英文标题：
《Investigating the configurations in cross-shareholding: a joint
  copula-entropy approach》
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作者：
Roy Cerqueti (Macerata), Giulia Rotundo (Roma), and Marcel Ausloos
  (Leicester)
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最新提交年份：
2018
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英文摘要：
  --- the companies populating a Stock market, along with their connections, can be effectively modeled through a directed network, where the nodes represent the companies, and the links indicate the ownership. This paper deals with this theme and discusses the concentration of a market. A cross-shareholding matrix is considered, along with two key factors: the node out-degree distribution which represents the diversification of investments in terms of the number of involved companies, and the node in-degree distribution which reports the integration of a company due to the sales of its own shares to other companies. While diversification is widely explored in the literature, integration is most present in literature on contagions. This paper captures such quantities of interest in the two frameworks and studies the stochastic dependence of diversification and integration through a copula approach. We adopt entropies as measures for assessing the concentration in the market. The main question is to assess the dependence structure leading to a better description of the data or to market polarization (minimal entropy) or market fairness (maximal entropy). In so doing, we derive information on the way in which the in- and out-degrees should be connected in order to shape the market. The question is of interest to regulators bodies, as witnessed by specific alert threshold published on the US mergers guidelines for limiting the possibility of acquisitions and the prevalence of a single company on the market. Indeed, all countries and the EU have also rules or guidelines in order to limit concentrations, in a country or across borders, respectively. The calibration of copulas and model parameters on the basis of real data serves as an illustrative application of the theoretical proposal.
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中文摘要：
---填充股票市场的公司及其连接可以通过定向网络进行有效建模，其中节点表示公司，链接表示所有权。本文论述了这一主题，并讨论了市场集中度。考虑了交叉持股矩阵，以及两个关键因素：节点外度分布，代表涉及公司数量方面的投资多样化，以及节点内度分布，报告公司因向其他公司出售其自身股份而进行的整合。虽然文献中广泛探讨了多元化，但在关于传染病的文献中，整合最为常见。本文抓住了这两个框架中的这些关注点，并通过copula方法研究了多元化和一体化的随机依赖性。我们采用熵作为衡量市场集中度的指标。主要问题是评估相关性结构，从而更好地描述数据或市场极化（最小熵）或市场公平（最大熵）。在这样做的过程中，我们获得了关于如何将内外度联系起来以塑造市场的信息。监管机构对这个问题很感兴趣，美国合并指南中公布的限制收购可能性和单一公司在市场上普遍存在的具体警报阈值就证明了这一点。事实上，所有国家和欧盟都有各自的规则或指导方针，以限制集中在一个国家或跨境。基于真实数据对copula和模型参数的校准是理论建议的一个示例应用。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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