投资组合均值-方差模型和极小极大模型的实证比较

本文针对传统的Markowitz均值-方差(MV)模型和Young(1998)提出的极小极大(Minimax)模型进行了实证比较研究。我们将2001年上证30指数的实际数据分成两部分,一部分作为样本数据进行优化组合分析,另一部分作为非样本数据进行模拟投资,检验绩效。结果发现:在同样的样本数据下,由两种模型的解描绘的风险-收益率有效前沿图非常相似;将两组模型的最优解分别进行模拟投资,Minimax模型的结果明显优于MV模型。本文的实证结果检验了Minimax模型的理论结论,表明其在实际投资中具有良好的可操作性和实用价值。     

Dependence Estimation Over a Finite Bivariate Failure Time Region

This articleconcerns nonparametric estimation of association between bivariatefailure times. In the presence of independent right censoring,the support for failure time variates may be restricted and measuresof dependence over a finite failure time region may be of particularinterest. To this end, the reciprocal cross ratio function, weightedby the bivariate failure time density, is proposed as a summarymeasure of dependence over a failure time region. This `relativerisk' estimator is shown to be consistent and asymptoticallynormally distributed, with consistent bootstrap variance estimator.A finite-region version of Kendall's tau, which is suitable forcensored failure time data, is also proposed, and correspondingasymptotic distribution theory is noted. The accuracy of theseasymptotic approximations is studied in simulations and an illustrationis provided.     

高等师范院校研究性课程的开发及其策略思考

基础教育及其课程改革对高等师范院校师资培养模式和课程内容结构提出了改革的要求。开发研究性课程是超前因应基础教育课程改革的必要之举 ,也是培养研究型教师的应对策略     

Bandwidth selection for local linear regression smoothers

Summary. The paper presents a general strategy for selecting the bandwidth of nonparametric regression estimators and specializes it to local linear regression smoothers. The procedure requires the sample to be divided into a training sample and a testing sample. Using the training sample we first compute a family of regression smoothers indexed by their bandwidths. Next we select the bandwidth by minimizing the empirical quadratic prediction error on the testing sample. The resulting bandwidth satisfies a finite sample oracle inequality which holds for all bounded regression functions. This permits asymptotically optimal estimation for nearly any regression function. The practical performance of the method is illustrated by a simulation study which shows good finite sample behaviour of our method compared with other bandwidth selection procedures.     

试析我国古代荐举与科举选官制的利弊

我国古代的选官制度历史悠久,经历了禅让制、世袭制、军功爵制、荐举制、九品中正制和科举取士制等发展阶段。以隋唐为分界线将其分为"荐举"与"科举"两种制度,比较分析两者之间的利弊得失,以期对今天的用人制度有所借鉴。     

A weighted polynomial regression method for local fitting of spatial data

Typically, parametric approaches to spatial problems require restrictive assumptions. On the other hand, in a wide variety of practical situations nonparametric bivariate smoothing techniques has been shown to be successfully employable for estimating small or large scale regularity factors, or even the signal content of spatial data taken as a whole.We propose a weighted local polynomial regression smoother suitable for fitting of spatial data. To account for spatial variability, we both insert a spatial contiguity index in the standard formulation, and construct a spatial-adaptive bandwidth selection rule. Our bandwidth selector depends on the Gearys local indicator of spatial association. As illustrative example, we provide a brief Monte Carlo study case on equally spaced data, the performances of our smoother and the standard polynomial regression procedure are compared.This note, though it is the result of a close collaboration, was specifically elaborated as follows: paragraphs 1 and 2 by T. Sclocco and the remainder by M. Di Marzio. The authors are grateful to the referees for constructive comments and suggestions.     

Perfect simulation for correlated Poisson random variables conditioned to be positive

In this paper we present a perfect simulation method for obtaining perfect samples from collections of correlated Poisson random variables conditioned to be positive. We show how to use this method to produce a perfect sample from a Boolean model conditioned to cover a set of points: in W.S. Kendall and E. Thönnes (Pattern Recognition 32(9): 1569–1586, 1999), this special case was treated in a more complicated way. The method is applied to several simple examples where exact calculations can be made, so as to check correctness of the program using ²-tests, and some small-scale experiments are carried out to explore the behaviour of the conditioned Boolean model.     

Center and Distinguisher for Strings with Unbounded Alphabet

Consider two sets and of strings of length L with characters from an unbounded alphabet , i.e., the size of is not bounded by a constant and has to be taken into consideration as a parameter for input size. A closest string s* of is a string that minimizes the maximum of Hamming¹ distance(s, s*) over all string s : s . In contrast, a farthest string t* from maximizes the minimum of Hamming distance(t*,t) over all elements t: t . A distinguisher of from is a string that is close to every string in and far away from any string in . We obtain polynomial time approximation schemes to settle the above problems.     

Robust portfolio planning in the presence of market anomalies

In this study, a short-term portfolio modeling formulation is developed using existing anomalies as a single determinant for daily Istanbul Stock Exchange National 100 Composite Index (ISE) and US dollars (USD) returns in a Robust optimization (RO) framework. Using anomalies in planning within an RO framework establishes a balance between risk seeking and risk averse behaviors, as generating profit from anomalies is risky and RO enables to settle down the extreme risk seeking behavior. Applications of the model using various data sets result in real profit generation such that terminal wealth figures increase considerably more than Wholesale Price Index (WPI). This study demonstrates that RO is a viable approach to make use of anomaly information for short-term profits.     

An interactive three-stage model for mutual funds portfolio selection

The aim of this work is to be a useful instrument for helping finance practitioners on the selection of suitable mutual fund portfolios. The portfolio selection problem is characterized by imprecision and/or vagueness inherent in the required data and more generally, in the context where investors have to make decisions. In order to mitigate these problems, a three stage model has been proposed based on a multi-index model and considering several market scenarios described in an imprecise way by an expert. The proposed fuzzy model allows the Decision Maker to select, by means of an outranking method, a suitable portfolio taking into account the uncertainty related to the market scenarios and the imprecision and/or vagueness associated with the model data.