加权复合分位数回归方法在动态VaR风险度量中的应用

风险价值(VaR)因为简单直观,成为了当今国际上最主流的风险度量方法之一,而基于时间序列自回归(AR)模型来计算无条件风险度量值在实业界有广泛应用。本文基于分位数回归理论对AR模型提出了一个估计方法--加权复合分位数回归(WCQR)估计,该方法可以充分利用多个分位数信息提高参数估计的效率,并且对于不同的分位数回归赋予不同的权重,使得估计更加有效,文中给出了该估计的渐近正态性质。有限样本的数值模拟表明,当残差服从非正态分布时,WCQR估计的的统计性质接近于极大似然估计,而该估计是不需要知道残差分布的,因此,所提出的WCQR估计更加具有竞争力。此方法在预测资产收益的VaR动态风险时有较好的应用,我们将所提出的理论分析了我国九只封闭式基金,实证分析发现,结合WCQR方法求得的VaR风险与用非参数方法求得的VaR风险非常接近,而结合WCQR方法可以计算动态的VaR风险值和预测资产收益的VaR风险值。     

基于条件概率积分变换的多元Copula函数选择

本文构建了基于条件概率积分变换的Copula函数选择方法,通过对条件概率积分变换下Anderson-Darling(AD)、Kolmogorov-Smirnov(KS)、Cramér-von Mises(CM)这三种统计量的比较,讨论在不同样本容量和变量维数下其对多种Copula函数的拟合效果。利用GSPTSE、INMEX.MX和NDX三大股指样本,将基于条件概率积分变换的Copula函数选择方法与核密度估计和极大似然估计选择法的效果进行系统比较。结果表明,基于条件概率积分变换的检验法可以有效解决多元Copula函数的选择问题,其拟合优度检验更精确、更稳定;核密度估计检验在大样本下比较稳定,而小样本下稳定性较差;相比之下,极大似然值检验法则不稳定。     

房价波动、 收入水平对住房消费的影响——基于SYS-GMM估计方法的区域差异分析

基于生命周期-持久收入假说,在一般随机Ramsey模型的基础上,推导包含房价、收入和财富的住房消费函数,利用2002年至2013年31个省直辖市的面板数据,采用两步System-GMM估计方法考察我国房价波动和居民收入水平对住房消费的影响.实证结果表明:房价波动对全国居民住房消费具有显著的抑制作用;其中,滞后期和当期房价波动与当期住房消费负相关,挤出效应明显;未来一期房价波动与住房消费变化方向一致,存在积极的财富效应;此外,房价波动对东中西部各地区居民住房消费的抑制作用存在较大差异,西部地区抑制效应最为明显;滞后期住房消费与当期住房消费变动方向一致,人均可支配收入波动和人均年底储蓄余额对我国人均住房消费都起着重要的支撑作用.     

基于非参数时变Copula模型的美国次贷危机传染分析

金融危机传染分析是国际金融领域中的重要课题,本文对Copula变点检测方法进行推广,采用时变非参数阿基米德Copula模型检验金融危机传染的存在性及其变化趋势,以时变尾部相依系数的大小来度量危机传染程度,并结合系数的变化趋势和时间段对金融危机传染效应进行分析.最后选择全球六个主要股票市场指数和S&P500指数进行危机传染实证研究,得出次贷危机对不同国家或地区的传染效应有所差别.     

A class of weighted dependence measures for bivariate failure time data

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Differential equation models for statistical functions

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation models of data, and smoothing over complicated multidimensional domains. This paper aims to stimulate interest in this approach to functional estimation problems, rather than provide carefully worked out methods.     

以现代教学论指导普通物理教学

本文针对师范院校非物理专业的“普通物理”教与学的矛盾,以现代教学理论为指导,近年来探索新的教学策略,并致力实施,调动了学习积极性和改进评价,取得了较好效果。