组合证券投资风险最小化模型研究

组合证券投资风险最小化模型研究①杨桂元马永开唐小我ＡＢＳＴＲＡＣＴ①国家教委优秀年轻教师基金资助项目。ＢａｓｅｄｏｎｇｅｎｅｒａｌＭｉｎｉｍｉｚｅｄＲｉｓｋＭｏｄｅｌｏｆＣｏｍｂｉｎｅｄＳｅｃｕｒｉｔｉｅｓｉｎｖｅｓｔｍｅｎｔ,ｔｈｅａｕｔｈｏｒｓｓ...     

带有不可观测变量的模型及其应用

在应用回归模型研究社会经济现象间的关系时,常常遇到一些变量如文化水平、人的智能等,它们从内在本质上是不能直接被观察到的,但是又与一些可度量的变量紧密相连。文化水平与受教育状况密切相关,而人的智能则可通过一些有效的指标如智力测验的结果来表示。又如,永久...     

基于潜变量模型的多元有序数据轮廓分析法

提出了一种适用于多元有序数据的轮廓分析方法。鉴于有序数据无法满足轮廓分析对数据正态性的要求,采用潜变量模型对有序变量进行赋值,利用Bootstrap方法重构样本,使重构后的新数据满足正态性且总体均值与原样本一致,因而可以将轮廓分析法应用于有序数据均值向量的比较问题。讨论了单样本情形的同水平假设、两样本和多样本情形的平行、同水平和平坦性假设,并给出相应的检验统计量和拒绝域。最后,通过随机模拟来检验该方法的合理性,并得到结论:样本质量较高时,该方法在控制第一类错误和提高检验的功效上效果很好;对于一般样本而言,该方法的实际第一类错误较名义值有所增大,可通过提高原始样本量、降低名义第一类错误和进行多次试验来解决。     

我国国民收入增长模型研究

The paper provides a multi-stages indicator model of national income growth and solves the problems of continuous presentation of change points, parameter estimation as well as the realization of the multi-stage economic indicator model on computer. The paper also gives four equivalent types of multi-stage indicator models on GNP growth.     

计量经济学中的潜变量模型：一个方法论角度的考察

上世纪中叶,因子分析和典型相关分析方法的发展完善,解决了潜变量的测度及其相关关系衡量问题,奠定了潜变量因果模型的方法论基础。此后,潜变量模型被引入到计量经济学研究领域,依次经历了共同结构范式模型、经典潜变量模型和非经典潜变量模型三个阶段,逐步成为现代计量经济模型的重要组成部分。本文从方法论角度对计量经济学中的潜变量模型发展过程进行了全面考察,比较了各个阶段建模方法论的特征,归纳总结了其发展演化规律,并对下一步研究的重点领域进行了展望。     

我国A股市场系统性风险的实证研究

一、系统性风险的定义及其统计计量现代投资理论表明 ,股票投资风险是投资者的未来实际收益与预期收益的偏差程度。用统计语言可表述为 ,投资者投资于某股票的实际收益与期望值的偏离程度 ,即收益方差 (或收益均方差 )。股票投资的总风险可分为系统性风险和非系统性风险两种。系统性风险是指由于某种因素对证券市场上所有证券带来损失的可能性 ,如国家的某项经济政策变化、有关法律的制定等 ,都会影响整个证券市场价格。系统性风险强调的是对股票市场上所有的股票都有影响 ,而且该风险通常难以直接规避和消除。非系统性风险是指某些因素对单…     

国债期限风险溢价的ARCH模型族研究

国债期限风险溢价是指国债投资回报率与无风险资产收益率之间的差值.分析国债期限风险溢价的影响因素及可预测性.具有较强的理论与实践意义.文章利用ARCH模型族对上交所国债期限风险溢价的周序列建立了回归模型.取得了显著的统计效果.     

基于马尔可夫转换模型的违约风险溢价预测研究

针对违约风险溢价变化依赖于经济波动状态以及市场、宏观经济变量依赖于经济周期时变因素的阶段,基于马尔可夫转换阶段的具体特征,构建马尔可夫违约风险溢价预测转换模型,并以香港恒生指数信用违约互换波动为例,测算因时变系数波动的指数息差、宏观经济变量等概率,通过实证算例剖析股市、宏观经济变量与违约风险溢价之间的内在联动关系和信用违约风险溢价变化的转换机制,以期实现对违约风险溢价能够进行有效预测,实证仿真结果说明了模型的有效性。     

我国国债风险的实证分析

 近年来,我国国民经济出现明显的通货紧缩迹象,有效需求不足,物价指数下跌,经济增长率也从1992年的14.2%下降到1999年的7.1%。在货币政策实施效果不明显的情况下,1998年下半年,政策决定采取积极的财政政策,刺激消费和投资,拉动经济增长。在财政收入的“两个比重”下降的情况下,政府不得不采取积极的国债政策,扩大国债发行规模,筹集更多资金,以加大公共投资支出。因此,国债政策已成为当前积极财政政策的重要组成部分。但从动态趋势来看,中国国债发行规模自1994年以来以30%的速度急剧扩张,正面临着巨大的压力及与日俱增的财政信用风险。因此,在实施积极财政政策时,既要充分发挥国债对国民经济增长的拉动作用及对经济运行的反周期调节作用,又要防止国债规模过大对财政收支造成难以承受的压力,避免像某些发展中国家那样因债务问题而陷入财政信用危机。     

家庭收入跨地区比较的潜变量分析

针对居民家庭消费与社会经济状况调查中户主自报的家庭固定收入可信程度差以及不同地区的不可比问题,利用陕西省2008年第四次国家卫生服务调查数据,将自报的家庭收入作为不能准确测量的潜变量,运用DIHOPIT模型确定反映家庭收入高低的指示变量,通过不同地区共同的指示变量,用相同或近似的指示变量作为截断点评价不同地区家庭收入的差别。用陕西省城乡6个县(区)的调查数据拟合模型,发现在众多的指示变量中,"电话类型"为5个县(区)共同指示变量,同时也发现部分指示变量在部分调查县(区)缺乏效度,需要在今后的调查问卷条目设计时做适当调整和补充。     

Generalized Linear Latent Variable Models with Flexible Distribution of Latent Variables

Abstract. We consider a semi‐nonparametric specification for the density of latent variables in Generalized Linear Latent Variable Models (GLLVM). This specification is flexible enough to allow for an asymmetric, multi‐modal, heavy or light tailed smooth density. The degree of flexibility required by many applications of GLLVM can be achieved through this semi‐nonparametric specification with a finite number of parameters estimated by maximum likelihood. Even with this additional flexibility, we obtain an explicit expression of the likelihood for conditionally normal manifest variables. We show by simulations that the estimated density of latent variables capture the true one with good degree of accuracy and is easy to visualize. By analysing two real data sets we show that a flexible distribution of latent variables is a useful tool for exploring the adequacy of the GLLVM in practice.     

Modeling Longitudinal Obesity Data with Intermittent Missingness Using a New Latent Variable Model

We propose a latent variable model for informative missingness in longitudinal studies which is an extension of latent dropout class model. In our model, the value of the latent variable is affected by the missingness pattern and it is also used as a covariate in modeling the longitudinal response. So the latent variable links the longitudinal response and the missingness process. In our model, the latent variable is continuous instead of categorical and we assume that it is from a normal distribution. The EM algorithm is used to obtain the estimates of the parameter we are interested in and Gauss–Hermite quadrature is used to approximate the integration of the latent variable. The standard errors of the parameter estimates can be obtained from the bootstrap method or from the inverse of the Fisher information matrix of the final marginal likelihood. Comparisons are made to the mixed model and complete-case analysis in terms of a clinical trial dataset, which is Weight Gain Prevention among Women (WGPW) study. We use the generalized Pearson residuals to assess the fit of the proposed latent variable model.     

Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.     

Sieve Estimation of Time-Varying Panel Data Models With Latent Structures

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle property so that the group-specific functional coefficients can be estimated as well as if the individuals’ membership were known. Several extensions are discussed. Simulations demonstrate excellent finite sample performance of the approach in both classification and estimation. We apply our method to study the heterogeneous trending behavior of GDP per capita across 91 countries for the period 1960–2012 and find four latent groups.     

线性时变参数DGM(1,1)变权组合预测模型

文章为了提高统计组合预测的拟舍和预测精度,根据线性时变参数离散灰色预测模型的初值优化方法,给出了几个线性时变参数DGM(1,1)模型作为单项预测模型,进一步利用这些单项预测模型建立了一类变权线性时变参数组合预测方法.最后,将变权重线性时变参数组合预测方法应用于新疆生产建设兵团城镇化发展水平的组合预测,实例结果表明变权重线性时变参数组合预测方法具有较高的拟合精度.     

Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads

This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008–2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.     

The Role of Posterior Densities in Latent Variable Models for Ordinal Data

In latent variable models, problems related to the integration of the likelihood function arise since analytical solutions do not exist. Laplace and Adaptive Gauss-Hermite (AGH) approximations have been discussed as good approximating methods. Their performance relies on the assumption of normality of the posterior density of the latent variables, but, in small samples, this is not necessarily assured. Here, we analyze how the shape of the posterior densities varies as function of the model parameters, and we investigate its influence on the performance of AGH and of the Laplace approximation.     

A Note on Goodness-of-Fit Test in Latent Variable Models with Categorical Variables

Assessing the goodness-of-fit of latent variable models for categorical data becomes a problem in presence of sparse data since the classical goodness-of-fit statistics are badly approximated by the chi square distribution. A good solution to this problem is represented by statistical tests based on the residuals associated to marginal distributions of the manifest variables (Cagnone and Mignani, 2007 Cagnone , S. , Mignani , S. ( 2007 ). Assessing the goodness-of-fit of a latent variable model for ordinal data . Metron LXV : 337 – 361 . [Google Scholar]; Maydeu-Olivares and Joe, 2005 Maydeu-Olivares , A. , Joe , H. ( 2005 ). Limited- and full-information estimation and goodness-of-fit testing in 2n contingency tables: A unified framework . J. Amer. Statist. Assoc. 100 ( 471 ): 1009 – 1020 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Reiser, 1996 Reiser , M. ( 1996 ). Analysis of residual for the multinomial item response model . Psychometrika 61 : 509 – 528 .[Crossref], [Web of Science ®] , [Google Scholar]). The quadratic form associated to the test involves the use of a generalized inverse of the covariance matrix of the sample proportions. In this article we prove that the rank of the Moore-Penrose generalized inverse is univocally determined and hence it can be used appropriately.     

潜在因素模型在商业银行信用风险分析中的应用

在评估商业银行整体信用风险时,债务人的信息一般不会传递到风险管理部门,导致在缺少违约数据时传统方法的分析十分复杂甚至难以进行.基于贝叶斯方法的潜在因素模型可以有效解决无法获得特定债务人信用质量的问题,并能够在宏观经济环境变动时准确评估违约风险强度变化,从而避免低估风险.利用MCMC模拟方法对商业银行数据的实证分析表明,潜在因素模型不仅推断方法及模拟途径简洁清晰,估计结果更加精确,而且在贝叶斯框架下具有较强的灵活性,适合在不同的数据约束条件下应用,便于国内风险分析人员采用.