全球股票市场系统性风险的预警与防范——基于高低波动风险溢出网络的分析北大核心

本文依据全球股票市场波动率划分低波动和高波动,通过构建高低波动风险溢出网络,探究全球股市系统性风险演变的特征,并识别积聚和爆发阶段的风险源头和传染结构。研究发现:第一,全球股市系统性风险具有顺周期性,且低波动溢出水平具有前瞻性,可有效预警系统性风险。第二,相较于低波动溢出范围大、水平低的特征,全球股市高波动具有溢出范围小、水平高的特征。第三,风险传染具有一定的结构稳定性,同区域、同组织股市间风险溢出水平较高;但在高波动状态时,尤其是危机期间,跨区域、跨组织股市间风险溢出水平明显上升。第四,不同股市在网络中扮演的角色不同,且具有时变性,发达经济体股市主要为风险输出方,新兴市场股市主要为风险输入方。上述结论对全球股市系统性风险的有效预警和准确防范具有参考价值。     

波动溢出网络视角下全球股市风险传染研究

为刻画全球股票市场风险传染的动态路径特征,从波动溢出网络视角分析全球股票市场的风险传染机制.首先,采用DCC-GARCH动态溢出指数框架来捕捉全球股市波动溢出的动态联动性和风险传染效应;然后,基于方差分解构建信息溢出复杂网络,从网络视角分析全球股票市场的风险传染特征.研究发现,在整个样本期间,全球股票市场高度相互关联,并依赖于极端经济事件;从次贷危机到欧债危机期间全球股市溢出整体呈现减弱态势;近年来国际资本流动、金融开放与国际贸易往来等推动我国股市进程走向新阶段,风险溢出与吸收水平有上升趋势.     

共同风险贡献还是异质性风险传染——来自全球股市的新证据北大核心

本文基于广义动态因子模型(GDFM)识别全球股市波动率的共同因子与异质性因子,刻画波动率共同因子与异质性因子的脉冲响应曲线与风险贡献变动,运用长期方差分解网络(LVDN)方法构建全球股市异质性风险传染网络,测度极端事件期间全球股市的异质性风险传染效应,追溯风险传染的源头。结果表明:全球股市波动率共同因子与异质性因子走势间呈现出“协同效应”,在极端事件期间,发达经济体股市波动率异质性因子迅速攀升。在标准冲击下,全球股市波动率共同因子震荡周期约为7天,说明共同风险对于各经济体股市的作用机制以短期冲击效应为主。然而,全球股市波动率异质性因子脉冲响应曲线具有响应程度低与收敛速度慢的特性,在极端事件冲击下,全球股市长期因果网络节点分布具备“高度聚类”属性,在剔除过度识别因素后,运用阈值约束方法求解全球股市异质性风险波动溢出净值发现,美国股市仍然是全球股市异质性风险的主要输出方。     

新冠肺炎疫情冲击下全球资本市场波动溢出风险及其连通网络研究

结合全球28个股市在2003—2021年的日度数据,采用基于广义方差分解的动态波动溢出指数方法来测度新冠肺炎疫情冲击下全球股市波动溢出风险及其连通网络的动态演化特征.在此基础上,基于面板中介效应模型来揭示疫情对股市波动风险的影响机制.研究结果表明:第一,全球重大危机事件(金融危机、疫情危机)冲击均会加剧各国股市的极端波动风险且危机发生国成为波动溢出的主要来源;第二,新冠肺炎疫情冲击加剧全球股市的总体溢出水平和网络连通性水平,使得大部分国家股市面临危机发生国股市的波动溢出风险;第三,新冠肺炎疫情暴发以来,我国沪深股市自身波动风险不大,但面临海外国家股市波动溢出风险;第四,从全球来看,总体上存在着"疫情冲击→股市波动率(波动溢出风险)→股市尾部风险"的影响路径和中介效应.     

全球股市的依存结构与风险传染研究

为全面刻画全球股市极端波动风险的跨区域传染效应及其传染路径,本文结合滚动窗口技术构建了高维动态R-Vine Copula模型,分析全球28个股市在2003—2020年的动态相依结构演化及其系统性风险溢出效应.结论表明,全球股市相依结构分布呈明显的洲际聚集特征,即亚太区域-欧洲区域-美洲区域.中国香港、法国和美国股市分别为亚太、欧洲和美洲的中心枢纽.新加坡和荷兰股市起到了联结亚太区域和欧洲区域的中介桥梁作用.2008年金融危机和2020年新冠肺炎疫情危机的冲击均未改变全球股市的总体相依结构,但导致了临时性结构突变,并提升了全球系统性风险溢出水平.新冠肺炎疫情导致欧美区域股市间的系统性风险水平超过了金融危机期间的影响,而金融危机导致亚太区域股市间的系统性风险溢出水平则明显超过了新冠肺炎疫情的影响.在2008年和2020年的两次危机中,沪深股市主要面临中国香港股市的系统性风险溢入.     

国际股市极端风险溢出效应研究 ——基于上行CoVaR和下行CoVaR的分析

将广义CoVaR模型和溢出指数方法相结合,分别从极端上涨和极端下跌两个视角测度国际股市间极端风险溢出效应,并在此基础上利用社会网络方法构建下行风险和上行风险溢出网络,对全球14个股市在下行风险和上行风险溢出中的地位和影响力进行了动态分析.实证结果显示:无论是下行风险还是上行风险,欧洲和美洲成熟股市始终是主要的风险溢出净输出者,日本和韩国等亚洲股市是主要的风险溢出净输入者,风险类型的变化不会显著影响某一股市在全球股市中的地位;上行风险溢出和下行风险溢出之间存在着明显的协同性、周期性和非对称性;上行风险溢出与下行风险溢出之间具有较高的跨期相关性,两者之间存在相互引导作用.因此,可以构建合理的上行风险指标以实现对下行风险的有效预警.     

基于EVT-Copula-CoVaR模型的“一带一路”沿线国家股市风险溢出效应研究

随着"一带一路"倡议的不断推进,沿线各国的经济融合度不断提高,资本流动规模的增大将对各国股市间的风险溢出造成重要影响。本文从"一带一路"倡议实施的角度,运用EVT-Copula-CoVaR模型对沿线国家间股市风险溢出进行刻画,从而探讨不同时期内各国股市间风险溢出状态的变化。研究结果表明:我国股票市场与沿线其他国家股票市场间具有双向的、非对称的风险溢出效应;"一带一路"倡议的推行增大了我国与沿线其他国家股市间的风险溢出强度,也就是说,当沿线其他国家股市处于极端风险情况时,我国股票市场受到冲击的概率将增大;倡议实施后,沿线东南亚国家对我国股市表现出了相对较高的风险溢出水平。     

疫情冲击下全球股市的风险溢出效应研究

本文通过构建全球股市风险溢出网络,测度疫情期间全球股市风险溢出强度,研究各国股市风险的传递方向及溢出机制;通过与2008年金融危机的横向比较以及全样本纵向分析,探究不同阶段全球股市风险溢出效应的差异.研究发现:(1)疫情期间全球股市风险总溢出强度先上升后下降,其强度明显高于2008年金融危机与全样本均值.(2)不同时期全球股市风险溢出中心存在差异,中国是全球股市的主要风险接受国.金融危机时期,美国是全球股市单一的风险溢出中心;疫情期间,疫情严重的欧洲国家成为全球股市的风险溢出中心.     

疫情冲击下全球股市的风险溢出效应研究

本文通过构建全球股市风险溢出网络,测度疫情期间全球股市风险溢出强度,研究各国股市风险的传递方向及溢出机制;通过与2008年金融危机的横向比较以及全样本纵向分析,探究不同阶段全球股市风险溢出效应的差异.研究发现:(1)疫情期间全球股市风险总溢出强度先上升后下降,其强度明显高于2008年金融危机与全样本均值.(2)不同时期全球股市风险溢出中心存在差异,中国是全球股市的主要风险接受国.金融危机时期,美国是全球股市单一的风险溢出中心;疫情期间,疫情严重的欧洲国家成为全球股市的风险溢出中心.     

系统性金融风险动态测度与跨部门网络溢出效应研究北大核心

科学、有效地进行系统性金融风险动态测度与溢出效应评估,直接关系到我国金融体系重大风险的防范与化解。本文基于金融压力指数法进行系统性金融风险动态测度,构建跨部门风险溢出网络,论证多维风险因子对系统性金融风险驱动作用的结构性差异和系统重要性。研究结果表明:第一,危机时期,跨部门风险协同运动趋势明显,风险跨部门溢出方向和强度均具有非对称性。第二,外汇市场、债券市场和房地产市场是主要的风险溢出方,在危机时期,金融机构、股票市场和外汇市场是系统性金融风险重要的传播渠道。第三,股票市场估值水平、投资者情绪和经济政策不确定性对系统性金融风险水平的驱动作用呈倒U型,在系统性金融风险测度指数分布的右尾,大宗商品价格波动的驱动作用最大。第四,随机森林算法测度的风险驱动因子重要度证明,投资者情绪和大宗商品市场价格波动因子对系统性金融风险拐点的出现具有关键性影响。     

Measuring the increasing connectedness of Chinese assets with global assets: using a variance decompositions method

This study uses the network topology of variance decompositions to investigate the connectedness of four assets (stocks, bonds, foreign exchange and commodities) across five countries (US, EU, UK, Japan and China). We find that connectedness to and from the Chinese asset markets increased significantly from 2013 to 2018, which reveals that Chinese assets have gradually become integrated into the global economy. We also investigate the volatility connectedness in economically fragile periods and find that the Chinese market acted as a transmitter of volatility in the 2015 Chinese stock crash. This finding is potentially essential to modern risk measurement and management.     

Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Measuring the systemic risk contribution (SRC) of country-level stock markets helps understand the rise of extreme risks in the worldwide stock system to prevent potential financial crises. This paper proposes a novel SRC measurement based on quantifying tail risk propagation's domino effect using ΔCoVaR and the cascading failure network model. While ΔCoVaR captures the tail dependency structure among stock markets, the cascading failure network model captures the nonlinear dynamic characteristics of tail risk contagion to mimic tail risk propagation. As an illustration, we analyze 73 markets' SRCs using a daily closing price dataset from 1990.12.19 to 2020.9.8. The validity test demonstrates that our method outperforms seven classic methods as it helps early warning global financial crises and correlates to many systemic risk determinants, e.g., the market liquidity, leverage, inflation, and fluctuation. The empirical results identify that Southeast European markets have higher SRCs with time-varying and momentum features corresponding to significant financial crisis events. Besides, it needs attention that South American and African markets have displayed increasing risk contributions since 2018. Overall, our results highlight that considering tail risk contagion's dynamic characteristics helps avoid underestimating SRC and supplement a “too cascading impactive to fail” perspective to improve financial crisis prevention.     

波动溢出网络视角的金融风险传染研究

当前各类经济风险交叉关联,金融系统的风险溢出效应备受关注,为刻画我国金融系统性风险传染的路径特征,本文从波动溢出网络的视角分析金融系统内部的风险传染机制。首先使用广义动态因子模型对收益波动的共同波动率成分和特质性波动率成分进行区分。然后,根据货币市场、资本市场、大宗商品交易市场、外汇市场、房地产市场和黄金市场之间的特质性波动溢出效应,利用基于TVP-VAR模型的方差分解溢出指数分析金融系统波动溢出的动态联动性和风险传递机制。在分析方向性波动溢出效应的基础上,采用方差分解网络方法构建起信息溢出复杂网络,从网络视角分析金融系统内部的风险传染特征。实证研究发现,房地产市场和外汇市场的净溢出效应绝对值相较于其他市场更大,其受其他市场风险冲击的影响强于对外风险溢出效应,而股票市场的单向对外风险溢出效应强度最大。在波动溢出的基础上,进一步考虑股市波动率指数与其他市场波动率指数进行投资组合的资产配置权重,计算了波动率指数投资组合的最优组合权重和对冲策略。研究结论有助于更好地理解我国金融系统的风险传染机制,对监管机构加强宏观审慎监管、投资者规避投资风险具有重要意义。     

Dynamic spillovers across oil,gold and stock markets in the presence of major public health emergencies

Extreme events have a systemic impact on global financial markets, leading to significant cross-market spillovers in the oil, gold, and stock markets and raising widespread concerns about market linkages and risk contagion. In this paper, with a focus on both return and volatility, a frontier spillover network analysis is used to examine the strength and scale characteristics of spillovers in the oil, gold and stock markets under major public health emergency shocks. In addition, the paper adopts a marginal spillover and network analysis to evaluate linkage relationships, risk sources and transmission paths in the oil, gold, and stock markets during such events. The results show that the return and volatility spillover effects generated across the oil, gold, and stock markets are significant, with return spillovers being more stable and volatility spillovers being highly sensitive to emergencies. Meanwhile, the COVID-19 pandemic has displayed the strongest return and volatility spillovers. The high intensity of the shocks during the COVID-19 period has changed the usual characteristics of the market, with the gold market becoming the risk receiver and the oil market becoming risk sources.     

Sentiment and stock market connectedness: Evidence from the U.S. – China trade war

We assess the impact of monthly and daily investor sentiment on stock market return and volatility connectedness during the U.S.-China trade war period. Our analyses focus on the connectedness between the two economies and their major trading partners. We also investigate the asymmetric impact of sentiment on volatility connectedness by exploring the upside and downside markets separately. We consistently document a negative relationship between investor sentiment and stock market connectedness for both return and volatility. We further confirm that investor sentiment exerts a larger impact on volatility connectedness in the downside market compared to the upside market.     

基于MSV与CoVaR模型的公司债市场与股票市场间风险溢出效应研究

依据2015—2017年中证公司债指数与沪深300指数的日收益数据,运用GC-t-MSV模型,检验中国公司债市场与股票市场间的风险溢出效应,并通过条件在险价值(CoVaR)模型度量两市场间风险溢出效应。结果表明：公司债市场与股票市场间存在不对称的双向风险溢出效应,且公司债市场对股票市场的风险溢出效应强于股票市场对公司债市场的风险溢出效应;公司债市场与股票市场的波动受其自身波动的影响较大,鉴此,监管部门和投资者应增强对公司债市场的关注,根据公司债市场的风险变化及时采取应对措施,充分发挥其风险信号作用。     

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.     

Small fish in big ponds: Connections of green finance assets to commodity and sectoral stock markets

We analyze return and volatility connectedness of the rising green asset and the well-established US industry stock and commodity markets from September 2010 to July 2021. We find that the time-varying return and volatility connectedness have exhibited serious crisis jumps. Some individual assets of both the green and commodity markets are in connection to the US sectoral stock market returns, and the volatility connections are even more common than the return connections. Furthermore, some financial and economic uncertainty indicators manifest positive impacts from the volatility of some ‘big pond’ markets for e.g. commodities, whereas some others affect the connectedness negatively. Additional analysis of financial and economic uncertainty indicators manifests positive impacts from the volatility of some ‘big pond’ markets, e.g., commodities, while others negatively affect the connectedness.     

Global portfolio investment network and stock market comovement

This paper examines the impact of financial connectedness of countries on international stock market comovement. In recent decades, cross-border capital flows have increased dramatically, and I use bilateral cross-border portfolio holdings to create a global portfolio investment network. Using network analysis, I examine the effect of a country's centrality within this network on stock market comovement while also controlling for the country's trade connectedness. The results show that stock markets of countries that occupy highly central positions within the global portfolio investment network exhibit higher comovement after I control for the level of trade connectedness. Countries that simultaneously occupy highly central positions in both financial and trade networks display even higher levels of stock market comovement. Moreover, linkages derived from total portfolio holdings matter just as much as or more than those derived only from equity linkages.     

Dynamic spillovers between uncertainties and green bond markets in the US,Europe, and China: Evidence from the quantile VAR framework

This paper investigates the quantile connectedness between uncertainties and green bonds in the US, Europe, and China by using a quantile VAR model-based connectedness approach. The empirical findings suggest that the spillover effect under extreme market conditions is significantly higher than that under normal market conditions. We also show that stock market uncertainty (VIX) and oil market uncertainty (OVX) have a greater impact on green bonds, especially in extreme upward markets. In addition, the US is the dominant transmitter of spillovers in other green bond markets, while China is always the net receiver of spillovers. Further research, meanwhile, demonstrates that the connectedness between green bonds and uncertainties is time-varying and that the spillover effects at extreme upper and lower quantiles are asymmetric and heterogeneous, especially in the early days of the COVID-19 pandemic. These findings provide investors and policymakers with systematic insights into the risk resistance of different green bond markets.