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.

Ranking academic institutions based on the productivity,impact,and quality of institutional scholars

Purpose:The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity,impact,and quality.Design/methodology/approach:The institutional ranking process developed here considers all institutions in all countries and regions,thereby including those that are established,as well as those that are emerging in scholarly prowess.Rankings of individual scholars worldwide are first generated using the recently introduced,fully indexed ScholarGPS database.The rankings of individual scholars are extended here to determine the lifetime and last-five-year Top 20 rankings of academic institutions over all Fields of scholarly endeavor,in 14 individual Fields,in 177 Disciplines,and in approximately 350,000 unique Specialties.Rankings associated with five specific Fields(Medicine,Engineering&Computer Science,Life Sciences,Physical Sciences&Mathematics,and Social Sciences),and in two Disciplines(Chemistry,and Electrical&Computer Engineering)are presented as examples,and changes in the rankings over time are discussed.Findings:For the Fields considered here,the Top 20 institutional rankings in Medicine have undergone the least change(lifetime versus last five years),while the rankings in Engineering&Computer Science have exhibited significant change.The evolution of institutional rankings over time is largely attributed to the recent emergence of Chinese academic institutions,although this emergence is shown to be highly Field-and Discipline-dependent.Practical implementations:Existing rankings of academic institutions have:(i)often been restricted to pre-selected institutions,clouding the potential discovery of scholarly activity in emerging institutions and countries;(ii)considered only broad areas of research,limiting the ability of university leadership to act on the assessments in a concrete manner,or in contrast;(iii)have considered only a narrow area of research for comparison,diminishing the broader applicability and impact of the assessment.In general,existing institutional rankings depend on which institutions are included in the ranking process,which areas of research are considered,the breadth(or granularity)of the research areas of interest,and the methodologies used to define and quantify research performance.In contrast,the methods presented here can provide important data over a broad range of granularity to allow responsible individuals to gauge the performance of any institution from the Overall(all Fields)level,to the level of the Specialty.The methods may also assist identification of the root causes of shifts in institution rankings,and how these shifts vary across hundreds of thousands of Fields,Disciplines,and Specialties of scholarly endeavor.Originality/value:This study provides the first ranking of all academic institutions worldwide over Fields,Disciplines,and Specialties based on a unique methodology that quantifies the productivity,impact,and quality of individual scholars.

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.

Requirements ranking based on crowd-sourcing high-end product USs

Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.

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.

肉苁蓉苷A通过JNK/MAPK通路抑制破骨细胞活性

背景:研究证实肉苁蓉苷A具有抗炎、抗氧化和抗骨质疏松的作用,但其对破骨细胞分化和功能的影响及潜在的机制尚缺少研究。目的:探讨肉苁蓉苷A对核因子κB受体活化因子配体诱导的体外破骨细胞分化和骨吸收功能的影响及作用机制。方法:提取与培养4-6周龄C57BL/6小鼠股骨和胫骨中的骨髓巨噬细胞,采用CCK-8毒性实验检验不同浓度的肉苁蓉苷A(5,10,20,40,80,160μmol/L)对骨髓巨噬细胞的毒性,抗酒石酸酸性磷酸酶染色观察不同浓度的肉苁蓉苷A干预破骨细胞的分化情况,确定药物的有效干预浓度。将实验分为阳性对照组、肉苁蓉苷A低、中、高剂量组(40,80,160μmol/L),细胞贴壁后各组均加入50 ng/mL的核因子κB受体活化因子配体诱导破骨细胞分化,肉苁蓉苷A低、中、高剂量组分别加入相应剂量的肉苁蓉苷A进行干预。采用肌动蛋白环鬼笔环肽染色和DAPI染色检测肉苁蓉苷A对破骨细胞形成的影响;骨磨片甲苯胺蓝染色观察肉苁蓉苷A对破骨细胞骨吸收功能的影响;Western blot检测JNK/MAPK通路上下游蛋白的表达情况;RT-qPCR检测抗酒石酸酸性磷酸酶、DC-STAMP、Nfatc-1、Ctsk和c-Fos等破骨细胞分化和骨吸收功能相关基因的表达情况。结果与结论:①抗酒石酸酸性磷酸酶染色、肌动蛋白环染色和骨陷窝甲苯胺蓝染色结果表明,与阳性对照组相比,肉苁蓉苷A对核因子κB受体活化因子配体诱导的体外破骨细胞的分化和骨吸收功能具有呈剂量依赖性的显著抑制作用;②RT-qPCR结果表明,与阳性对照组相比,肉苁蓉苷A高剂量组和低剂量组的抗酒石酸酸性磷酸酶、DC-STAMP、Nfatc-1、Ctsk和c-Fos等的mRNA表达量均显著降低,且肉苁蓉苷A高剂量组的降低效果更加显著;③Western blot结果显示,高剂量肉苁蓉苷A干预10,20,30和60 min时显著抑制p-JNK蛋白的表达;与阳性对照组相比,肉苁蓉苷A低、中、高剂量组显著抑制Nfatc1和c-Fos蛋白的表达,且呈剂量依赖性;④结果说明,肉苁蓉苷A能够通过降低p-JNK蛋白水平,抑制MAPK通路的激活和破骨细胞关键基因的表达,进而抑制破骨细胞的形成和骨吸收功能。

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.

线性程序的Ranking函数自动合成

针对判定一个程序终止性的经典方法Ranking函数法,运用半代数系统的概念,把程序终止性问题转换为求半代数系统的Ranking函数。然后运用符号计算工具DISCOV-ERER和Farkas引理,求出函数参数存在的充分必要条件,并根据符号计算理论的方法自动合成Ranking函数。通过计算代数理论的证明和试验的验证,并与其他方法做了比较,这种方法是高效合理的。

一种基于潜变量的Ranking模型构造算法

现有的Ranking算法获得的模型全部来自训练数据,因为很多模型的有用信息并不能完全从训练数据中得到,因此这样得到的模型不够精确,对此,提出一种基于潜变量的Ranking算法。该算法以结构化SVM为学习工具,将除训练数据外的其他有用信息以潜变量形式引入算法的框架中,并在此基础上定义了面向NDCG的目标函数。针对该目标函数非凸非平滑,首先使用"凹-凸过程"进行逼近,然后用"近似Bundle法"展开优化计算。基准数据集上的实验结果表明:相比完全依靠训练数据的Ranking算法,本文算法获得的模型更为精确。

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。

基于pairwise的改进ranking算法

传统基于pairwise的ranking算法,学习后得到的模型在用NDCG这样的ranking标准评价时效果并不好,对此提出了一种新型ranking算法。该算法也是使用样本对作为训练数据,但定义了一个面向NDCG评估标准的目标函数。针对此目标函数非平滑、难以直接优化的特点,提出使用割平面算法进行学习,不仅解决了上述问题,而且使算法迭代的次数不再依赖于训练样本对数。最后基于基准数据集的实验证明了算法的有效性。

基于CreditMetrics模型的Var方法计算

近期全球金融市场呈现出前所未有的波动性和脆弱性,各国金融监管当局和金融机构都在不断强化和完善对风险的管理。基于CreditMetrics模型,确定了一种能够根据信贷资产的具体情况,方便、快捷地量化风险的通用计算方法。该方法通过明确设定信用等级转移矩阵,估算未来不同信用等级下的贷款远期价值以及推导贷款价值变动的远期分布,对单笔贷款的信用风险进行了具体的量化分析,有利于各金融机构对信贷资产的风险识别、业绩评估和对金融创新业务风险的监管。

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。