利用稳定分布构建开放式基金业绩评价体系

经典的基金业绩评价方法是在收益率服从正态分布假设下构建风险指标，该指标或者高估风险，或者低估风险。可引入稳定分布来描述收益率，并利用稳定分布，构建风险调整的基金业绩评价体系，并利用该体系对我国开放式基金的业绩进行评价。     

汇率波动非对称效应的计量检验-基于非对称ARCH类模型

本文利用非对称ARCH类模型对1994年1月1日至2007年8月24日的人民币／美元的日汇率序列的波动特性进行了计量检验,检验结果表明在此期间汇率序列的波动存在显著的条件异方差性和波动过程的非对称性,利空消息引起的汇率波动大于利好消息引起的汇率波动。     

投资者导向的基金业绩评价指标体系

证券投资基金业绩评价指标体系研究在当前国际投资金融领域是非常前沿的课题,本文尝试从投资者角度宋研究中国证券投资基金业绩评价指标体系问题。如果离开投资者的关注及参与,证券投资基金业的生存及发展便成为空中楼阁及无源之水,再科学合理的基金业绩评价指标体系都将毫无意     

一类自融资条件下多期投资半方差组合模型及在中国证券市场中的实证分析

方差自问世以来,虽经历了种种讨论和质疑,但其主要焦点是方差法将正负离差不加区别的对待使得其在实际应用中无法区别买方和卖方,为此引入半方差的概念十分必要.对基于半方差风险计量模型进行了总结归纳,应用半方差模型对上海股市进行了实证分析,并对文中所提出的多时段半方差模型与多时段Markowitz模型进行了比较,验证了中国证券市场多时段半方差模型的有效性.     

基于SOM神经网络的基金业绩分类评价

证券投资基金在2005年证券市场持续低迷的情况下获得了可观的投资收益,使得越来越多的人开始关注投资基金。为了有效地保护投资者利益,避免信息不对称带来的逆向选择,同时给金融监管部门监管提供更可靠的依据,对证券投资基金进行科学合理的考核评估已势在必行。目前,基金业绩的     

玉米国内外价格非对称传递研究

文章运用惯性门槛自回归模型(M-TAR)和非对称误差修正模型(APT-ECM)对玉米国内外价格传递的非对称性进行了研究.研究结论:玉米国内外价格传递在长期和短期均是非对称的,具体地,玉米国内外价格向长期均衡调整时的调整速度是非对称的,而且玉米国内外价格存在滞后非对称效应.     

基于SVM的基金业绩评估系统研究

基于结构风险最小化原则的支持向量机对小样本决策具有全局收敛性和较好的学习推广性.基金业绩评价问题本质是分类问题,本文提出基于SVM的二叉树多级分类器实现方法,并对系统特征进行选择验证.实证分析表明采用支持向量机设计的评价系统思路清晰,操作简单,重复训练样本少,在评估系统应用中有较强的实用性.     

基于多元回归方法的基金业绩持续性影响因素分析

基金业绩持续性作为投资者选择投资对象的重要标准之一,对其进行研究具有重要的意义.文章基于统计指数法和多元回归方法对我国42只股票型开放式基金在2005～2007年三个年度内业绩持续性进行评价并对其影响因素进行了分析.     

国有企业经营者业绩评价指标体系的改进

随着信息时代的到来,企业经营管理的内容和重点都已有了重大的转变,传统对国有企业经营者业绩评价指标体系已经不能满足所有者评价的需要.文章针对国有企业经营者业绩评价体系方面存在的问题,提出完善国有企业经营者业绩评价体系的一些对策.     

基金业绩分析——基于有向学习网络的研究

本文使用LASSO算法构建了基于基金持股数据的基金间动态学习网络,将基金研究中传统的无向网络扩展为有向网络,并检验了正向学习与反向学习两种不同的学习模式(信息利用方式) 对基金业绩的影响,进而探讨了其背后的经济含义。实证结果表明:当基金作为被学习者(信息被观测方)时,被正向学习会显著提高其业绩,被反向学习会显著降低其业绩;当基金作为主动学习者(信息观测方)时,无论是正向学习还是反向学习均不会对其业绩造成显著影响;对基金学习动机的分析表明,基金参与学习是为了提升相对自己上期的业绩、防止业绩倒退,且反向学习相对更加有效。本文分析了信 息传递方向、信息利用方式对基金业绩的影响,为如何将统计学习方法应用于金融问题的分析提供了一个新的视角。     

Measurement error in regression analysis

Consider the linear regression model Y = Xθ+ ε where Y denotes a vector of n observations on the dependent variable, X is a known matrix, θ is a vector of parameters to be estimated and e is a random vector of uncorrelated errors. If X'X is nearly singular, that is if the smallest characteristic root of X'X s small then a small perurbation in the elements of X, such as due to measurement errors, induces considerable variation in the least squares estimate of θ. In this paper we examine for the asymptotic case when n is large the effect of perturbation with regard to the bias and mean squared error of the estimate.     

Performance of some new Liu parameters for the linear regression model

Abstract

This article introduces some Liu parameters in the linear regression model based on the work of Shukur, Månsson, and Sjölander. These methods of estimating the Liu parameter d increase the efficiency of Liu estimator. The comparison of proposed Liu parameters and available methods has done using Monte Carlo simulation and a real data set where the mean squared error, mean absolute error and interval estimation are considered as performance criterions. The simulation study shows that under certain conditions the proposed Liu parameters perform quite well as compared to the ordinary least squares estimator and other existing Liu parameters.     

中国开放式基金绩效的实证研究

文章通过实证分析得到中国开放式基金的收益、风险调整收益、基金经理人的时机和股票选择能力等方面的指标,并运用无量纲数据标准化处理方法和因子分析法进行综合评价。实证结果表明:中国的大部分开放式基金可以战胜市场,但没有足够证据显示中国的开放式基金经理人具有显著市场选择能力和证券选择能力。     

MSE performance of the weighted average estimators consisting of shrinkage estimators when each individual regression coefficient is estimated

In this paper, we analytically derive the exact formula for the mean squared error (MSE) of two weighted average (WA) estimators for each individual regression coefficient. Further, we execute numerical evaluations to investigate small sample properties of the WA estimators, and compare the MSE performance of the WA estimators with the other shrinkage estimators and the usual OLS estimator. Our numerical results show that (1) the WA estimators have smaller MSE than the other shrinkage estimators and the OLS estimator over a wide region of parameter space; (2) the range where the relative MSE of the WA estimator is smaller than that of the OLS estimator gets narrower as the number of explanatory variables k increases.     

Performance of the almost unbiased ridge-type principal component estimator in logistic regression model

ABSTRACT

This article considers some different parameter estimation methods in logistic regression model. In order to overcome multicollinearity, the almost unbiased ridge-type principal component estimator is proposed. The scalar mean squared error of the proposed estimator is derived and its properties are investigated. Finally, a numerical example and a simulation study are presented to show the performance of the proposed estimator.     

Some finite-sample-size properties of rosenblatt density estimates

The results of a Monte Carlo study of the sensitivity of Rosenblatt density estimates to the scale factor are presented.     

A note on the estimation of variance of sample mean Using the knowledge of coefficient of variation in normal population

A class of estimators for the variance of sample mean is defined and its properties are studied in case of normal population. It is identified that the usual unbiased estimator, Singh, Pandey and Hirano (1973) -type estimator and Lee (1931) estimator are particular members of the proposed class of estimators. It is found that the minimum Mean Squared Error (MSE) of the proposed class of estimators is less than that of other estimators.     

A new nonparametric estimator for the mean of the selected population

A modified bootstrap estimator of the mean of the population selected from two populations is proposed which is a convex combination of the two sample means, where the weights are random quantities. The estimator is shown to be strongly consistent. The small sample behavior of the estimator is investigated and compared with some competitors by means of Monte Carlo studies. It is found that the newly proposed estimator has smaller mean squared error for a wide range of parameter values.     

On the estimation of the marginal density of a moving average process

The authors present a new convolution‐type kernel estimator of the marginal density of an MA(1) process with general error distribution. They prove the √n; ‐consistency of the nonparametric estimator and give asymptotic expressions for the mean square and the integrated mean square error of some unobservable version of the estimator. An extension to MA(q) processes is presented in the case of the mean integrated square error. Finally, a simulation study shows the good practical behaviour of the estimator and the strong connection between the estimator and its unobservable version in terms of the choice of the bandwidth.     

A note on some conditions for optimality of certain ridge estimators

Consider the linear regression model, y = Xβ + ε in the usual notation with X'X being in the correlation form. Galpin(1980) claimed that the ridge estimators of Hoerl, Kennard and Baldwin(1975) and Lawless and Wang(1976) give guaranteed lower mean squared error than the least squares estimator when X'X has at least two very small eigen values. We show that the arguments of Galpin(1980) leading to the above claim are incorrect, and hence the claim itself is unsubstantited. A Monte Carlo study shows that Galpin's claim is not correct in general.