军民融合协同创新驱动产业园区高质量发展研究

军民融合产业园区高质量发展对于推进军民深度融合、在全国形成可复制可推广经验具有重要意义。军民融合协同创新通过促进新知识涌现、推进新产业集聚发展，最终汇聚经济发展新动能，驱动园区高质量发展。在提出军民融合协同创新驱动园区高质量发展的机理与组织框架基础上，将发展模式归纳为以分享经济为背景的资源共享模式、以新型研发机构为核心的第三方模式、以产业联盟为核心的产业链拓展模式和以交易平台为核心的“PPP”模式，最后提出相关对策建议。     

大数据能力对企业创新绩效的影响——IT-业务融合与双元环境的调节作用

基于动态能力理论和权变理论，以IT-业务融合、双元环境为调节变量，探究大数据能力与企业创新绩效间的作用关系，结果发现：大数据能力对创新绩效具有正向促进作用；IT-业务融合调节大数据能力与创新绩效的关系，IT-业务融合水平越高，大数据能力对创新绩效的正向影响就越显著；双元环境能够调节大数据能力与创新绩效间关系。其中，环境动态性在大数据能力与创新绩效关系间起正U型调节作用，而环境竞争性在大数据能力与创新绩效关系间起倒U型调节作用。从内外部情境视角解释大数据能力的创新转化，对中国企业大数据应用具有指导意义。     

管理层权力制衡强度、债务约束与企业创新投资

企业创新是国家经济可持续增长的关键，受到管理层意愿的影响，因而需要对内部经营者的权力进行制衡。以2010-2018年我国A股上市公司为研究样本，实证检验管理层权力制衡强度对企业创新投资的影响，以及不同债务约束情境下高商业信用配置、高负债水平的调节效应，此外，还考察了产权性质的差异化影响。研究表明，管理层权力制衡强度越大，企业创新投资水平越高；高商业信用强化了该促进作用，而高负债水平弱化了该促进作用。进一步研究发现，管理层权力制衡强度与企业创新投资的关系在民企中更显著；国企能够更好地获得和运用商业信用，使其高商业信用对该关系的强化效应更显著；民企具有更强的债务约束，其高负债水平对该关系的弱化效应更明显。     

Market impact: a systematic study of the high frequency options market

This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders—large orders that are split into smaller pieces before being sent to the market—on one of the main Asian markets. In line with our previous work on the equity market [Said, E., Bel Hadj Ayed, A., Husson, A. and Abergel, F., Market impact: A systematic study of limit orders. Mark. Microstruct. Liq., 2018, 3(3&4), 1850008.], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well-understood equity market: Square-Root Law, Fair Pricing Condition and Market Impact Dynamics.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Economic interpretations of DEA

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention.     

高科技社会我国风险沟通重塑

高科技社会衍生“人为风险”，西方发达高科技国家倡导新型风险沟通，而后发场域由于系统落差，呈现出迟滞型风险沟通。通过构建全球化高科技风险社会、全球本地化风险沟通的新分析框架，发现高科技社会我国风险沟通面临着风险的社会放大、气泡结构、路径互动阙如、手段叠加、目标战略缺失、过程碎片化等迟滞困境。通过创新理念、吸纳型风险沟通、复合式路径、平衡化手段、连续统、政府学习和政府职能的“七位一体”再造，可有效重塑高科技社会我国风险沟通，推进国家风险治理体系和治理能力现代化。     

Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

Methodological Advances in DEA: A survey and an application for the Dutch electricity sector

We survey the methodological advances in DEA over the last 25 years and discuss the necessary conditions for a sound empirical application. We hope this survey will contribute to the further dissemination of DEA, the knowledge of its relative strengths and weaknesses, and the tools currently available for exploiting its full potential. Our main points are illustrated by the case of the DEA study used by the regulatory office of the Dutch electricity sector (Dienst Toezicht Elektriciteitswet; Dte) for setting price caps.