The Network Origins of Aggregate Fluctuations

This paper argues that, in the presence of intersectoral input–output linkages, microeconomic idiosyncratic shocks may lead to aggregate fluctuations. We show that, as the economy becomes more disaggregated, the rate at which aggregate volatility decays is determined by the structure of the network capturing such linkages. Our main results provide a characterization of this relationship in terms of the importance of different sectors as suppliers to their immediate customers, as well as their role as indirect suppliers to chains of downstream sectors. Such higher‐order interconnections capture the possibility of “cascade effects” whereby productivity shocks to a sector propagate not only to its immediate downstream customers, but also to the rest of the economy. Our results highlight that sizable aggregate volatility is obtained from sectoral idiosyncratic shocks only if there exists significant asymmetry in the roles that sectors play as suppliers to others, and that the “sparseness” of the input–output matrix is unrelated to the nature of aggregate fluctuations.     

The Effect of Demand–Supply Mismatches on Firm Risk

A supply chain management (SCM) system comprises many subsystems, including forecasting, order management, supplier management, procurement, production planning and control, warehousing and distribution, and product development. Demand–supply mismatches (DSMs) could indicate that some or all of these subsystems are not working as expected, creating uncertainties about the overall capabilities and effectiveness of the SCM system, which can increase firm risk. This article documents the effect of DSMs on firm risk as measured by equity volatility. Our sample consists of three different types of DSMs announced by publicly traded firms: production disruptions, excess inventory, and product introduction delays. We find that all three types of DSMs result in equity volatility increases. Over a 2‐year period around the announcement date, we observe mean abnormal equity volatility increases of 5.62% for production disruptions, 11.19% for excess inventory, and 6.28% for product introduction delays. Volatility increases associated with excess inventory are significantly higher than the increases associated with production disruptions and product introduction delays. Across all three types of DSMs, volatility changes are positively correlated with changes in information asymmetry. The results provide some support that volatility changes are also correlated with changes in financial and operating leverage.     

壮族伦理思想的和谐意蕴及其当代价值

壮族伦理思想中蕴藏着协调人与自然、人与人、人与社会和谐相处的意蕴和追求,与今天构建和谐社会有着共同的价值追求。弘扬壮族伦理思想中的和谐理念,有利于协调壮民族地区人与自然的现代冲突,为其构建社会主义和谐社会奠定良好的自然基础;有利于协调壮民族地区的人际关系和利益冲突,为其构建社会主义和谐社会提供和谐稳定的秩序保障;有利于增强壮民族地区的凝聚力和向心力,为其构建社会主义和谐社会提供精神动力和道德支持。因此,我们应充分彰显壮族伦理思想和谐意蕴的当代价值,为壮民族地区构建社会主义和谐社会服务。     

资产价格波动、人民币汇率升值与短期国际资本流动风险防范——基于随机风险溢价KFG理论模型的修正

自2006年以来,我国的资产泡沫严重,研究发现资产价格上涨预期是诱发短期国际资本流动冲击的一个重要因素,严重时会产生金融市场危机风险。基于这样的背景,通过引入资产价格波动变量,修正了Flood引入随机风险溢价的KFG理论模型,分析中国短期国际资本流动触发机制,探究资产价格波动对短期国际资本流动的作用机制。并以此为基础分析中国现有宏观政策对短期国际资本流动风险的作用和政策效果,提出减少投机性国际短期资本流动合理化的政策建议。     

中国国际短期资本流动与股市波动关系分析

影响中国股市波动因素很多,但随着中国市场的进一步开放、国际短期资本流动规模的不断扩大,一部分国际短期资本流人股市,对股市的影响在不断加深,并具有一些特点。文章采用学术界较为通用的方式即主要根据流动性的强弱对国际短期资本进行了界定;分析了中国国际短期资本流动的动因;通过中国国际短期资本流动和中国股市波动的历史数据分析了二者的特点、关系及影响因素。根据分析结果对中国国际短期资本流动的监管,股市的稳定提出一些政策建议。     

可转债市场与股票市场间的溢出效应研究

将利率作为外生变量,运用VAR-（BV）EGARCH模型对可转债市场与股票市场之间的溢出效应进行研究。研究结果表明:在收益溢出方面,只存在股票市场对可转债市场的溢出效应。在波动溢出方面,存在可转债市场与股票市场之间的双向溢出效应;并且存在利率市场向股票市场的单向波动溢出效应。从非对称溢出效应来看,存在股票市场对可转债市场的波动溢出的非对称性,由此可以看出,股票市场仍然是出于绝对支配地位的金融市场。     

股票市场的羊群行为与波动: 关联及其演化——来自深圳股票市场的证据

提出羊群行为指标以及基于多重分形谱分析的市场波动指标,运用Diks 与Panchenko ［19］提出的非线性Granger 因果关系检验和Zebende［20］提出的DCCA 交叉相关系数,动态研究了深圳股票市场的羊群行为与市场波动的关联以及他的演变规律． 结果显示,深圳股票市场羊群行为与市场波动之间的关联并不表现为简单的线性方式,而是复杂的非线性机制; 金融危机前后深圳股市羊群行为与市场波动之间表现出方向不同的相关性和不同的因果关系． 总体来说,2005 年股改之前羊群行为与市场波动之间互不影响,2005 年至2007 年间羊群行为正向影响市场波动,而2008 年之后羊群行为负向影响市场波动,同时市场波动也负向影响羊群行为． 这一新的发现表明,市场羊群行为并不像传统研究所宣称的总是产生“正反馈效应”,有时也表现出“负反馈效应”     

“特质波动率之谜”与估计模型有关吗？

以CAPM、Fama-French三因子和五因子模型为均值方程,分别采用无条件标准差,以及GARCH、EGARCH条件方差为基准计算出中国股票市场的特质波动率,研究了特质波动率与股票收益率之间的关系。研究发现,关于高特质波动风险对应低预期收益的定价异象来源于特质波动率估计模型的差异。具体地,当使用无条件标准差方式估计特质波动率时,存在定价异象,而使用GARCH、EGARCH等条件方差模型估计特质波动率时,则不存在定价异象。该结果在改变残差估计均值方程以及控制规模、流动性等其它变量后依然稳健。本文的研究有助于解释长期困扰在资产定价领域的“特质波动率之谜”。     

已实现波动和赋权已实现波动的比较研究

已实现波动是针对高频金融时间序列的一种全新的波动度量方法,具有不需要模型和计算方便的优点。赋权已实现波动则对已实现波动进行了改进,是另一种更为有效的波动度量方法。本文从定义形式、无偏性、有效性、日历效应等方面对已实现波动和赋权已实现波动加以比较。通过对上海股票市场的实证研究,说明了赋权已实现波动是优于已实现波动的波动估计量。     

Deciding between GARCH and stochastic volatility via strong decision rules

The GARCH and stochastic volatility (SV) models are two competing, well-known and often used models to explain the volatility of financial series. In this paper, we consider a closed form estimator for a stochastic volatility model and derive its asymptotic properties. We confirm our theoretical results by a simulation study. In addition, we propose a set of simple, strongly consistent decision rules to compare the ability of the GARCH and the SV model to fit the characteristic features observed in high frequency financial data such as high kurtosis and slowly decaying autocorrelation function of the squared observations. These rules are based on a number of moment conditions that is allowed to increase with sample size. We show that our selection procedure leads to choosing the model that fits best, or the simplest model under equivalence, with probability one as the sample size increases. The finite sample size behavior of our procedure is analyzed via simulations. Finally, we provide an application to stocks in the Dow Jones industrial average index.