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.     

中国绿色债券市场与金融市场间的风险溢出效应研究

本文基于向量自回归模型的预测误差方差分解方法,研究中国绿色债券市场与传统固定收益市场、股票市场以及外汇市场等多种类型的金融市场间的风险溢出效应。实证结果表明,绿色债券市场与包括国债、高收益企业债券以及公司债券市场在内的传统固定收益市场的风险溢出效应最为显著,与股市和外汇市场间的风险溢出效应微弱;绿色债券市场的对外溢出效应强于其接收到的来自其他市场的溢出效应,并且绿色债券市场与传统固定收益市场间的风险溢出具有较大的不确定性。     

金融科技风险传染效应的识别与防范研究

为了研究金融科技风险在传统金融行业以及传统金融机构间的传染路径,本文以16家金融科技机构组成的金融科技行业和6个传统金融行业为样本数据,构建金融科技机构之间的关联网络,最后根据影响深度（风险溢出）和影响广度（关联网络）识别出金融科技行业及其机构的风险传染效应与影响路径。研究表明：第一,金融科技行业的时变波动率最大,且与其他相关行业间存在明显的风险传染效应;第二,与国有银行相比,其他非银金融与金融科技行业间的风险传染更大;第三,金融科技机构之间存在明显的以业务为聚集的核心。因而建议：第一,采用纵向监管与行为监管相结合的方式,重点监管和防范金融科技行业的风险溢出;第二,通过打造有利于金融科技业态发展的监管沙盒机制,建立相对完善的行业和机构内部监管制度,提高金融风险监管效率。     

全球系统性金融风险跨市场传染效应分析 ——基于货币市场/外汇市场金融网络的视角

金融全球化背景下,一国发生的系统性金融风险或金融危机,很可能会传染到其他国家,货币市场和外汇市场是传染的重要渠道.本文基于14个主要国家/地区2001年1月至2020年9月的利率和汇率数据进行计算分析,得到以下结论:(1)不同国家/地区的货币市场与外汇市场链接成强度不一的金融网络,金融风险可能通过金融网络实现跨国传染,对其他国家/地区的货币市场/外汇市场形成风险冲击.(2)这一网络具有明显的分层特征,网络中心层的风险溢出效应和风险输入效应较大;网络节点的作用不同,有的表现为风险净输出效应,有的表现为风险净输入效应.(3)随着国家之间经济地位变化和不同时期国际金融重大事件,不同国家/地区金融市场对风险事件的反应程度不同,全球跨货币市场/外汇市场网络的中心层国家/地区、风险传染路径也会相应调整.(4)网络中心层国家/地区并非总是重大金融风险事件的源发地,风险传染效应更多由具体国家/地区金融市场的行为反应和波动程度所决定.     

境内外人民币外汇市场的多阶矩风险溢出效应研究

本文测算了人民币汇率的意外波动率、条件偏度和条件峰度等多阶矩风险指标,结合DY溢出指数模型与DCC-GARCH模型研究了境内人民币外汇市场、香港离岸外汇市场和NDF市场之间的风险溢出效应。研究发现：人民币汇率的高阶矩波动率能够更好地捕捉到小概率冲击或极端冲击的影响;在频域视角下,境内外人民币外汇市场间存在显著的静态风险溢出效应,且意外波动率风险溢出效应持续期相对较长,境内市场是风险溢出的源头,往往以香港离岸市场为中介将风险传导至NDF市场;从时域角度看,各阶矩下的风险溢出效应都具有不断增强的时变特征,且对未预期冲击、小概率冲击和极端冲击较为敏感;就风险溢出效应所体现的风险联动性来看,境内外市场的风险联动性在各阶矩维度下也都是显著的。     

金融市场与实体经济主要行业之间的风险溢出效应

运用基于TVP-VAR模型的预测误差方差分解方法和Copula方法,量化分析金融各子市场与五大实体行业之间的风险溢出效应。实证结果表明,银行业和债市的风险溢出效应较大,银行业的影响更容易传导至工业和能源业等传统行业,而债市的影响更多传导到信息技术等新兴行业;汇市的风险溢出效应较小,且更容易传导至能源业。相较于其他金融市场,债券市场对实体行业的尾部风险溢出作用相对更强;国债波动对实体行业的风险溢出效应大,但是企业债与实体行业的尾部相关系数更大。金融市场和实体经济风险之间存在互相溢出,近年来金融市场成为风险被溢出者,尤其是外汇市场。因此,要将实体经济的需求特征纳入金融监管体系的考量范围,定期监测和分析金融市场风险,尤其控制银行和债券市场风险的溢出。     

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。     

中国房地产企业间系统性风险溢出效应分析——基于尾部风险网络模型

本文以2007—2021年沪深A股上市房企为样本,首先基于SIM单指数分位数回归技术提出测量系统性风险的新指标SIM-CoVaR,并结合前沿的TENET网络模型,构造跨房地产企业风险动态传染的尾部风险网络,然后采用块模型探究房地产市场系统性风险溢出的聚类性、触发机制及传播路径,最后考察网络整体结构和宏观经济变量对房地产市场系统性风险溢出的影响。研究表明:(1)我国房地产企业间存在明显的系统性风险联动性和溢出效应,在市场动荡时期房地产部门是金融风险溢出的放大器;(2)评估系统重要性节点企业时,除考虑企业规模等内部属性,还应考虑房企间关联结构,利用系统性风险指数可有效捕捉网络中系统重要性节点;(3)跨房企的系统性风险溢出具有显著的聚类特征,尾部风险网络可被划分为4个不同的功能模块,各模块的成员及其角色呈现明显的时变特性,监管部门可据此从供给端“因企施策”;(4)网络聚集性、网络效率和网络匹配性的降低能显著降低房地产市场的系统性风险溢出效应。本文从企业微观层面探讨房地产市场风险的形成机制,为促进房地产业健康发展和防范化解宏观层面的系统性金融风险提供参考。     

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.     

中国金融周期与横向关联:时空双维度相结合视角

宏观审慎政策关注各金融子市场在时间维度上的金融周期和空间维度上的横向关联。本文结合时间维度与空间维度视角,使用股票市场、货币市场、房地产市场以及信贷市场的数据,测算2001—2019年中国金融周期和横向关联的波动特征、作用关系与频域叠加机理。研究结果表明:时间维度金融周期与空间维度横向关联的波动趋势具有一致性。我国金融周期长度约为10.33年,横向关联波动周期的长度约为10.58年。从作用关系上看,首先,我国房地产周期达到波峰后,会对股票市场和信贷市场产生较强的溢出效应。随后,股市周期达到波峰后,会向房地产市场和信贷市场产生较强的溢出效应。最后,我国信贷市场接受股票市场和房地产市场溢出后,信贷周期会逐渐达到波峰。从频域叠加机理的角度看,我国金融子市场间横向关联的波动主要由中低频波段驱动,中低频波段横向关联的持续期在2个月以上。     

全球股票市场间风险传染的测度、监管及预警

近年来,伴随金融一体化程度的加深,全球各股票市场间风险传染的动态复杂性加剧,其准确测度、高效监管及实时预警已成为优先事项。本研究选取全球21个代表性股票市场作为分析样本,首先基于广义向量自回归模型的滚动估计准确测度其间风险动态传染的高维网络序列,进一步借由矩阵值因子模型来稳健收缩上述序列,以探究其潜在动态核心结构,从而实现高效监管。最后,通过向量自回归模型的预测功能实现对全球股票市场间风险传染的实时预警。研究表明,全球股票市场间风险传染具有时变性,其监管与预警可通过少数与地理区域高度相关的风险区域间的动态传染关系及内部的市场构成来刻画。与此同时,我们发现中国内地等新兴市场的重要地位逐渐凸显。本文研究结论可为有效防范与化解金融风险提供有益参考。     

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.     

全球股票市场间风险传染的测度、监管及预警

近年来,伴随金融一体化程度的加深,全球各股票市场间风险传染的动态复杂性加剧,其准确测度、高效监管及实时预警已成为优先事项。本研究选取全球21个代表性股票市场作为分析样本,首先基于广义向量自回归模型的滚动估计准确测度其间风险动态传染的高维网络序列,进一步借由矩阵值因子模型来稳健收缩上述序列,以探究其潜在动态核心结构,从而实现高效监管。最后,通过向量自回归模型的预测功能实现对全球股票市场间风险传染的实时预警。研究表明,全球股票市场间风险传染具有时变性,其监管与预警可通过少数与地理区域高度相关的风险区域间的动态传染关系及内部的市场构成来刻画。与此同时,我们发现中国内地等新兴市场的重要地位逐渐凸显。本文研究结论可为有效防范与化解金融风险提供有益参考。     

Dynamic volatility spillovers and investment strategies between the Chinese stock market and commodity markets

Building on the increased interest in the volatility spillover effects between Chinese stock market and commodity markets, this paper investigates the dynamic volatility spillovers of Chinese stock market and Chinese commodity markets based on the volatility spillover index under the framework of TVP-VAR. The result shows that there is a highly dependent relationship between the stock market and commodity markets. On average, the Chinese stock market is the net recipient of spillover, non-ferrous metals and chemical industry have a very obvious spillover impact on the stock market. The degree of total volatility spillover is different in different periods. After major crisis events, the volatility correlation between markets increases. Since the outbreak of COVID-19, the spillover effect of the stock market on the commodity market has been significantly enhanced. Then optimal portfolio weights and hedge ratios are calculated for portfolio diversification and risk management. The result shows that the ability of most commodities to hedge against risks is significantly reduced when the crisis occurs; NMFI (precious metals) and CRFI (grain) still have good hedging ability after the crisis, but the effectiveness of hedging risk is relatively low. Besides, the combination of CRFI and SHCI (the Shanghai composite index) is the most effective for risk reduction.     

‘Slow-burn’ spillover and ‘fast and furious’ contagion: a study of international stock markets

‘Fast and furious’ contagion across capital markets is an important phenomenon in an increasingly integrated financial world. Different from ‘slow-burn’ spillover or interdependence among these markets, ‘fast and furious’ contagion can occur instantly. To investigate this kind of contagion from the US, Japan and Hong Kong to other Asian economies, we design a research strategy to capture fundamental interdependence, or ‘slow-burn’ spillover, among these stock markets as well as short-term departures from this interdependence. Based on these departures, we propose a new contagion measure which reveals how one market responds over time to a shock in another market. We also propose international portfolio analysis for contagion via variance decomposition from the portfolio manager’s perspective. Using this research strategy, we find that the US stock market was cointegrated with the Asian stock markets during four specific periods from 3 July 1997 to 30 April 2014. Beyond this fundamental interdependence, the shocks from both Japan and Hong Kong have significant ‘fast and furious’ contagion effects on other Asian stock markets during the US subprime crisis, but the shocks from the US have no such effects.     

Risk transmission between Islamic and conventional stock markets: A return and volatility spillover analysis

This paper contributes to the current debate on the empirical validity of the decoupling hypothesis of the Islamic stock market from its mainstream counterparts by examining return and volatility spillovers across the global Islamic stock market, three main conventional national stock markets (the US, the UK and Japan) and a number of influential macroeconomic and financial variables over the period from July 1996 to June 2016. To that end, the VAR-based spillover index approach based on the generalized VAR framework developed by Diebold and Yilmaz (2012) is applied. The empirical analysis shows strong interactions in return and volatility among the global Islamic stock market, the conventional stock markets and the set of major risk factors considered. This finding means that the Islamic equity universe does not constitute a viable alternative for investors who wish to hedge their investments against the vagaries of stock markets, but it is exposed to the same global factors and risks hitting the conventional financial system. Therefore, this evidence leads to the rejection of the decoupling hypothesis of the Islamic stock market from conventional stock markets, which has significant implications for faith-based investors and policy makers in terms of portfolio diversification, hedging strategies and contagion risk.     

沪深300股指期货与现货市场间风险传染效应及影响因素

本文选取2005—2019年我国沪深300股指期货和沪深300股票指数日收盘价数据,结合股票推出时间、股价波动性,设置样本组、对照组,运用GARCH模型、DCC-GARCH模型、Granger因果关系检验及多元线性回归模型分析了沪深300股指期货与现货间的风险传染效应及影响因素,并结合研究结论提出对策,以期促进资本市场健康发展。结果表明:沪深300股指期货市场与现货市场间存在双向的风险传染效应,且经DCC-GARCH模型分析表明风险传染效应在动荡期尤为明显;影响这种风险传染效应的因素有很多,主要表现为微观因素中的股票市场流动性和股票市场不确定性与极端事件两个方面。     

Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management

In this article we take a recent generalized VAR-GARCH approach to examine the extent of volatility transmission between oil and stock markets in Europe and the United States at the sector-level. The empirical model is advantageous in that it typically allows simultaneous shock transmission in the conditional returns and volatilities. Insofar as volatility transmission across oil and stock sector markets is a crucial element for portfolio designs and risk management, we also analyze the optimal weights and hedge ratios for oil-stock portfolio holdings with respect to the results. Our findings point to the existence of significant volatility spillover between oil and sector stock returns. However, the spillover is usually unidirectional from oil markets to stock markets in Europe, but bidirectional in the United States. Our back-testing procedures, finally, suggest that taking the cross-market volatility spillovers estimated from the VAR-GARCH models often leads to diversification benefits and hedging effectiveness better than those of commonly used multivariate volatility models such as the CCC-GARCH of Bollerslev (1990), the diagonal BEKK-GARCH of Engle and Kroner (1995) and the DCC-GARCH of Engle (2002).     

Volatility spillovers between oil prices and the stock market under structural breaks

This paper employs univariate and bivariate GARCH models to examine the volatility of oil prices and US stock market prices incorporating structural breaks using daily data from July 1, 1996 to June 30, 2013. We endogenously detect structural breaks using an iterated algorithm and incorporate this information in GARCH models to correctly estimate the volatility dynamics. We find no volatility spillover between oil prices and US stock market when structural breaks in variance are ignored in the model. However, after accounting for structural breaks in the model, we find strong volatility spillover between the two markets. We compute optimal portfolio weights and dynamic risk minimizing hedge ratios to highlight the significance of our empirical results which underscores the serious consequences of ignoring these structural breaks. Our findings are consistent with the notion of cross-market hedging and sharing of common information by financial market participants in these markets.