Unbiased estimation of expected return using CAPM

Estimation of expected return is required for many financial decisions. For example, an estimate for cost of capital is required for capital budgeting and cost of equity estimates are needed for performance evaluation based on measures such as EVA. Estimates for expected return are often based on the Capital Asset Pricing Model (CAPM), which states that expected excess return (expected return minus the risk-free rate) is equal to the asset's sensitivity to the world market portfolio (β) times the risk premium on the “world market portfolio” (the market risk premium). Since the world market portfolio, by definition, contains all assets in the world, it is not observable. As a result, an estimate for expected return is commonly obtained by taking an estimate for β based on some index (as a proxy for the world market portfolio) and an estimate for the market risk premium based on a potentially different index and multiplying them together. In this paper, it is shown that this results in a biased estimate for expected return. This is undesirable since biased estimates lead to misallocation of funds and biased performance measures. It is also shown in this paper that the straightforward procedure suggested by Fama and MacBeth [J. Financ. Econ. 1 (1974) 43] results in an unbiased estimate for expected return. Further from the analysis done, it follows that, for an unbiased estimate, it does not matter what proxy is used, as long as it is used correctly an unbiased estimate for expected return results.     

Density estimation in the uniform deconvolution model

We consider the problem of estimating a probability density function based on data that are corrupted by noise from a uniform distribution. The (nonparametric) maximum likelihood estimator for the corresponding distribution function is well defined. For the density function this is not the case. We study two nonparametric estimators for this density. The first is a type of kernel density estimate based on the empirical distribution function of the observable data. The second is a kernel density estimate based on the MLE of the distribution function of the unobservable (uncorrupted) data.     

Nonparametric estimation of distribution functions

Summary Using Silverman and Young’s (1987) idea of rescaling a rescaled smoothed empirical distribution function is defined and investigated when the smoothing parameter depends on the data. The rescaled smoothed estimator is shown to be often better than the commonly used ordinary smoothed estimator.     

供应链管理绩效评价及激励机制

本文通过分析供应链管理的特点、目标和内容，提出供应链管理绩效评价指标体系，其中矩阵指标具有一定的创新，且较好地反映了供应链企业之间的复杂关系。介绍了确定供应链管理的绩效指标值的两种方法，并分析了供应链管理六种激励模式。     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.     

搞好《建筑工程概预算》课程教学的思考

根据《建筑工程概预算》课程的特点,结合教学实践经验,对如何搞好该课程的教学做了一些思考：强调课程重要性,使学生明确课程学习的意义;精心挑选教材,备足教学参考资料;注重培养学生分析、解决问题的能力和自学能力;开展课程设计教学,提高教学质量;充分发挥教师的主导作用,培养“双师型”的教师;教学中理论联系实际,支持、鼓励学生参加执业资格证考试。     

A semiparametric GARCH model for foreign exchange volatility

A semiparametric extension of the GJR model (Glosten et al., 1993. Journal of Finance 48, 1779–1801) is proposed for the volatility of foreign exchange returns. Under reasonable assumptions, asymptotic normal distributions are established for the estimators of the model, corroborated by simulation results. When applied to the Deutsche Mark/US Dollar and the Deutsche Mark/British Pound daily returns data, the semiparametric volatility model outperforms the GJR model as well as the more commonly used GARCH(1,1) model in terms of goodness-of-fit, and forecasting, by correcting overgrowth in volatility.     

市场调查中的不完全事后分层估计量

在使用多个分类变量对样本进行交叉事后分层时，经常面临边缘总值已知、格子总值未知的不完全事后分层问题。针对这一情况，给出两种估计量即搜索比率估计量和广义搜索比率估计量。     

基于遗传算法的核函数参数优化

核主分量分析是一种输入输出特征非线性变换技术。选择最优或接近最优的非线性变换核函数参数,使类的可分性测度最大,是KPCA应用于特征提取的关键。本文采用高斯变异遗传算法作优化技术,实现了KPCA和GA的集成,适合核函数参数的优化选择。仿真表明,该技术可行、有效。     

中国政府统计抽样调查制度的总体框架研究

随着中国市场经济体制的逐步确立,政府的职能在逐渐转变,政府对统计数据的要求也在变化,这就要求政府统计调查制度也随之改革。适应市场经济体制和中国政府管理的特点以及城乡统筹发展的要求,政府统计调查应该以抽样调查为主体,抽样调查队伍和机构应主要设立在地市一级,调查的指标体系和内容应该改革与调整,企业抽样调查应该采用名单框和区域框结合的双框抽样方式。对于抽样调查得到的数据资料,不仅应当汇总得出各种可用于宏观经济管理与分析的总量指标数据和分类数据,而且还应当建立起全部被调查企业和被调查家庭的数据库,供政府部门和国内的科学研究机构进行微观社会经济活动主体层次的研究。