对完善股份合作制的认识

股份合作制是现代市场经济的必然产物 ,是历史的必然选择。但作为我国经济生活中不容忽视的一种企业组织形式 ,也存在着诸多问题。认清几个发展趋势 ,进一步规范和完善股份合作制在当前尤为重要     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

股票价格波动与宏观经济波动

本文从理论和实证两方面分析股票价格波动与宏观经济波动的关系,得出股票价格波动与宏观经济波动存在相关性,股票价格作为宏观经济的先行指标,已成为影响货币政策的外部因素的结论。尽管目前中国的股票价格波动与宏观经济波动呈现异动性,但中央银行也应关注股票价格波动。     

深圳证券交易所不同指数收益率的波动比较

股票价格指数的波动的大小通常代表了它的风险程度,研究股指的波动对风险管理、投资组合以及价格预测有着十分重要的意义。文章采用时间序列分析方法,对深圳证券交易所的三种价格指数建立GARCH模型,并对这三种指数的波动率进行比较。     

研究生导师队伍建设的重要环节——学术论文量化评估

本文从研究生导师队伍科研成果的结构入手 ,认为对导师科研工作评估重点应是对学术论文的量化 ,并提出学术论文量化评估的指标设置、权重系数及其数学模型。     

人力资本股份化探讨

本文通过对人力资本及其确认、人力资本的价值与价值计量的研究,按“人力资本的价值取决于它所创造的超额利润（经济增加值）”的原则,设计人力资本价值计量模型,进而探索不同类型人力资本股份化的实现方式,并建议利用期权股份激励创新型人力资本所有者。     

指数分析中多种方法的结合运用

指数分析法在经济分析的各个领域里已得到广泛的应用。但只用单一方法分析问题往往不够全面、深入。本文以工业劳动生产率的分析为例,从三个方面,多角度、多层次、比较系统地阐述了指数分析中独具特色的分析手法。     

Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study

Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.     

投资者信心和中国宏观经济波动——基于MIDAS混频模型

将投资者信心指数引入MIDAS混频模型之中,可以考察新冠疫情背景下投资者信心对于中国宏观经济波动的影响。基于投资者信心指数和“三驾马车”对我国季度GDP增长率进行预测,通过实证发现:引入投资者信心指数的MIDAS混频模型在预测精准度方面和基准模型进行比较,预测精准度高于未加入投资者信心的模型,均方根残差比值更小;在多元MIDAS混频模型之中,加入投资者信心指数的回归模型对我国GDP的实时预报和短期预测结果更加稳定,可为决策者提供更加精确的参考区间;宏观经济的波动对投资者信心指数变化反应,和投资、消费和进出口相比是最灵敏的。这为定量追踪宏观经济波动提供了一个新的视角。     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.