Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions

We propose multilayer networks in the frequency domain, including the short-term, medium-term, and long-term layers, to investigate the extreme risk connectedness among financial institutions. Using the conditional autoregressive value at risk (CAViaR) tool to measure the extreme risk of financial institutions, we construct extreme risk networks and inter-sector extreme risk networks of 36 Chinese financial institutions through the proposed approach. We observe that the extreme risk connectedness across financial institutions is heterogeneous in the short-, medium-, and long-term. In general, the long-term connectedness among financial institutions rises sharply during times of financial stress, such as the 2015 Chinese stock market turbulence and the 2020 COVID-19 pandemic. Moreover, we note that the insurers are key players in driving the inter-sector extreme risk networks, because the inter-sector systemic importance of insurance institutions is dominant. Finally, our conclusions provide valuable information for regulators to prevent systemic risk.     

Financial stress transmission between the U.S. and the Euro Area

This paper examines financial stress transmission between the U.S. and the Euro Area. To better understand the linkages between financial stress in the two regions, we construct a financial stress index for the U.S. similar to the Composite Indicators of Systemic Stress (CISS) that has been developed for the Euro Area with a focus on systemic risk. Using weekly data from 2000 to 2021 and Granger predictability in distribution test, we analyze stress transmission in “normal” times as well as under unusually high and low stress episodes. While we document unilateral transmission from the U.S. to the Euro Area under normal conditions based on the center of the distribution, tail dependence tests and impulse response analysis show significant bilateral transmission, particularly in unusually high financial stress episodes. This holds true for aggregate indices as well as the subindicators of financial stress in various financial markets. As such, there must be global efforts to contain financial crises and ensure a strong and resilient financial system.     

民营企业战略性人力资源管理研究

21世纪是“科技”时代、“大数据人工智能”时代、更是“知识经济”时代。在当今全球化背景下,战略性人力资源管理越发重要,致力于极高人力资源管理的效率和效能,简化人事管理流程,减少不必要办公时间。随着产业竞争加剧,企业管理从主体为物转换为人,企业的未来发展和社会进步在很大程度上依靠人力资源规划,这取决于人力资源的战略性和先导性。     

系统性风险与宏观审慎监管——基于美国银行业的实证研究

后危机时期,国际金融界开始反思金融监管体制的弊端,加强基于系统性风险的宏观审慎监管,维护金融稳定成为今后改革的方向。以2009年初美联储进行压力测试的美国18家大银行为研究对象,对其风险波动率和系统性风险予以度量。实证研究表明,2007年次贷危机爆发起,美国银行业的系统性风险从低谷走向高峰,至今虽有所缓释,但仍高于危机前的水平。因此,金融监管目标框架应该是:以控制系统性风险为中心,加强宏观审慎监管,保持微观审慎监管,确保金融体系的安全与健康。     

全球管道天然气贸易节点重要性评价

因各国天然气需求增加,全球天然气贸易局势愈加复杂,本文构建全球管道天然气贸易复杂网络,从静态网络结构特征与动态网络功能维度选取多个指标,运用“熵值法”和“时序加权平均算子”构建节点国家重要性动态综合评价模型,利用2013~2019年全球管道天然气贸易数据进行实证分析。结果表明:从出口视角看,俄罗斯占据核心地位;哈萨克斯坦和挪威等靠近网络中心、出口稳定;荷兰、美国和英国起贸易枢纽作用;阿尔及利亚和加拿大等虽出口量大,但贸易关系单一。德国、法国和中国等国家,受限于能源禀赋,在进口端占据重要地位。美洲、欧洲地区管道天然气贸易流转速率和市场化程度优于中东、亚太和非洲地区。最后,本文提出了优化我国管道天然气进口的相关建议。     

It takes more than two to tango: Multiple bank lending,asset commonality and risk

Multiple-bank lending is the most prevalent form of bank-firm credit relationships in nearly all countries. It results in high asset commonality and interconnectedness, allows idiosyncratic risks to become systemic, and makes the banking system more fragile and vulnerable to shocks. Using detailed, granular-level, supervisory data on large corporate loans, we show that multiple bank lending is driven, inter alia, by regulatory limits on large credit exposures. These limits, aimed at mitigating an individual bank's concentration risk, force firms to explore alternative sources of funding, making the common borrowers' phenomenon more prominent. We find that multiple bank lending is determined endogenously, and its likelihood increases with the level of portfolio similarity between lenders. The size of the original lender and its systemic importance magnifies this effect. We argue that banks do not internalize the systemic effect of their lending decisions and that multiple bank lending constitutes an insurance mechanism related to an implicit "too-many-to-fail" guarantee. Its externalities are suboptimal and should be reinforced with better monitoring by the related authorities.     

系统性金融风险文献综述:现状、发展与展望

防范风险是我国金融业的永恒主题。如何有效应对金融系统的异常波动、缓释国际市场的外部冲击、精准处置重点领域风险,仍将是我国“十四五”期间的重要任务。本文分别基于系统性金融风险的有效测度、传染溢出、驱动因素、前瞻预测,以及其与宏观经济的相互作用关系、风险调控政策与政策的有效性、监管理念发展等不同视角,对272篇国内外顶级(权威)文献进行了全面、深入的梳理与总结,并展望该领域的重点研究方向,从而为我国构建金融风险防控长效机制、完善“双循环”新格局下的金融监管体系提供参考,以牢牢守住不发生系统性金融风险的底线,推动经济社会高质量发展。     

Risk spillovers and interconnectedness between systemically important institutions

In this paper, we gauge the degree of interconnectedness and quantify the linkages between global and other systemically important institutions, and the global financial system. We document that the two groups and the financial system become more interconnected during the global financial crisis when linkages across groups grow. In contrast, during tranquil times linkages within groups prevail. Global systemically important banks (G-SIBs) contribute most to system-wide distress but are also most exposed. There are more links coming from G-SIBs to other systemically important institutions (O-SIIs) than the other way around, confirming the role of G-SIBs as major risk transmitters in the financial system. The two groups and the global financial system tend to co-vary for periods up to 60 days Prior to their official designation as G-SIBs or O-SIIs, the prevalent news sentiment about these institutions (we measure with a textual analysis) was negative. Importantly, the systemic importance and exposure of G-SIBs and O-SIIs is perceived differently by the Financial Stability Board (FSB) and the European Banking Authority (EBA).     

False dichotomy alert: Improving subjective-probability estimates vs. raising awareness of systemic risk

Taleb et al. (2022) portray the superforecasting research program as a masquerade that purports to build “survival functions for tail assessments via sports-like tournaments.” But that never was the goal. The program was designed to help intelligence analysts make better probability judgments, which required posing rapidly resolvable questions. From a signal detection theory perspective, the superforecasting and Taleb et al. programs are complementary, not contradictory (a point Taleb and Tetlock (2013) recognized). The superforecasting program aims at achieving high hit rates at low cost in false-positives, whereas Taleb et al. prioritize alerting us to systemic risk, even if that entails a high false-positive rate. Proponents of each program should, however, acknowledge weaknesses in their cases. It is unclear: (a) how Taleb et al. (2022) can justify extreme error-avoidance trade-offs, without tacit probability judgments of rare, high-impact events; (b) how much superforecasting interventions can improve probability judgments of such events.     

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.