The Impact of the Cross-Shareholding Network on Extreme Price Movements: Evidence from China

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2019-10-29 DOI:10.21314/jor.2019.423

Jie Cao, Fenghua Wen

引用次数: 27

Abstract

By using information about the ownership structure of listed companies from 2004 to 2016, we construct the cross-shareholding network for each year and examine the effects of the network position of a firm on extreme price movement. The results show that firms that are in more central positions exhibit less extreme price movements because they have more connections with other firms, because they can collect or disseminate information more easily through their connections and because their price information transparency is higher. Moreover, we examine the different effects of network structure on extreme upward and downward movements in price and find that the centrality of a firm more strongly inhibits extreme price upward movements than it does downward movements. Our results suggest that a firm’s position in the cross-shareholding network can influence its extreme price movements, which gives us new insights into extreme stock market movements and provides useful suggestions for future financial regulations.

查看原文本刊更多论文

交叉持股网络对极端价格变动的影响:来自中国的证据

本文利用2004 - 2016年上市公司的股权结构信息，构建了各年份的交叉持股网络，并考察了企业的网络位置对价格极端变动的影响。结果表明，处于中心位置的企业表现出较少的极端价格变动，因为他们与其他企业有更多的联系，因为他们可以更容易地通过他们的联系收集或传播信息，因为他们的价格信息透明度更高。此外，我们研究了网络结构对价格极端向上和向下运动的不同影响，发现企业的中心性对价格极端向上运动的抑制作用比对价格极端向下运动的抑制作用更强。我们的研究结果表明，公司在交叉持股网络中的位置会影响其极端价格波动，这为我们提供了对股票市场极端波动的新见解，并为未来的金融监管提供了有益的建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Operational Risk BUSINESS, FINANCE-

CiteScore

1.00

自引率

40.00%

发文量

期刊介绍： In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.