信用风险而著称。作者首先对Credit Metrics模型加以改进,使用我国的信用评级转移矩阵,其次考虑宏观经济和企业本身的非系统性风险,重新调整信用评级转移矩阵。最后以某家银行为基础,使用Credit Metrics模型进行实证研究,同时对于模型中的部分参数进行修正并对模型加以改进,从而完善Credit Metrics模型在我国商业银行业的应用。

银行信用风险度量Credit Metrics^(TM)模型与CPV模型比较研究

根据新巴塞尔资本协议的基本原则及巴塞尔银行监管委员会推荐的确定资本金的VaR方法,结合中国商业银行目前信用风险管理的实际情况,比较分析CreditMet-ricsTM模型和CPV模型的基本原理和参数选择的共性及差异,对两模型各自特点做出客观评价,结果发现运用CPV模型有利于提高信用风险度量的精确性,为商业银行信用风险管理提供了有益的借鉴.

基于损失程度变化的CreditRisk^+的鞍点逼近

传统的CreditRisk+模型在度量信用风险过程,假定违约损失是给定不变的,而近年的实证研究表明在实际的金融市场中违约损失是变化的。针对传统模型这一假设的不合理性,本文对模型作了发展。新模型充分考虑了违约时损失程度的变化,用β分布来刻画这种变化,并利用鞍点逼近给出了信用风险的度量,改进了传统递推算法的不足。最后进行数值模拟以说明方法的有效性。

一种基于Credit的变长分组并行交换网络调度算法

该文提出了一种新的并行分组交换(PPS)网络调度算法。该算法通过在解复用器处采用以变长分组为业务分配单元的方式消除了信元的乱序问题;通过采用Credit机制进行业务分配,实现了业务到各个交换平面完全公平的分配;各个并行交换单元采用组合输入输出排队,降低了对缓存和交换平面的加速要求,同时可以充分利用现有单Crossbar网络调度算法的研究成果。文中证明了该算法对业务分配的公平性,对高速缓存的需求量以及整个网络的稳定性,仿真进一步证明了该算法具有良好性能。

CreditMetrics模型中转移概率和风险价值的计算

CreditMetrics模型是量化信用风险的管理模型,信用矩阵转移概率的确定是该模型的核心问题之一.该文提出一种信用矩阵转移概率的估计方法,采用随机模拟的数据进行验证,并通过误差分析确定较为合适的样本容量.同时改进原有模型中对贷款现金流的计算方法,即一类客户在n年内信用等级的各种转移情况下的贷款现金流折算.最后采用核估计方法计算贷款风险值VaR,并与原有模型的计算结果进行比对.根据比对结果,可以证明此方法是行之有效的.

我国商业银行信用风险宏观压力测试研究——基于改进的Credit Portfolio View模型

本文利用2004-2013年的季度数据,基于改进的Credit Portfolio View模型,对我国商业银行的信用风险进行宏观压力测试,并利用蒙特卡罗模拟技术,模拟了不同压力冲击下商业银行不良贷款率的路径变化。研究结果表明,在不同的压力冲击下,商业银行的不良贷款率都出现不同幅度的上升,尤其是在极端压力情形下,我国商业银行的不良贷款率将急剧上升。最终宏观压力测试风险评估的结果显示,目前我国商业银行体系抵御风险的能力整体较强。