信息与投资组合

研究信息与均值-方差投资组合之间的关系.在L2框架和给定部分信息下,清楚地给出了均值-方差意义下的最优收益和有效前沿的表达式.为了刻画信息对投资组合的影响,引入了信息的风险-收益系数概念.信息的风险-收益系数随信息的增加而增加.投资者的最优收益的期望值和方差由其对应信息的风险-收益系数和风险厌恶参数共同决定.而市场的风险价格完全由信息的风险-收益系数确定.等价信息下最优风险资产组合的收益(在相差常数倍意义下)是唯一的.零信息下投资者仅投资于无风险资产.投资者对一无所知的风险资产不投资.     

基于收益序列相关的动态投资组合选择——动态均值-方差模型

以动态均值-方差模型研究基于收益序列相关的投资组合选择.利用嵌入法将动态均值-方差模型嵌入到二次效用模型当中,通过动态规划方法求得最优投资策略和有效边界的解析形式.还得到一个反映风险资产动态投资价值的指标,它能包涵序列相关性的影响,在静态情形下,该指标是夏普指数绝对值的单调递增光滑变换.风险资产投资价值影响最优投资,并决定有效边界斜率.最优投资策略使期望终期财富向目标值趋近,该目标值随风险资产投资价值递增.最后以收益服从AR(1)过程为例对最优投资策略进行数值计算,发现序列相关可以对最优策略造成显著影响.     

我国商品期货的无模型隐含方差与方差风险溢价研究——基于豆粕和白糖的分析

无模型隐含方差具有不依赖于期权定价模型的特点,它与已实现方差的差值度量了方差风险溢价,近年来在金融资产的方差风险溢价研究中备受关注.本文利用我国豆粕期货和白糖期货的期权数据估计出无模型隐含方差,然后在方差互换合约的框架下,深入研究商品期货的方差风险溢价.研究发现:我国豆粕期货和白糖期货的方差风险溢价显著为负,且具有时变性;经典的市场风险因子对豆粕期货和白糖期货的方差风险溢价的解释力度较小;两种商品期货的方差风险溢价与期货收益之间均存在非线性关系,并且方差风险溢价对未来一定期限内的资产收益具有预测作用.同时,本文还发现,我国豆粕期货和白糖期货与上证50 ETF的方差风险溢价存在较大差异,样本期内上证50 ETF的方差风险溢价不显著,而且对其未来收益无显著预测作用.     

辅助变量不完全情形下的回归插补及其方差估计

针对抽样调查中经常出现的无回答数据,在均匀回答机制下,讨论了辅助变量不完全情形下的回归插补,给出了目标变量总体均值的插补估计与其Jackknife方差估计,同时给出了该情形下回归插补推广到多元辅助变量的方法.     

联合均值与方差模型的变量选择

在许多应用方面, 特别在经济领域和工业产品的质量改进试验中, 非常有必要对方差建模. 推广经典的正态回归模型, 对联合均值与方差模型提出一种同时对均值模型和方差模型的变量选择方法. 提出的惩罚极大似然估计具有相合性和oracle性质. 随机模拟和实例研究结果表明该模型和方法是有用和有效的.     

线性预报误差时变方差补偿与语音增强

对常规的语音信号线性预报模型进行了推广 ,将线性预报误差方差考虑为时变的 ,这在一定程度上可对语音信号帧内非平稳特性进行补偿。基于对线性预报残差的短时 (2ms～ 4ms)特性分析 ,给出了线性预报系数及线性预报误差时变方差的迭代估计方法。将此模型应用于基于Kalman滤波的语音增强方法。仿真结果表明 ,这种线性预报误差时变方差补偿可以提高语音增强的信噪比     

收益率为模糊数的加权证券组合选择模型

Markowitz基于概率理论建立了有名的均值方差证券组合模型，文章则基于模糊理论建立了一类具有权系数的均值方差证券组合模型，首先对证券市场上的收益与风险特性重新进行度量和刻画，提出了一类新的具有加权的可能性均值、方差及协方差的概念，类似于概率论中均值方差的分析讨论了这些概念的性质．其次基于该文定义的均值方差，建立了以收益率为模糊数的加权可能性证券组合投资模型，并给出了相应的加权可能性有效证券组合及有效前沿概念，通过求解两个相对应的优化模型得到了一个具有带状投资区域的有效前沿．尤其当资产收益率具有线性或分段线性隶属函数的模糊数时，该证券组合选择模型实质上为一个线性规划问题，因此有效前沿可化为一个具有折线段的带状投资区域。     

基于Copula-GARCH-EVT的资产组合选择模型及其混合遗传算法

在非正态分布的条件下,M arkow itz的均值-方差资产组合选择模型存在不足。为此,以V aR和CV aR作为风险度量方法,EVT反映收益率的尾部分布,GARCH反映收益率的波动性,Copu la函数反映金融资产收益的相关性,构建了基于Copu la函数的资产组合选择模型。针对非正态分布条件下V aR非凸性和分布函数不连续性导致资产组合选择优化计算复杂、不精确的难题,设计了基于单纯形和传统遗传算法的混合遗传算法。最后,根据中国证券市场数据,采用该混合遗传算法对建立的资产组合选择模型求解。     

直觉梯形模糊群决策的可能性均值方差方法

研究方案属性值为直觉梯形模糊数(ITFN)多属性决策群问题,提出了一种基于可能性均值-方差的决策方法.首先定义了ITFN新的合理运算法则,引入了ITFN的可能性均值、方差及其指标值的概念,根据可能性均值和方差指标给出了ITFN新的排序方法.通过构建线性目标规划模型求解得到方案的群体综合属性值,进而给出群决策结果.实例分析验证了方法的有效性.     

软件可靠性模型的变点估计及仿真研究

软件可靠性模型是研究软件失效数据的重要工具,对软件产品质量的度量和评估有重要的意义.为了解决单个模型不能准确描述软件失效情况以及难以对未来的软件失效做出合理预测的问题,将变点分析引入到软件可靠性模型的问题研究,建立了有变点的软件可靠性模型,给出了软件可靠性模型变点的最小二乘估计.数值仿真表明最小二乘法适用于软件可靠性模型的变点估计,其估计结果具有好的准确性和稳健性.     

Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data

This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric exponential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distribution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.     

Variable selection in joint location,scale and skewness models of the skew-normal distribution

Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and (or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes, and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.     

Adaptive trimmed mean as a location estimate

The trimmed mean is one of the most common estimators of location for symmetrical distributions,whose effect depends on whether the trim rate matches the proportion of contaminated data.Based on the geometric characteristics of the curve of the trimmed variance function,the authors propose two kinds of adaptive trimmed mean algorithms.The accuracy of the estimators is compared with that of other often-used estimates,such as sample mean,trimmed mean,trimean,and median,by means of simulation method.The results show that the accuracy of the adaptive derivative optimization trimmed mean method is close to the optimum performance in case of medium contamination(the contamination rate is less than 50%).Under high contamination situation(the contamination rate equals 80%),the performance of the estimates is comparable to that of the median and is superior to other counterparts.     

Two-step estimators in partial linear models with missing response variables and error-prone covariates

A partial linear model with missing response variables and error-prone covariates is considered. The imputation approach is developed to estimate the regression coefficients and the nonparametric function. The proposed parametric estimators are shown to be asymptotically normal, and the estimators for the nonparametric part are proved to converge at an optimal rate. To construct confidence regions for the regression coefficients and the nonparametric function, respectively, the authors also propose the empirical-likelihood-based statistics and investigate the limit distributions of the empirical likelihood ratios. The simulation study is conducted to compare the finite sample behavior for the proposed estimators. An application to an AIDS dataset is illustrated.     

Multidimensional credibility estimators with random common effects and time effects

In this paper, multidimensional credibility model with a type of dependence structures over risks and over time is considered. By means of the projection method, the inhomogeneous and homogeneous Bhlmann credibility estimators are obtained, which are extended to slightly more general versions. The inhomogeneous estimator can be expressed as the weighted sum of individual mean,overall sample mean and collective mean. In addition, the estimations of structural parameters are also investigated.     

小波域局部贝叶斯阈值的SAR图像斑点抑制算法

提出了一种抑制SAR图像斑点噪声的小波域贝叶斯软阈值方法。该算法不同于有偏的去除乘性噪声的同态滤波算法,而是将噪声转化为局部平稳的加性白噪声。在非下采样小波子带上,视数给定时该算法可以简洁有效地估计局部加性噪声方差。在实验中,该算法同Kuan,Lee和Argenti等算法作了比较,结果表明,在常用性能指标上所提算法优于其它算法。     

基于小波变换的时变长记忆SV模型估计方法研究

研究了基于小波变换的时变长记忆SV模型参数的估计方法.根据小波变换可将过程分解到不同的尺度上以及长记忆SV过程同一尺度下和不同尺度下DWT系数的近似不相关性,提出了建立局部似然函数的方法,又根据DWT系数和MODWT系数之间的关系,将局部似然函数表示为模型参数和局部小波方差估计的形式.用该方法对中国股市收益进行了时变长记忆SV模型参数的估计.     

风险度量半参数变系数复合Expectile回归模型及应用

本文结合半参数变系数回归模型、期望分位数风险价值(EVaR)的思想以及充分利用多个Expectile信息能提高参数估计效率的假设,提出了一类半参数变系数复合Expectile回归模型,并对该模型进行了估计,建立了所提出复合Expectile回归(CER)估计的大样本性质.针对该模型既含有参数部分也含有非参数部分的特征,采用了方便计算的三步估计方法.通过数值模拟也发现,当误差为厚尾或非对称分布时,在均方根误差(RMSE)的标准下,所提出的CER估计大大优于最小二乘(LS)估计和简单的Expectile回归(ER)估计.另外,本文还应用所发展的理论分析了我国货币政策对上证综指的影响.     

威布尔部件的经验贝叶斯评估

在逐步增加Ⅱ型截尾寿命试验下, 讨论了威布尔部件可靠性指标的估计及性质. 基于Linex损失函数, 给出了威布尔部件寿命分布参数、可靠度函数及失效率函数的一致最小方差无偏估计、贝叶斯估计及经验贝叶斯估计, 并证明了经验贝叶斯估计的渐进最优性. 最后运用Monte-Carlo方法对各种估计 的均方误差进行了模拟比较. 结果表明, 经验贝叶斯估计精度高.     

Heteroscedasticity Detection and Estimation with Quantile Difference Method

When dealing with regression analysis, heteroscedasticity is a problem that the authors have to face with. Especially if little information can be got in advance, detection of heteroscedasticity as well as estimation of statistical models could be even more difficult. To this end, this paper proposes a quantile difference method (QDM) that can effectively estimate the heteroscedastic function. This method, being completely free from the estimation of mean regression function, is simple, robust and easy to implement. Moreover, the QDM method enables the detection of heteroscedasticity without any restrictions on error terms, consequently being widely applied. What is worth mentioning is that based on the proposed approach estimators of both mean regression function and heteroscedastic function can be obtained. In the end, the authors conduct some simulations to examine the performance of the proposed methods and use a real data to make an illustration.