一个数学建模问题的简单解法

银行利率给出了一个简单计算方法 ,这种方法更符合中学生思维的习惯 .     

CreditRisk+模型下商业银行经济资本配置研究

对金融资产风险的度量与经济资本的分配应该体现分散化效应,传统的V aR方式不能保证分散化效应的次可加性.本文讨论了基于T a ilV aR这一新的风险度量与经济资本分配标准,并在违约率均值不变情况下,对C red itR isk+模型下的商业银行经济资本分配进行了实证分析.     

基于人工神经网络的商业银行信用风险模型

在对人工神经网络的基本原理进行简要介绍的基础上 ,着重对构建商业银行信用风险的人工神经网络模型进行了研究 ,实证结果表明 ,人工神经网模型具有很高的预测精度     

生物催化在绿色化学和新药开发中的应用

近年来生物技术领域有了突破性进展，如: 公共基因数据库(GenBank)和蛋白质数据库(PDB)中序列的指数增长，高效基因克隆和表达平台的建立，可有效改进生物催化剂专一性、选择性和稳定性的酶定向进化技术的应用。这些进展使生物催化在化学合成中日趋重要[1]。本文综述了生物催化在如下领域的成功应用：在药物生产中用于开发经济的化学酶法合成工艺，在绿色化学领域中最大程度的减少废物的产生和危险试剂的应用，在天然化学领域中对天然产物进行修饰以发现具有更好生物活性的新药物。     

独立项目的久期预估与融资优化研究

项目投资与融资匹配程度,不仅关系到项目资金成本的大小,还体现对项目利率风险的对冲。基于债券久期的概念内涵,提出了项目久期与融资结构久期的概念及计算方法。以具体项目的投融资为案例进行研究,对案例公司已拟定的针对独立项目的债券融资方案进行投融资久期匹配、各期现金流匹配评价,并从市场利率曲线中发现融资成本优化空间,从投融资久期差及各期投融资现金流量差中找到优化融资的方案。以此提出结论:项目久期与融资结构久期的匹配是降低项目利率风险的重要手段;应综合融资结构久期与项目久期、现金流大小选择融资结构。     

农业供应链金融能有效缓解企业的融资约束？——涉农企业参与精准扶贫的实证研究

农业供应链金融为涉农企业融资难问题提供了新思路。本文以上市涉农上市企业2016~2018年的财务数据为样本,探讨了农业供应链金融对缓解企业融资约束的影响,以及涉农企业参与精准扶贫对缓解融资约束的调节作用。研究发现:涉农企业普遍面临融资约束问题,而农业供应链金融显著缓解了涉农企业的融资约束,且对参与精准扶贫企业的融资约束缓解更为显著。本文揭示了:农业供应链金融能有效缓解涉农企业的融资约束问题,再次证明了农业供应链金融缓解涉农企业融资约束的重要性和必要性,拓展了供应链金融研究的领域,同时对企业参与精准扶贫,响应新时期涉农企业社会责任的具体要求,形成政府、企业等共同承担社会责任的多元化减贫治理机制,助力乡村振兴提供相应借鉴。     

市场需求信息不对称下的保兑仓融资风险控制策略

信息不对称风险广泛存在于保兑仓融资过程当中,本文运用Stackelberg博弈模型刻画融资系统成员关系,运用动态规划优化分析方法求解对应博弈均衡策略。总结出需求信息不对称的三种表现形式:信息造假,信息优势及信息隐匿,分析各类信息不对称情形对融资系统所造成影响,并相应提出实现信息显示功能的契约甄别机制。研究表明:零售商可从信息不对称中获取巨大信息优势,但对其他成员造成损害,其中信息隐匿对生产商损害程度更高;二部定价机制可实现信息甄别,但生产商须为之付出信息租金,造成效率损失;而合理参数设定下的二部定价加回购机制有助于进一步改进融资系统及各成员收益,甚至达到次协调状态,最终实现融资成员收益的帕累托改进。本研究对于控制供应链融资中的信息风险、改善融资效率提供了理论依据及决策参考。     

The long‐term extreme price risk measure of portfolio in inventory financing: An application to dynamic impawn rate interval

Different from the short‐term risk measure for traditional financial assets (stocks, bonds, etc.), the key to illiquid inventory portfolio traded in the over‐the‐counter markets is to estimate the long‐term extreme price risk with time varying volatility. In this article, a new long‐term extreme price risk (value at risk and conditional value at risk) measure method for inventory portfolio and an application to dynamic impawn rate interval are proposed. To realize this, we first establish AutoRegressive Moving Average‐Exponential Generalized Autoregressive Conditional Heteroskedasticity‐Extreme Value Theory model and multivariatet‐Copula to depict the autocorrelation, fat tails, and volatility clustering of returns of inventories and the nonlinear dependence structure of inventories. Furthermore, we obtain the long‐term extreme price risk with time varying volatility via Monte Carlo simulation instead of square‐root‐of time rule. The results show that, first, benefits from risk diversification is significant; second, long‐term extreme price risk measure of inventory portfolio via Monte Carlo method outperforms the square‐root‐of time rule; the last is that the dynamic rate interval based on the long‐term price risk is superior to the crude rules of thumb in terms of reducing efficiency loss and improving risk coverage. In summary, this article provides a new quantitative framework for managing the risk of portfolio in inventory financing practice for banks constrained by risk limitation. © 2014 Wiley Periodicals, Inc. Complexity 20: 17–34, 2015     

基于极课大数据的初中化学组卷及试卷评价

为了提高组卷质量,基于极课大数据建立初中化学校本题库,根据双向细目表,利用该系统的出卷功能进行组卷。采用SPSS软件对试卷难度、区分度、信度和效度进行综合分析和评价,可知基于极课所出试卷各项评价指标较优秀,符合考试要求,试卷质量高。     

A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.