基于混频数据模型的宏观经济对股票市场波动的长期动态影响研究

股票价格时间序列与宏观经济变量时间序列原始数据的不同频直接导致传统计量模型在处理宏观经济波动与股票市场波动的关系问题中产生模型误设和估计偏误。本文运用混频自回归条件异方差模型从水平值和波动率两个维度实证分析生产者价格指数、居民消费价格指数、宏观经济景气指数及同业拆借利率四个宏观经济变量对股票市场波动的长期动态影响。同时,运用主成分分析提取宏观经济第一主成分并构建一个宏观经济综合指数,进一步探究宏观经济总体状况对股票市场波动的长期影响。研究发现：股票市场已实现波动率显著地放大了股票市场的长期波动。生产者价格指数、居民消费价格指数、宏观经济景气指数的水平值和波动率均对股票市场长期波动产生显著影响;且其波动率维度呈现出较强的持续效应;同业拆借利率仅在水平值维度对股票市场波动长期成分产生微弱影响。宏观经济第一主成分和宏观综合指数的波动率对股票市场波动长期成分均具有显著的正向放大作用,但持续效应较弱;而其水平值对股票市场波动长期成分的影响虽然微弱,但持续时间较长。     

大学生村官留任意愿及影响因素分析

利用第一手的大学生村官问卷调查资料,基于Ordered Logistic模型对陕西省282名大学生村官的工作满意度及留任意愿进行了相关分析和回归分析。结果表明：当前大学生村官的工作满意度与留任意愿普遍较低;留任意愿与大学生最初选择从事村官工作的动机紧密相关;大学生村官的性别、年龄、从事村官年限、婚育状况、学历等人口学特征对工作满意度及留任意愿影响显著;社会各界对大学生村官的重视与认可对工作满意度与留任意愿有显著影响;提高大学生村官的收入水平、实行连续三年考核优秀,直接晋升事业编制的政策以及增加晋升为国家公务员等政治机会能够显著提高大学生村官的工作满意度并增强留任意愿,而其它因素对于提高大学生村官的工作满意度与留任意愿的效果并不明显。     

Model Selection and Shrinkage: An Overview

This special issue is concerned with model selection and shrinkage estimators. This Introduction gives an overview of the papers published in this special issue.     

Functional and emotional traits of corporate social media message strategies: Behavioral insights from S&P 500 Facebook data

Drawing from literature regarding public engagement, the Elaboration Likelihood Model (ELM), computer-mediated communication research, and emotion psychology, this study examines the effects of companies’ social media communication strategies on public engagement behaviors as indexed by post likes, shares, and comments. Specifically, it investigates how corporate Facebook posts’ functional traits (functional interactivity and vividness) and emotional traits (emotion presence, valence, and strength) impact public engagement online. Through data mining and computer-assisted sentiment analysis of 33,379 posts from 106 Standard & Poor 500 companies’ Facebook accounts, this study finds a negative effect of functional interactivity but a positive effect of vividness on engagement. It also shows that emotional traits overall yield stronger public engagement outcomes. Two-way interactions between emotional and functional features are also detected. Theoretical and practical implications are discussed.     

基于高维波动率网络模型的股票市场风险特征研究

波动率是金融风险管理研究的重要内容之一。本文基于复杂网络理论和数据挖掘技术提出股票市场的高维波动率网络模型。首先运用互信息度量不同股票价格波动之间的相关关系,其次对股票市场不同周期下的波动情况建立度的中心势、平均距离、幂律分布等网络拓扑指标,再次根据这些指标利用Prim算法构建出高维波动率网络模型,最后运用Newman-Girvan算法对股票价格波动率的相关性进行分层研究。高维波动率网络模型突破了传统波动率模型关于变量维数的限制,能够在依赖少量假设的基础上,挖掘出多个金融市场主体间的相互关系,反映金融市场的风险特征及网络拓扑性质。实证结果发现：与常用的Pearson相关系数法相比,在互信息框架下,股价波动的非线性相关关系得到了更好的度量;股票市场的整体波动性与个股波动率相关性变化趋势相反,市场处在高波动时期资产组合分散化效果较好;网络中存在少量度数大的关键节点和中心节点,风险通过这些节点可以迅速传递到整个市场;股票市场的运行具有明显的行业聚集现象;网络分层研究进一步直观的展现了风险在层与层之间的传递规律和与之对应的行业特征。高维波动率网络模型为挖掘股票市场的风险特征与管理金融风险提供了一个新的工具。     

金融周期、金融波动与中国经济增长——基于省际面板门槛模型的研究

本文应用面板门槛模型,研究了2005-2017年间我国31个省（市、自治区）金融波动对经济增长的影响。研究发现,随着金融周期所处阶段的变化,金融波动对经济增长会产生显著的非对称性双重门槛效应,主要体现为如下两点：首先,金融周期处于膨胀期、平稳期和萧条期时金融波动对经济增长会产生负向影响,但从影响系数值的大小来看,处于膨胀期时最大,是后两者的2倍之多,处于平稳期时最小且并不显著;其次,分区域的稳健性检验表明,金融发展水平高的区域双重门槛值出现得早,且两个门槛值间的区间要比金融发展水平低的区域宽28%。这些结论说明,经济增长对于金融波动的容忍弹性会随着金融周期所处的阶段而变化,金融发展水平的提高会放大经济增长对金融波动的容忍区间,但也会加速金融周期处于膨胀期时爆发金融危机的可能性,这使得当前我国存在着进一步发展金融水平和严控金融风险的矛盾,对此本文也提出了相应的政策建议。     

网络社区发现算法在流动表建模中的设计与应用

代际流动表可以统计子代与其父代社会地位配对数据的交互频数,反映了社会资源占有的优劣势在父子两代人之间的比较。对财富、阶级、特权等社会基本特征演变的实证考察,均依赖于代际流动表的量化分析。对数线性模型是流动表建模分析的基本工具,通过对列联表单元格频数进行拟合,可以识别流动表行分类与列分类之间的强弱交互效应,刻画父子社会地位间的交互结构。本文利用复杂网络社区发现算法分析父子社会地位的关联结构,针对简约对数线性模型拟合精度不够的问题,提出一种新的建模思路：利用社区发现算法对简约对数线性模型的残差列联表进行关联关系挖掘,将发现的社区效应作为附加参数约束引入原对数线性模型,以改善数据的拟合情况。由于该方法只在原简约对数线性模型中增加了一个参数约束,因此仍可以保证建模结果的简洁性及理论意义,同时社区效应补充了原对数线性模型对经验数据结构的解读。论文用此方法对来源于中国综合社会调查数据的经验代际职业流动表进行建模分析,较好地解释了子代职业阶层与父代职业阶层间的关联模式。     

基于心智模型的恐怖主义袭击扩展式演化博弈分析

政府反恐措施与恐怖分子袭击手段存在着相互观察、适应性演化的过程。本文首先构建了政府作为先行动者、恐怖分子作为后行动者的斯塔克伯格博弈模型,进一步建立了恐怖袭击问题的扩展式演化博弈模型。由于扩展式演化博弈的均衡分析需要考虑代际内和代际间不同层次的信念学习,造成了演化分析的复杂性。本文将"心智模型"概念引入到演化博弈,采取了心智模型演化解的分析方法,得出了简化后的演化均衡解。最后,结合新疆墨玉县6.28暴恐事件进行算例分析,比较怀特流形演化解和心智模型演化解的差异。心智模型演化解的结果表明,根据恐怖分子群体中选择袭击比例是否高于临界值,政府策略收敛到防御或者不防御。该求解方法通过将普遍接受的社会规范引入到演化过程分析,不仅简化了怀特流形的分析过程,而且均衡解展示了更为丰富的、更为切合实际管理问题的演化特征。     

The embeddedness of organizational performance: Multiple Membership Multiple Classification Models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical merits of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and partially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteristics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covariates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures.     

The Contingent-Claims Approach to Investment Decisions*

This paper presents a contingent-claims approach to project valuation when capital expenditures are made sequentially over time. It focuses on an important facet of sequential investment projects that the firm can undertake—or pass up projects—as more information becomes available. The contingent-claims approach takes account of this important feature of firms' investment decision process, whereas the traditional capital budgeting procedure does not. Since the traditional method does not reflect the options nature of investment opportunities, it underestimates the value of sequential investment projects. As a result, a naive implementation of the traditional capital budgeting procedure could result in rejecting profitable projects. The extent of undervaluation associated with the traditional capital budgeting procedure is greater when the correlation between the random component of the future asset value and that of the required capital expenditure is smaller and/or when the growth rate of the required capital expenditure is higher.