沪、深股市收益率风险的极值VaR测度研究

资产组合的ＶａＲ是指在某一置信水平下 ,该资产组合可能遭受的最大损失 ,或者说 ,ＶａＲ即资产组合收益率损失分布函数的某一分位点。由于传统的方差 协方差法、历史模拟法、蒙特卡洛模拟法在估计金融资产收益率的ＶａＲ值上的低效 ①,ＦｒａｎｃｏｉｓＬｏｎｇｉｎ等学者便将统计极值理论引入到估测ＶａＲ的研究中来 ② 。本文将运用近年发展起来的这些极值ＶａＲ方法来测度沪、深股市收益率的市场风险 ,同时也对极值方法、半参数方法 ③ 以及传统ＶａＲ方法的测度效果进行比较。　　一、估测ＶａＲ的极值方法通常有两类估测ＶａＲ的极值…     

基于GARCH和半参数法的VaR模型及其在中国股市风险分析中的应用

一、引言ＶａＲ是风险估值模型 (ＶａｌｕｅＡｔＲｉｓｋ)的简称 ,是近年来国外兴起的一种金融风险管理工具 ,旨在估计给定金融产品或组合在未来资产价格波动下可能的或潜在的损失。用Ｊｏｒｉｏｎ( 1996)给出的权威定义 ,可将其表述为“给定置信区间的一个持有期内的最坏的预期损失” ,即在一定的持有期和一定的置信度内 ,某金融工具和投资组合所面临的潜在的最大损失金额。目前 ,该方法已经得到国际金融界越来越多的重视和应用 ,部分国家已明文规定银行、上市公司、证券公司等机构必须定期公布自己的ＶａＲ值。ＶａＲ的意义在于 ,它不仅可…     

基于CVaR风险度量的投资组合优化决策

投资组合优化问题依赖于风险度量方法和投资组合收益率分布函数的选取。针对收益率通常不服从多元正态分布以及均值—方差模型低估了投资组合发生重大损失的风险,文章利用多元广义双曲线分布来拟合投资组合收益率,从而更加灵活地捕捉收益率数据的偏态和尖峰厚尾特征;使用CVaR代替方差和VaR来度量金融资产重大损失风险,进而建立均值—CVaR投资优化模型。实证研究结果表明,相对于均值—方差模型,均值—CVaR能够更好地反映投资组合收益率分布,提高投资者控制投资风险的能力。     

金融风险管理M-V方法的资产组合灵敏度分析

文章基于均值-方差(M-V)风险计量技术,分析了资产组合在资产品种减少的敏感性,通过引入协方差扰动,建立相关的M-V组合模型。同时和原模型进行比较,分析有效前沿的变化,探究其经济意义。     

VaR约束下的资产配置决策模型

在现代投资组合理论(Markowitz,1952)中,投资者通过对各种资产的有效配置以达到风险分散并获取最大的投资回报,并利用不得买空及要求必要回报率的条件下,将投资组合风险最小化,并据此形成有效前缘。为更精确度量风险的回报能力,随后Sharpe(1966,1975)利用均值─方差法(M-V)的框     

基于不确定性均值-方差模型的稳健静态资产组合选择

文章借助相对熵测度风险资产收益的一阶矩和前两阶矩的不确定性对资产组合终期财富期望效用的影响,基于极大极小化理论建立了模型参数不确定下的稳健静态资产组合模型,运用稳健控制方法获得了模型的最优解;根据最优解,以上证综指1997年1月至2009年6月的月收益数据构建了两个不同区间段的样本做实证研究。结果表明,参数不确定性导致资产组合中风险资产的比例降低,并随着投资者不确定性偏好程度增加降低得越多;历史数据或信息越少,参数不确定性影响越强;均值不确定性的影响强于方差不确定性的影响;即使投资者完全不相信方差的预测功能,但仍在一定程度上相信均值的预测功能。     

投资组合选择的均值-风险价值模型研究

文章以风险价值(Value at risk,VaR)作为风险度量,建立了投资组合选择的均值-风险价值模型.在资产收益率服从联合正态分布的假设下,研究了置信水平对最优组合和有效边缘的影响,同时分析了持有期对它们的影响,给出了全局最小VaR组合存在时置信水平和持有期的阈值,从而得到了有效边缘存在的条件.进一步研究了全局最小VaR组合与全局最小方差组合的关系,结果表明如果全局最小VaR组合存在,则一定位于均值-方差有效边缘上,且位于全局最小方差组合的上方.     

资产数量减少情形下CVaR投资组合的灵敏度分析

文章基于CVaR的风险度量技术,考察了当投资组合的资产数量减少时投资组合的均值-CVaR有效边缘,探究了其经济含义,并与方差风险下的均值-方差边界进行了对比研究。我们发现,使用CVaR风险度量标准可以使投资者在资产选择时更加稳健,同时也有利于对风险进行分散和监管。     

基于风险控制的证券投资决策

Markowitz证券组合核心理论是在投资决策中选择风险(用方差或标准差描述)最小、期望收益(用均值描述)最大的证券组合,即依据均值-方差模型来构造最优证券投资组合.Markowitz的均值-方差模型给出了投资决策的最基本、最完整的分析框架,是当今投资理论和投资实践的主流方法.     

均值-CVaR投资组合有效边缘的灵敏度分析

文章基于CVaR的风险度量技术,考察了当投资组合的资产数量增加时投资组合的均值-CVaR有效边缘.探究了其经济含义,并与方差风险下的均值一方差边界进行了对比研究.可以发现,使用CVaR风险度量标准可以使投资者在资产选择时更加稳健,同时也有利于对风险进行分散和监管.     

城市基本公共服务面向农民工开放度测量研究

2016年1月起实施的《居住证暂行条例》从根本上摒弃了农民工享有城市基本公共服务的制度桎梏，但农民工实际所能享有的水平尚取决于城市政府开放其公共服务的程度，包括“承诺”服务的领域多少和所设置的约束“条件”高低。本文以各城市居住证管理制度文件为依据，运用全国流动人口调查数据，测量城市基本公共服务面向农民工群体的开放度。结果显示：（1）城市基本公共服务全部领域超过一半面向农民工开放，开放度0.5076。即便在排他性服务领域，也超过1/3面向农民工开放，开放度达到0.3569，农民工开始实质性享有城市基本公共服务，但离平等权利仍有距离；（2）开放度在区域间的分布表现为东部城市低于中部城市更低于西部城市、直辖市低于省会城市更低于其他地级市，珠三角、长三角和京津冀城市低于三大经济圈外城市。相对发达的城市开放度更低，反映出开放度高低主要取决于城市政府的开放意愿而非提供服务的能力；（3）开放限制主要来源于“条件”约束，城市政府在排他性服务领域的承诺度0.7703，承诺服务的领域超过3/4。但约束度达0.4502，城市政府只向其中54.98%能满足约束条件的农民工开放所承诺的服务，源自约束条件的限制占到开放限制的60%以上。城市政府遵循居住证制度规范面向外来人口统一“承诺”服务，但通过“条件”约束选择性地限制了农民工获得城市基本公共服务的机会，农民工平等权利的最终实现尚需要政策制度的系统性配套改革。     

大数据背景下非概率抽样的统计推断问题

利用大数据进行抽样,很多情况下抽样框的构造比较困难,使得抽取的样本属于非概率样本,难以将传统的抽样推断理论应用到非概率样本中,如何解决非概率抽样的统计推断问题,是大数据背景下抽样调查面临的严重挑战。本文提出了解决非概率抽样统计推断问题的基本思路：一是抽样方法,可以考虑基于样本匹配的样本选择、链接跟踪抽样方法等,使得到的非概率样本近似于概率样本,从而可采用概率样本的统计推断理论;二是权数的构造与调整,可以考虑基于伪设计、模型和倾向得分等方法得到类似于概率样本的基础权数;三是估计,可以考虑基于伪设计、模型和贝叶斯的混合概率估计。最后,以基于样本匹配的样本选择为例探讨了具体解决方法。     

我国35个大中城市房价-持久收入比研究

房价收入比是反映居民购房可支付能力的重要指标。为了更真实准确反映我国居民长期承受的购房负担,本文基于持久收入假说,利用我国35个大中城市数据,建立状态空间模型,估计出持久收入意义下的房价收入比,通过情景分析确定其合理上限,并分析主要特征。研究表明：①我国房价-持久收入比的合理上限为7.6,2002-2013年35个大中城市的房价-持久收入比均值是9.2,其中28个城市已超过该上限,比值最高的北京已达到14.9;②城市越发达,则房价-持久收入比越高,居民需要承受的购房压力越大,且在时间趋势上,一线与二三线城市间的差距呈现扩大趋势;③在地理分布上,我国东部、中部、东北地区、西部大中城市的房价-持久收入比呈现从高到低的排列顺序;④相比于传统方法中根据可支配收入测算的房价收入比,房价-持久收入比与其约有10%的差异。当前我国政府应采取有效措施继续限制房价,减轻居民购房负担。     

Zenga Distribution and Inequality Ordering

The aim of this article is to establish an ordering related to the inequality for the recently introduced Zenga distribution. In addition to the well-known order based on the Lorenz curve, the order based on I(p) curve is considered. Since the Zenga distribution seems to be suitable to model wealth, financial, actuarial, and, especially, income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. This investigation shows that for the Zenga distribution, two of the three parameters are inequality indicators.     

Modelling two types of heterogeneity in the analysis of student success

ABSTRACT

Student dropout is a worldwide problem, leading private and public universities in developed and underdeveloped countries to study the subject carefully or, as has recently been done, to analyse what drives student success. On this matter, different approaches are used to obtain useful information for decision-making. We propose a model that considers the enrolment date to the dropout or graduation date and also covariates to measure student success rates, to identify what the academic and non-academic factors are, and how they drive the student success. Our proposal assumes that there is one part of the population who is not at risk of dropping out, and that the part of the population at risk is heterogeneous, that is, we assume two types of heterogeneity. We highlight two advantages of our model: one is to identify the period of higher risk to dropout due to considering the academic survival time and the second is due to the inclusion of covariates that enable us to identify the characteristics linked to dropout. In this research, we also demonstrate the identifiability of the model and describe the estimation procedures. To exemplify the applicability of the approach, we use two real datasets.     

Heteroscedastic Regression Models and Applications to Off-line Quality Control

We discuss in the present paper the analysis of heteroscedastic regression models and their applications to off-line quality control problems. It is well known that the method of pseudo-likelihood is usually preferred to full maximum likelihood since estimators of the parameters in the regression function obtained are more robust to misspecification of the variance function. Despite its popularity, however, existing theoretical results are difficult to apply and are of limited use in many applications. Using more recent results in estimating equations, we obtain an efficient algorithm for computing the pseudo-likelihood estimator with desirable convergence properties and also derive simple, explicit and easy to apply asymptotic results. These results are used to look in detail at variance minimization in off-line quality control, yielding techniques of inferences for the optimized design parameter. In application of some existing approaches to off-line quality control, such as the dual response methodology, rigorous statistical inference techniques are scarce and difficult to obtain. An example of off-line quality control is presented to discuss the practical aspects involved in the application of the results obtained and to address issues such as data transformation, model building and the optimization of design parameters. The analysis shows very encouraging results, and is seen to be able to unveil some important information not found in previous analyses.     

A critical review of graphics for subgroup analyses in clinical trials

Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.     

Analysis of the importance of on-field covariates in the German Bundesliga

In modern football, various variables as, for example, the distance a team runs or its percentage of ball possession, are collected throughout a match. However, there is a lack of methods to make use of these on-field variables simultaneously and to connect them with the final result of the match. This paper considers data from the German Bundesliga season 2015/2016. The objective is to identify the on-field variables that are connected to the sportive success or failure of the single teams. An extended Bradley–Terry model for football matches is proposed that is able to take into account on-field covariates. Penalty terms are used to reduce the complexity of the model and to find clusters of teams with equal covariate effects. The model identifies the running distance to be the on-field covariate that is most strongly connected to the match outcome.     

A new class of survival regression models with heavy-tailed errors: robustness and diagnostics

Birnbaum-Saunders models have largely been applied in material fatigue studies and reliability analyses to relate the total time until failure with some type of cumulative damage. In many problems related to the medical field, such as chronic cardiac diseases and different types of cancer, a cumulative damage caused by several risk factors might cause some degradation that leads to a fatigue process. In these cases, BS models can be suitable for describing the propagation lifetime. However, since the cumulative damage is assumed to be normally distributed in the BS distribution, the parameter estimates from this model can be sensitive to outlying observations. In order to attenuate this influence, we present in this paper BS models, in which a Student-t distribution is assumed to explain the cumulative damage. In particular, we show that the maximum likelihood estimates of the Student-t log-BS models attribute smaller weights to outlying observations, which produce robust parameter estimates. Also, some inferential results are presented. In addition, based on local influence and deviance component and martingale-type residuals, a diagnostics analysis is derived. Finally, a motivating example from the medical field is analyzed using log-BS regression models. Since the parameter estimates appear to be very sensitive to outlying and influential observations, the Student-t log-BS regression model should attenuate such influences. The model checking methodologies developed in this paper are used to compare the fitted models.     

Electronic Journals: Cataloging and Management Practices in Academic Libraries

The growth and availability of electronic journals offer libraries the opportunity to provide end users with quick and easy access to more journals than ever before, thereby creating a complex new workload in academic libraries. Libraries have addressed the evolving challenges unique to electronic resources by creating new policies and workflows and dedicating staff to work on the processes, despite the lack of best practices. In the fall of 2009, a survey was distributed to ninety-five libraries at peer institutions to gather information about their policies and practices for cataloging and managing electronic journals in order to gauge the current status of electronic journal management among these peer institutions. This paper reports on the survey findings related to cataloging approach, sources for bibliographic records, methods for identifying problems, and the staff and staff hours dedicated to electronic journals.