基于线性规划和多项式样条函数的利率期限结构模型

针对线形规划利率期限结构模型只能得到离散贴现率,提出利用多项式插值方法拟合出连续光滑的利率期限结构.并依据样本内外国债信息把它与多项式样条函数模型进行了实证比较,前者的价格相对误差分别为0.45%和1.51%,后者分别为4.18%和4.5%.结果表明线性规划利率期限结构模型与多项式插值相结合的方法在拟合我国利率期限结构方面具有一定优势.     

基于NS混合模型的中国利率期限结构动态估计比较研究

采用NS混合模型动态估计中国利率期限结构,考察动态NS模型,无套利NS模型及广义无套利NS模型等NS混合模型对我国利率期限结构的动态估计效率,比较NS混合模型的样本外预测能力,检验无套利约束对混合模型动态估计的影响.本文的经验分析结果表明:无套利条件的引入增强了NS混合模型的样本内动态估计能力和样本外预测能力;五因素的广义无套利NS模型(AFGNS)无论在利率期限结构样本内动态估计还是在总体预测效率上都要高于其他模型,可将其作为利率期限结构研究的基础模型:     

基于预期理论的Shibor期限结构实证研究

本文基于利率期限结构预期理论对我国的Shibor市场进行了实证研究。本文回顾了利率期限结构预期理论的三种检验方法,通过单位根检验发现Shibor短端利率平稳、中长端利率存在单位根,并分别运用线性回归法、向量自回归法和协整检验法对Shibor整体、短端利率和中长端利率相应进行了实证检验,得出Shibor无论整体上还是短端利率或中长端利率都不支持预期理论成立的结论,并通过分析得出启示:Shibor应注重中长端利率的发展和报价制度的完善。     

考虑流动性的我国国债价格预测研究

随着金融改革的深化和利率市场化脚步的加快,我国的国债交易和国债市场已经得到了高速发展和充分成长.但在国债利率期限结构的研究方面还不够充分,仍有进一步完善的空间,在利率期限结构研究中考虑流动性的影响就是其中之一.从利率期限结构估计入手,将流动性以权重形式加入NSS模型,估计参数并预测国债价格.研究结果表明,加入流动性权重后,利率期限结构的预测性能显著提高,而且随着步长加大,效果更明显.     

利率期限结构与宏观经济变量的相互关系研究

本文研究了利率期限结构与宏观经济变量之间的相互关系。运用利率期限结构与宏观经济变量的无套利模型,对向量自回归模型进行了扩展,将其引入到状态空间模型框架中,基于卡尔曼滤波并结合EM算法对模型参数进行了有效估计,结合实际数据对利率期限结构与宏观经济变量的相互影响关系进行了实证研究。结果表明:利率期限结构与宏观经济变量的双向影响关系显著;宏观经济变量对利率期限结构具有一定的解释力;研究利率期限结构时,宏观经济变量的影响作用不能忽略。     

关于货币市场结构转换利率期限结构的实证研究

基本的利率期限结构模型均未能将结构转换效应考虑进来,因此为了探讨结构转换架构下利率期限结构模型的特性,本文在中国货币市场利率数据的基础上对基本利率期限结构模型和结构转换利率期限结构模型进行了比较研究,结果发现中国货币市场利率动态中存在明显的结构转换效应,且在结构转换效应中其本身也存在着不稳定性,这充分反映了中国货币市场在发展过程中的不成熟特征.     

中国债券市场动态利率模型及其实证检验

为了对中国债券市场动态利率期限结构进行深入的研究,本文基于状态空间模型和卡尔曼滤波技术构建了中国债券市场动态利率模型。本文模型根据数据的可观测结构进行建模,通过迭代计算寻找不可观测状态变量的最优估计值和隐含参数,很好地解决了传统计量方法中因为变量不可观测而无法获得真实数据所带来的研究困难。同时通过模型有效性的模拟实验和中国债券市场同业拆借利率的实证研究,证明了模型对利率期限结构在一段时间内的动态变化估计结果准确,在建模样本期内利率的动态变化能够得到有效的分析和预测。本文的研究为中国债券市场动态利率管理和定价问题提供了新的思路和可能的解决渠道。     

基于Hull-White模型的债券市场利率期限结构研究

现代利率研究中有许多理论和模型对利率期限结构问题进行探索，但是在中国还没有一种公认的理论或方法能够完全解决中国债券市场利率期限结构问题。本文尝试寻找一种更多的利用市场即时信息的定价方法对利率期限结构进行研究，应用三叉树模拟技术构建Hull-White模型，并对当前中国债券市场上几种常用利率进行比较分析。研究发现银行间质押式回购收益率具有较好的动态运动性质，比样本国债和政策性银行金融债更适宜作为短期金融产品定价的基础。     

运用非均匀三次B样条拟合我国国债利率期限结构

运用非均匀三次B样条作为拟合函数,以我国上海证券交易所附息国债的日价格为数据,拟合了我国国债的利率期限结构.实证结果显示,使用"等额现金量法"来确定样条节点向量,所拟合出的我国国债利率期限结构符合经济原理,且对债券价格的估计误差较小.     

利率期限结构的B-样条校准法

瞬时远期利率曲线是利率期限结构的重要表现形式.本文介绍了如何应用B样条方法及序列二次规划算法,根据市场利率产品的报价,快速准确地拟合出远期利率曲线.不同于常用的Bootstrapping方法,我们的方法所产生的曲线满足利率期限结构所要求具有的光滑性.最后作为一个实际应用,本文使用欧元市场数据说明了我们方法的具体应用.     

Time series of functional data with application to yield curves

We develop time series analysis of functional data observed discretely, treating the whole curve as a random realization from a distribution on functions that evolve over time. The method consists of principal components analysis of functional data and subsequently modeling the principal component scores as vector autoregressive moving averag (VARMA) process. We justify the method by showing that an underlying ARMAH structure of the curves leads to a VARMA structure on the principal component scores. We derive asymptotic properties of the estimators, fits, and forecast. For term structures of interest rates, these provide a unified framework for studying the time and maturity components of interest rates under one setup with few parametric assumptions. We apply the method to the yield curves of USA and India. We compare our forecasts to the parametric model that is based on Nelson‐Siegel curves. In another application, we study the dependence of long term interest rate on the short term interest rate using functional regression.     

学生成绩的学分绩与主成分排序模型

利用多元统计中的主成分分析研究学生成绩,发现第一主成分排序与学分绩排序结果基本相同,提出用第一主成分代替学分绩对学生进行综合评价更加合理.而且主成分还能反映教学过程中的优点和不足.对教学有一定的指导意义.     

A Penalized Likelihood Approach to Rotation of Principal Components

A new paradigm for enhancing the interpretability of principal components through rotation is presented within the framework of penalized likelihood. The rotated components are computed as the maximizers of a Gaussian-based profile log-likelihood function plus a penalty term defined by a standard rotation criterion. This method enjoys a number of advantages over other methods for principal component rotation, notably (1) the rotation specifically targets ill-defined principal components, which may benefit the most from rotation, and (2) the connection with likelihood allows assessment of the fidelity of the rotated components to the data, thereby guiding the choice of penalty parameter. The method is illustrated with an application to a small functional dataset. Efficient computation of the penalized likelihood solution is possible using recently developed algorithms for optimization under orthogonality constraints.     

基于主成分回归模型的经济增长因素分析

在经济增长因素分析中,常用多元回归分析方法,但有时建立的回归模型拟合效果不好或不合理。为此本文给出建立主成分回归分析的方法。本文对经济增长给出两种回归分析方法,即建立主成分线性回归模型,分析经济增长的边际效应,建立主成分非线性回归模型,分析经济增长的弹性效应,实例表明效果很好。     

A principal component method to impute missing values for mixed data

We propose a new method to impute missing values in mixed data sets. It is based on a principal component method, the factorial analysis for mixed data, which balances the influence of all the variables that are continuous and categorical in the construction of the principal components. Because the imputation uses the principal axes and components, the prediction of the missing values is based on the similarity between individuals and on the relationships between variables. The properties of the method are illustrated via simulations and the quality of the imputation is assessed using real data sets. The method is compared to a recent method (Stekhoven and Buhlmann Bioinformatics 28:113–118, 2011) based on random forest and shows better performance especially for the imputation of categorical variables and situations with highly linear relationships between continuous variables.     

稳健稀疏主成分分析法及其实证研究

主成分分析是多元统计分析中一种非常经典的降维技术。然而,经典主成分分析却是对离群值非常敏感的,常因离群值的存在导致结果与实际不相符。另一方面,当主成分分析用于综合评价时,主成分的含义常因载荷间绝对值大小不分明而含糊不清,从而导致综合评价难以展开。本文通过使用稳健稀疏主成分分析法进行模拟实验和实证分析,结果表明:该方法不仅能很好地抵抗离群值的影响,而且还能准确地识别出离群样本。通过该方法得出的主成分的含义也较经典主成分分析和稳健主成分分析更加地明确和贴近实际。     

Principal component histograms from interval-valued observations

The focus of this paper is to propose an approach to construct histogram values for the principal components of interval-valued observations. Le-Rademacher and Billard (J Comput Graph Stat 21:413–432, 2012) show that for a principal component analysis on interval-valued observations, the resulting observations in principal component space are polytopes formed by the convex hulls of linearly transformed vertices of the observed hyper-rectangles. In this paper, we propose an algorithm to translate these polytopes into histogram-valued data to provide numerical values for the principal components to be used as input in further analysis. Other existing methods of principal component analysis for interval-valued data construct the principal components, themselves, as intervals which implicitly assume that all values within an observation are uniformly distributed along the principal components axes. However, this assumption is only true in special cases where the variables in the dataset are mutually uncorrelated. Representation of the principal components as histogram values proposed herein more accurately reflects the variation in the internal structure of the observations in a principal component space. As a consequence, subsequent analyses using histogram-valued principal components as input result in improved accuracy.     

Sparse principal components by semi-partition clustering

A cluster-based method for constructing sparse principal components is proposed. The method initially forms clusters of variables, using a new clustering approach called the semi-partition, in two steps. First, the variables are ordered sequentially according to a criterion involving the correlations between variables. Then, the ordered variables are split into two parts based on their generalized variance. The first group of variables becomes an output cluster, while the second one—input for another run of the sequential process. After the optimal clusters have been formed, sparse components are constructed from the singular value decomposition of the data matrices of each cluster. The method is applied to simple data sets with smaller number of variables (p) than observations (n), as well as large gene expression data sets with p ? n. The resulting cluster-based sparse principal components are very promising as evaluated by objective criteria. The method is also compared with other existing approaches and is found to perform well.     

基于主成分-SFA的银行投入产出效率评价模型及实证

通过测算贷款、存款等投入要素对净利息收入的贡献,评价商业银行的投入产出效率,对银行的资本运营和监管机构的银行资本监管具有重要意义.原始投入变量过多和变量之间的高度相关都会对评价模型的估计和检验产生影响.创新和特色在于:一是通过提取互不相关的2个主成分,反映6个原始投入变量95%以上的信息.建立基于主成分的SFA模型,克服变量过多和变量高度相关对模型参数估计和检验的影响,解决原始投入变量高度相关导致的系数检验不显著和符号不正确问题.二是利用主成分回归,将主成分与投入变量的关系表达式代入基于主成分的SFA模型,进而确定投入变量的权重系数,建立银行的投入产出模型,反映6个投入变量对净利息收入的影响规律.实证研究结果表明:一是利用主成分建立的SFA模型系数检验显著,技术效率随时间增加.二是利息支出、贷款余额、总资产、存款总额、固定资产和员工人数产出弹性分别为0.287,0.272,0.254,0.086,0.072和0.053.因此影响银行净利息收入的主要因素为利息支出、贷款余额、总资产.存款总额、固定资产和员工人数对净利息收入的影响较小.三是18家商业银行的规模系数为1.025,银行的净利息收入表现出规模经济特征.     

A measure of skewness and kurtosis and a graphical method for assessing multivariate normality

Using principal components, a measure of skewness and kurtosis is developed for multivariate populations. The sample analogues of these measures are proposed as tests of multivariate normality. Also, a graphical method is presented for assessing multivariate normality.