Default and Punishment in General Equilibrium1

We extend the standard model of general equilibrium with incomplete markets to allow for default and punishment by thinking of assets as pools. The equilibrating variables include expected delivery rates, along with the usual prices of assets and commodities. By reinterpreting the variables, our model encompasses a broad range of adverse selection and signalling phenomena in a perfectly competitive, general equilibrium framework. Perfect competition eliminates the need for lenders to compute how the size of their loan or the price they quote might affect default rates. It also makes for a simple equilibrium refinement, which we propose in order to rule out irrational pessimism about deliveries of untraded assets. We show that refined equilibrium always exists in our model, and that default, in conjunction with refinement, opens the door to a theory of endogenous assets. The market chooses the promises, default penalties, and quantity constraints of actively traded assets.     

Teachers,Schools, and Academic Achievement

This paper disentangles the impact of schools and teachers in influencing achievement with special attention given to the potential problems of omitted or mismeasured variables and of student and school selection. Unique matched panel data from the UTD Texas Schools Project permit the identification of teacher quality based on student performance along with the impact of specific, measured components of teachers and schools. Semiparametric lower bound estimates of the variance in teacher quality based entirely on within‐school heterogeneity indicate that teachers have powerful effects on reading and mathematics achievement, though little of the variation in teacher quality is explained by observable characteristics such as education or experience. The results suggest that the effects of a costly ten student reduction in class size are smaller than the benefit of moving one standard deviation up the teacher quality distribution, highlighting the importance of teacher effectiveness in the determination of school quality.     

Feature Selection for Consistent Biclustering via Fractional 0–1 Programming

Biclustering consists in simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets (classes). Samples and features classified together are supposed to have a high relevance to each other which can be observed by intensity of their expressions. We define the notion of consistency for biclustering using interrelation between centroids of sample and feature classes. We prove that consistent biclustering implies separability of the classes by convex cones. While previous works on biclustering concentrated on unsupervised learning and did not consider employing a training set, whose classification is given, we propose a model for supervised biclustering, whose consistency is achieved by feature selection. The developed model involves solution of a fractional 0–1 programming problem. Preliminary computational results on microarray data mining problems are reported.This research work was partially supported by NSF, NIH and AirForce grants.     

A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem

In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The developed approach and the well-known sorting method UTADIS are applied to a real case study to analyze the R&D projects proposed to a grant program executed by a government funding agency in 2009. A five level R&D project selection criteria hierarchy and an assisting point allocation guide are defined to measure and quantify the performance of the projects. In the case study, the developed methods are observed to be more stable than UTADIS.     

基于HTFWGBM算子的供应商选择模型研究

由于供应商选择中的的复杂性与不确定性以及人类认知的有限性,从而导致信息融合失真和决策结果不准确的情况。针对决策属性以犹豫三角模糊数（HTFN）给出的供应商的选择问题,本文提出一种基于HTFGWBM算子的决策算法。首先,针对犹豫三角模糊数和几何Bonferroni平均算子理论,分别定义了犹豫三角模糊几何Bonferroni平均（HTFGBM）算子和犹豫三角模糊几何加权Bonferroni平均（HTFGWBM）算子,同时分别研究了算子的幂等性,置换不变性,单调性和有界性等性质。其次,基于HTFGWBM算子构建新型犹豫多属性决策模型,结合HTFN排序方法进行备选供应商排序。最后通过算南水北调中线工程中的供应商选择实例证明了决策模型的可行性与有效性。结果表明,通过调整模型参数,模型具有一定的延展性和容错能力,能够很好的进行科学决策。     

基于个性化需求的产品竞争供应链结构选择

基于"制销分离"与"定制一体"两种结构选择,构建个性化产品供应链处于非竞争与竞争环境下的博弈模型,在引入一个实际案例的基础上,运用算例仿真比较分析两种运营模式下的供应链运作策略和盈利差异。结果显示：与制销分离结构相比,个性化产品供应链选择定制一体结构,有利于匹配产品个性化制造、提升产品个性化水平和市场需求、增加供应链的期望收益;个性化产品供应链选择制销分离结构时,适度的批发价格激励能够提升产品个性化水平、更好满足消费者个性化需求、改善供应链的运营绩效,分销商适度让利加大批发价格激励力度可显著促进产品个性化制造互动、提升产品个性化水平;竞争将消减个性化产品供应链的运营绩效,但选择定制一体结构可显著提升竞争力。     

资本结构作用下市场投资组合轨迹的研究

根据企业理财中投融资决策的互动机理 ,将资本结构与投资组合优化结合起来 ,在证券组合模型基础上 ,引入资本结构这一重要因素 ,研究了资本结构作用下市场投资组合点的轨迹 ,从而得出任一资本结构下所对应的市场投资组合     

异质品双渠道供应链下制造商的渠道决策研究

研究由两个制造商、两个零售商以及消费者组成的双渠道供应链,制造商的最优产品分销渠道策略问题,通过构建Stackelberg主从动态博弈模型,分析制造商在不同渠道选择下的产品质量水平及定价决策,消费者三种不同渠道偏好下渠道竞争和品牌竞争激烈程度对制造商渠道选择均衡结果及帕累托最优选择的影响,以及对产品质量水平、产品价格、制造商及零售商利润的影响.研究表明：品牌和渠道双重竞争下,两种竞争之间存在相互作用的关系,并共同影响制造商最终渠道选择,制造商通过权衡价格决策的后动优势与消费者渠道偏好对其利润的贡献选择是否开辟双渠道,产品价格与产品质量水平正相关,与产品的替代率负相关,产品质量水平对渠道价格的影响程度等于消费者对该渠道的偏好程度.当消费者偏好某一渠道时,制造商可在该渠道上的产品采取优质高价策略,但渠道上产品性价比降低,当品牌竞争越激烈,产品价格越低,对制造商和零售商的利润挤压越严重.     

高频数据瞬时波动率核估计的窗宽选择及算法研究

在瞬时波动率的各种估计量中,非参数估计量因其能准确地度量瞬时波动率,一直是学者们的研究热点。然而,这类估计量在实际应用中都面临着最优窗宽的确定问题。由于最优窗宽中往往携带一些难以估计的未知参数,使得在实际应用过程中确定最优窗宽的具体数值存在困难。本文以瞬时波动率的核估计量为例,借鉴非参数回归分析中窗宽选择的思想,构建了一种能从数据中准确计算出最优窗宽具体值的算法。理论的分析和数值上的验证表明：文中所构建的算法具有良好的稳定性、适应性和收敛速度。算法的提出为瞬时波动率的后续应用研究铺平道路。     

基于股价预测的泛证券投资组合策略

本文基于期望效用最大化和L₁-中位数估计研究了在线投资组合选择问题。与EG（Exponential Gradient）策略仅利用单期价格信息估计价格趋势不同,本文将利用多期价格信息估计价格趋势,以提高在线策略的性能。首先,基于多期价格数据,利用L₁-中位数估计得到预期价格趋势。然后,通过期望效用最大化,提出一个新的具有线型时间复杂度的在线策略,EGLM（Exponential Gradient via L₁-Median）。并通过相对熵函数定义资产权重向量的距离,进而证明了EGLM策略具有泛证券投资组合性质。最后,利用国内外6个证券市场的历史数据进行实证分析,结果表明相较于UP（Universal Portfolio）策略和EG策略,EGLM策略有更好的竞争性能。