基于动态因子Copula模型的行业间系统性风险分析

国际金融市场间的相关关系以及系统性风险受到很多学者的重视，本文则以我国股市的行业指数作为研究对象进行实证研究。通过构建动态因子Copula模型，文章对行业的日收益率数据进行了动态相关性分析，并基于风险预期占比度量了我国行业之间系统性风险的溢出效应。本文分析了2006年1月4日至2016年7月1日的28个行业指数数据，基于GAS动态负荷因子的变化路径来刻画其相关关系，通过风险预期占比来研究行业间的风险溢出效应。研究表明，各个行业指数收益率之间存在较强的关联性。就单个行业来说，化工行业与其他行业关系最为不稳定。就金融与非金融行业而言，金融行业对非金融行业的影响较大且较为平稳。本文所得研究结果可以为投资者和风险管理者在进行决策时提供一定的指导。     

Fixed T dynamic panel data estimators with multifactor errors

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.     

Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering. However, this approach is sensitive to both diverse non-normalities of marginal variables and outliers, which are commonly observed in multivariate experiments. We propose mixtures of Gaussian copula factor analyzers (MGCFA) for clustering high-dimensional clustering. This model has two advantages; (1) it allows different marginal distributions to facilitate fitting flexibility of the mixture model, (2) it can avoid the curse of dimensionality by embedding the factor-analytic structure in the component-correlation matrices of the mixture distribution.An EM algorithm is developed for the fitting of MGCFA. The proposed method is free of the curse of dimensionality and allows any parametric marginal distribution which fits best to the data. It is applied to both synthetic data and a microarray gene expression data for clustering and shows its better performance over several existing methods.     

马克思主义视阈下的生产力解读

迄今,国内外对生产力的含义众说纷纭,有"劳动生产率与因素综合说";有"人类的能力说";有"人对自然的作用说";有"关系系统说". 正确地把握马克思主义生产力含义,对于我们坚持"解放生产力,发展生产力"的思想和科学发展观有重大的理论意义和实践意义.重新审视马克思的原著,应该把生产力定义为:劳动者通过具体劳动生产出满足人们需要的使用价值时所表征出来的力量.按照这一观点,决定生产力的因素应该包括实体性因素、附着性因素或渗透性因素和运行性因素.     

影响蓖麻种子萌发因子的研究

对蓖麻种子萌发进行温度、水分、播种深度、种皮等处理试验，结果表明，温度和播种深度显著影响蓖麻种子萌发，其中土壤温度低和覆土厚是导致蓖麻种子萌发缓慢的主要因子;种皮的存在有利于菌麻种子萌发;土壤水分含量在10％以上能满足蓖麻种子萌发。     

试论城市化进程中的民族关系--以对临夏市的调查为视点

临夏市作为西北民族地区的一个小城市,在城市化进程中,其民族关系也会受到影响.针对临夏市的实际情况及学术界的研究现状,我们选取临夏市城市化进程中变化较大的几个变量,即民族居住格局的变迁、人口流动的影响、民族通婚的增减以及宗教因素,作为研究重点来衡量城市化进程中的民族关系.在此基础上得出临夏市各民族之间的关系总体上是平等、团结、互助、合作的社会主义新型民族关系,和谐平等的民族关系占据了主导地位的结论,并归纳出城市化进程中临夏市的民族关系具有主从性、敏感性、易发性、历史性、隐蔽性、复杂性等特点,最后提出临夏市各族群众必须解决自身的定位问题,建立临夏市和谐民族关系的良性运转机制.     

西部民族地区退耕还林还草的法律保障探讨

自然生态环境是人类赖以生存的基本条件,是经济社会发展的根本基础。自然生态环境的不断恶化,已成为制约西部地区经济社会持续发展的严重问题之一。我国制定“退耕还林还草”战略决策最主要的目的,就是为了给西部地区人民营造一个新的生存和发展空间。实施“退耕还林还草”的战略决策,必须尊重群众意愿,确保林草的成活率;必须遵循自然规律,因地制宜,宜林则林,宜草则草,实行综合治理;必须严格执行国务院关于“退耕还林、封山绿化、以粮代赈、个体承包”的法规措施;必须认真落实保护农民土地经营权的法律规范,树立生态环境可持续发展的法律协调观;必须建立和健全西部地区生态效益补偿机制,充分利用价格杠杆的作用促进西部地区的生态平衡。西部大开发要维护经济发展与生态的良性循环,建设山川秀美的新家园,让西部地区人民群众脱贫致富奔小康。     

Time‐Varying Risk Premium in Large Cross‐Sectional Equity Data Sets

We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time‐varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and asset‐specific instruments. The estimator uses simple weighted two‐pass cross‐sectional regressions, and we show its consistency and asymptotic normality under increasing cross‐sectional and time series dimensions. We address consistent estimation of the asymptotic variance by hard thresholding, and testing for asset pricing restrictions induced by the no‐arbitrage assumption. We derive the restrictions given by a continuum of assets in a multi‐period economy under an approximate factor structure robust to asset repackaging. The empirical analysis on returns for about ten thousand U.S. stocks from July 1964 to December 2009 shows that risk premia are large and volatile in crisis periods. They exhibit large positive and negative strays from time‐invariant estimates, follow the macroeconomic cycles, and do not match risk premia estimates on standard sets of portfolios. The asset pricing restrictions are rejected for a conditional four‐factor model capturing market, size, value, and momentum effects.     

Identification Properties of Recent Production Function Estimators

This paper examines some of the recent literature on the estimation of production functions. We focus on techniques suggested in two recent papers, Olley and Pakes (1996) and Levinsohn and Petrin (2003). While there are some solid and intuitive identification ideas in these papers, we argue that the techniques can suffer from functional dependence problems. We suggest an alternative approach that is based on the ideas in these papers, but does not suffer from the functional dependence problems and produces consistent estimates under alternative data generating processes for which the original procedures do not.     

中国沪深股市的开盘效应与收盘效应

股票市场的日历效应历来受到学者和投资者的广泛关注,研究发现股市存在节日效应、月份效应与周内效应,但是针对日内效应的研究不多见.利用沪深股指2004年~2016年的高频数据,实证检验中国沪深股市是否存在日内效应,结果表明:中国沪深股市整体上存在开盘效应与收盘效应,但在牛市、熊市期间的表现形式存在差异,股指收益在牛市期间表现出正向开盘效应,在熊市期间表现出负向开盘效应,股指波动在熊市期间的开盘效应更为明显.基于滚动窗口法的进一步研究发现:股指收益开盘效应和收盘效应的变化领先于股指走势的变化,股指收益和波动的开盘效应或收盘效应存在一定程度的替代现象.研究有助于投资者合理地制定投资策略,对监管部门维护市场秩序具有一定的参考价值.