Estimation of the Multivariate Normal Precision Matrix under the Entropy Loss

Let X ₁, , X _n (n > p) be a random sample from multivariate normal distribution N _p (, ), where R ^p and is a positive definite matrix, both and being unknown. We consider the problem of estimating the precision matrix ^–1. In this paper it is shown that for the entropy loss, the best lower-triangular affine equivariant minimax estimator of ^–1 is inadmissible and an improved estimator is explicitly constructed. Note that our improved estimator is obtained from the class of lower-triangular scale equivariant estimators.     

Bayesian networks applied to credit scoring

Email: kchang{at}gmu.eduEmail: RobertFung{at}Fairlsaac.comEmail: alan.lucas{at}hotmail.com^¶Email: BobOliver{at}Fairlsaac.com^||Email: NShikaloff{at}Fairlsaac.com The objectives of this paper are to apply the theory and numericalalgorithms of Bayesian networks to risk scoring, and comparethe results with traditional methods for computing scores andposterior predictions of performance variables. Model identification,inference, and prediction of random variables using Bayesiannetworks have been successfully applied in a number of areas,including medical diagnosis, equipment failure, informationretrieval, rare-event prediction, and pattern recognition. Theability to graphically represent conditional dependencies andindependencies among random variables may also be useful incredit scoring. Although several papers have already appearedin the literature which use graphical models for model identification,as far as we know there have been no explicit experimental resultsthat compare a traditionally computed risk score with predictionsbased on Bayesian learning algorithms. In this paper, we examine a database of credit-card applicantsand attempt to ‘learn’ the graphical structure ofthe characteristics or variables that make up the database.We identify representative Bayesian networks in a developmentsample as well as the associated Markov blankets and cliquestructures within the Markov blanket. Once we obtain the structureof the underlying conditional independencies, we are able toestimate the probabilities of each node conditional on its directpredecessor node(s). We then calculate the posterior probabilitiesand scores of a performance variable for the development sample.Finally, we calculate the receiver operating characteristic(ROC) curves and relative profitability of scorecards basedon these identifications. The results of the different modelsand methods are compared with both development and validationsamples. Finally, we report on a statistical entropy calculationthat measures the degree to which cliques identified in theBayesian network are independent of one another.     

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。     

Performance of Portfolios Based on the Expected Utility-Entropy Fund Rating Approach

Yang and Qiu proposed and reframed an expected utility–entropy (EU-E) based decision model. Later on, a similar numerical representation for a risky choice was axiomatically developed by Luce et al. under the condition of segregation. Recently, we established a fund rating approach based on the EU-E decision model and Morningstar ratings. In this paper, we apply the approach to US mutual funds and construct portfolios using the best rating funds. Furthermore, we evaluate the performance of the fund ratings based on the EU-E decision model against Morningstar ratings by examining the performance of the three models in portfolio selection. The conclusions show that portfolios constructed using the ratings based on the EU-E models with moderate tradeoff coefficients perform better than those constructed using Morningstar. The conclusion is robust to different rebalancing intervals.     

跨界创新联盟关联信用风险传染模型研究

跨界创新联盟是企业创新过程中资源整合与共享的一种有效形式,但在带来企业创新资源共享效益的同时,联盟中复杂的关联关系也构成了联盟关联信用风险传染的新渠道。本文结合复杂网络理论与传染病模型,根据跨界创新联盟特点,在原有资产关联研究的基础上,综合考虑行业关联、资产关联和创新合作关联三种关联关系,改进了风险传播的传染病模型,并进行了数值仿真实验。结果表明：行业关联、资产关联和创新合作关联对跨界创新联盟关联信用风险传染阈值具有综合性影响,增加行业关联和创新合作关联因素,有利于提高关联信用风险传染阈值的精度。此外,资产关联度越高则感染成员密度越大;行业关联度越低,创新合作关联度越高则感染成员密度越小。     

云计算开源生态知识共享及风险治理的演化博弈研究

云计算开源生态正在逐步形成,促进知识共享、规避开源风险成为开源运营及开源治理的重要目标。本文基于演化博弈理论,构建了行业自律和政府监管两种治理机制下多方利益相关者(云计算企业、产业联盟以及政府)之间的演化博弈模型,分析了关键参数对各方策略及系统收益的影响。研究发现,在行业自律机制下,违约金和基金会补贴会促进知识共享合作,而产业联盟对于风险的存在则更为敏感。在政府监管机制下,罚款和政府补贴可以帮助参与者平衡风险并促进知识共享合作。研究结果可为云计算开源生态的可持续发展提供决策参考。     

基于风险-均值分析的供应商选择与订单分配集成优化模型与求解

针对不确定环境下具有不同供应合约的供应商选择与订单分配问题,本文构建了基于风险-均值分析的模糊两阶段多周期集成优化模型。与传统的该问题研究并未充分考虑供应商选择与订单分配两阶段决策的交互影响不同,在该模型中,第一阶段供应商选择的评价目标依赖于后期实际运营中的订单分配决策;并考虑未来需求和实际运营成本的不确定性,引入在险价值和期望值两种决策准则对供应商选择方案的绩效进行评价。提出了该模型的分析求解方法,在险价值得以精确评估,期望值被控制在确定的误差范围内,并可以达到足够的精度要求。     

Gompertz zero-inflated cure rate regression models applied to credit risk data

In the financial market, it is important to consider that there is a proportion of customers that have settled their debt in time zero, immediately recovering their ability to pay. In this context, in this paper, we propose a survival analysis methodology that allows the insertion of times equal to zero in scenarios where credit risk is observed. The proposed model addresses the survival analysis model of the zero-inflated cure rate which incorporates the heterogeneity of three subgroups (individuals having events in the initial time, and individuals not susceptible and susceptible to the event). In our proposal, all available survival data of customers are modeled considering that the number of competitive causes follows a Poisson distribution and the baseline risk function follows a Gompertz distribution. The model parameter estimation is obtained by the maximum likelihood estimation procedure and simulation studies are conducted to evaluate the estimators' performance. The studied methodology will be applied to a credit database provided by a financial institution in Brazil.     

中国进口食品安全风险评估的统计学方法

近年来,随着对外贸易的快速发展和人民生活水平的不断提高,我国已经成为食品进口大国。运用科学的方法优化检验资源配置,强化对进口食品安全的风险管理,是一个关系到人民福社的重大问题。国际上通行的做法是对不同种类的食品和检测项目的潜在风险进行评估,并根据评估结果制定有针对性的监督抽检计划,使得更多的检验资源可以投入到对高风险产品和项目的监管中去。我国在这方面的管理实践才刚刚起步,急需建立一套符合我国食品进口自身特点和管理需求的风险评价模型和相应的估计方法。本文借鉴了其它国家的相关做法,并结合我国进口食品的管理需求和历史检测数据的特点,制定了一套完整的风险评价和估计方法。     

基于混合Copula模型的投资组合风险分析

金融市场或股票之间的相关关系变化灵活多样,针对现实中往往需要考虑的是多个市场、股票的结构,采用Mixture-Copula模型来分析多元市场的相关性结构,进而构建了Multivariate-GARCH-Mixture-Copula,模型,并选取2002年1月1日至2011年12月31日上证工业指数、商业指数、地产指数三个行业指数序列的2425组数据利用该模型进行实证分析.分析表明,Multivariate-GARCH-Mixture-Copula模型能有效地应用于实际金融市场潜在结构的分析,对投资组合的风险研究有一定的参考意义.