中国股票市场风险和收益风格效应的非参数检验

本文利用Kendall协同系数检验考察我国股票市场风险和收益的风格效应。通过实证研究首次发现各风格指数的收益率、总风险及指数特有风险均具有明显的分层结构,风格效应显著。对影响风险和收益的风格因素进行的分析表明:股票风险受规模因素的影响十分明显;而股票回报率受价值因素的影响比较显著,受规模因素的影响不明显。并进一步用Spearman相关系数考察了风险与收益之间的秩相关性。本文研究结果对资产配置和风险监管等问题具有参考价值。     

多种评价方法在投资环境评价中的综合应用

本文针对过去在应用不同方法作投资环境评价时，往往出现评价结果不一致性的现象，提出了一种多方法综合应用的系统评价方法，该方法克服了以往只用一种方法评价的片面性，提高了评价的精度和可靠性，并以我国19个副省级以上城市为例，应用该方法对其投资环境进行了综合评价。     

Nonparametric estimation of variance, skewness and kurtosis of the distribution of a statistic by jackknife and bootstrap techniques

While jackknife and bootstrap estimates of the variance of a statistic are well–known, the author extends these nonparametric maximum likelihood techniques to the estimation of skewness and kurtosis. In addition to the usual negative jackknife also a positive jackknife as proposed by BERAN (1984) receives interest in this work. The performance of the methods is investigated by a Monte Carlo study for Kendall's tau in various situations likely to occur in practice. Possible applications of these developments are discussed.     

The t Copula and Related Copulas

The t copula and its properties are described with a focus on issues related to the dependence of extreme values. The Gaussian mixture representation of a multivariate t distribution is used as a starting point to construct two new copulas, the skewed t copula and the grouped t copula, which allow more heterogeneity in the modelling of dependent observations. Extreme value considerations are used to derive two further new copulas: the t extreme value copula is the limiting copula of componentwise maxima of t distributed random vectors; the t lower tail copula is the limiting copula of bivariate observations from a t distribution that are conditioned to lie below some joint threshold that is progressively lowered. Both these copulas may be approximated for practical purposes by simpler, better-known copulas, these being the Gumbel and Clayton copulas respectively.     

On testing for independence against right tail increasing in bivariate models

A random variableY is right tail increasing (RTI) inX if the failure rate of the conditional distribution ofX givenY>y is uniformly smaller than that of the marginal distribution ofX for everyy0. This concept of positive dependence is not symmetric inX andY and is stronger than the notion of positive quadrant dependence. In this paper we consider the problem of testing for independence against the alternative thatY is RTI inX. We propose two distribution-free tests and obtain their limiting null distributions. The proposed tests are compared to Kendall's and Spearman's tests in terms of Pitman asymptotic relative efficiency. We have also conducted a Monte Carlo study to compare the powers of these tests.Research supported by an NSERC Canada operating grant at the University of Alberta.Part of this research was done while visiting the University of Alberta supported by the NSERC Canada grant of the first author.     

公共事业指数与工业指数的相关性研究

本文根据阿基米德类Copula函数与Kendall's秩相关系数的关系,通过非参数估计法得到描述沪市行业指数中的公共事业指数与工业指数组合相关结构的最佳Copula函数形式,即用来描述牛市特征的Gumbel Copula,以及相应的尾部相关系数.尾部相关性分析结果表明,两指数收益率之间存在明显的非对称的尾部相关性,而且是上尾相关程度强于下尾相关程度,说明两指数牛市期间的相关性强于熊市期间的相关性.从规避风险角度分析,公共事业指数/工业指数组合是有效的指数组合.     

Engel's Law Reconsidered

Engel's Law expresses a negative stochastic association of the bivariate distribution of income and food share across a population. Among the many different definitions which can be found in the statistical literature four concepts are discussed and tested: Kendall's τ, quadrant dependence, stochastic decreasing conditional food share distribution function and decreasing regression. Only the last one is used in the economic literature, yet it does not imply useful information of the underlying distribution. For linking Engels's Law to micro-economics, stronger concepts of stochastic association are needed. This motivates the empirical study of the proposed alternative concepts of negative association.