Analysis of the impact of COVID-19 pandemic on G20 stock markets

We examine the impact of the COVID-19 pandemic on G20 stock markets from multiple perspectives. To measure the impact of COVID-19 on cross-market linkages and deeply explore the dynamic evolution of risk transmission relations and paths among G20 stock markets, we statically and dynamically measure total, net, and pairwise volatility connectedness among G20 stock markets based on the DY approach by Diebold and Yilmaz (2012, 2014). The results indicate that the total volatility connectedness among G20 stock markets increases significantly during the COVID-19 crisis, moreover, the volatility connectedness display dynamic evolution characteristics during different periods of the COVID-19 pandemic. Besides, we also find that the developed markets are the main spillover transmitters while the emerging markets are the main spillover receivers. Furthermore, to capture the impact of COVID-19 on the volatility spillovers of G20 stock markets, we individually apply the spatial econometrics methods to analyze both the direct and indirect effects of COVID-19 on the stock markets’ volatility spillovers based on the “volatility spillover network matrix” innovatively constructed in this paper. The empirical results suggest that stock markets react more strongly to the COVID-19 confirmed cases and cured cases than the death cases. In general, our study offers some reference for both the investors and policymakers to understand the impact of COVID-19 on global stock markets.     

COVID-19 and asymmetric volatility spillovers across global stock markets

In this study, I improve the assessment of asymmetry in volatility spillovers, and define six asymmetric spillover indexes. Employing Diebold-Yilmaz spillover index, network analysis, and my developed asymmetric spillover index, this study investigates the time-varying volatility spillovers and asymmetry in spillovers across stock markets of the U.S., Japan, Germany, the U.K., France, Italy, Canada, China, India, and Brazil based on high-frequency data from June 1, 2009, to August 28, 2020. I find that the global markets are well connected, and volatility spillovers across global stock markets are time-varying, crisis-sensitive, and asymmetric. Developed markets are the main risk transmitters, and emerging markets are the main risk receivers. Downside risk dominates financial contagion effects, and a great deal of downside risk spilled over from stock markets of risk transmitters into the global markets. Moreover, during the coronavirus recession, the total degree of volatility spillover is staying at an extremely high level, and emerging markets are the main risk receivers in the 2020 stock markets crash.     

Risk spillover and network connectedness analysis of China’s green bond and financial markets: Evidence from financial events of 2015–2020

This study investigated the dynamic return and volatility spillovers, together with the network connectedness analysis between China’s green bond and main financial markets. Based on a multidimensional DCC-GJRGARCH model and the spillover index method, we found significant two-way risk spillovers between the green bond market and traditional bond markets. Moreover, the green bond market was subject to one-way risk spillover from the stock and commodities markets. Meanwhile, risk spillovers between the green bond market, forex market, and monetary market were not significant. Finally, network connectedness analysis provided specific information about connectivity and strength during different subperiods corresponding to financial events. The analysis indicated that under the influence of emergencies, China’s financial market will enhance the risk-spillover level by transforming the same type of market’s internal spillover into cross-market spillover.     

Intraday Volatility Spillover between the Shanghai and Hong Kong Stock Markets—Evidence from A+H Shares after the Launch of the Shanghai-Hong Kong Stock Connect

Using minute data of eligible A+H stocks under the Shanghai-Hong Kong Stock Connect (SHHKSC), we investigate the volatility spillover between the Shanghai and Hong Kong stock markets based on a generalized autoregressive conditional heteroskedasticity-X (GARCH-X) model with four exogenous variables, namely, volatilities of the corresponding stocks on the other market, volatilities of the indexes of both stock markets, and volatilities of the correlated stocks, which are selected using the dynamic conditional correlation model and bootstrap approach. Results show that after the launch of the SHHKSC, volatility spillovers are significant in both directions almost all the time, and the volatility spillover between the two stock markets tends to be larger when bidirectional capital flows under the SHHKSC increase or when important financial events occur. We also analyze the influences of the volatilities of correlated stocks and industries on the volatility spillover and volatilities of A+H stocks. The bidirectional volatility spillovers between Shanghai and Hong Kong stock markets do not change qualitatively after incorporating the volatilities of correlated stocks and industries in the GARCH-X model. Moreover, the average volatilities of the correlated stocks are shown to have significant influences on the volatilities of individual A+H stocks, and the influences increase when the local stock market shows a sharp rise or fall. Compared with the market indexes, the correlated stocks could be regarded as a more important and indispensable factor for individual A+H stocks’ volatilities modeling, which may carry more information than the industry.     

国际金融市场波动溢出效应与动态相关性

在DCC GARCH、DCC EGARCH、DCC TGARCH方法下,采用中、美、日、德、英等国家1993年1月至2013年12月的金融数据,实证得出如下结论：样本国市场利率和股指波动率呈现尖峰、肥尾、有偏的特征,更为符合t分布。样本国市场利率波动表现出显著的溢出效应、杠杆效应和联动效应。样本国股指波动率对中国股指波动率的溢出效应趋于增强,特别在美国金融危机后。样本国利率波动对中国股指波动率具有一定的溢出效应和杠杆效应,但影响程度非常低。治理世界性金融风险,各国当局应加强政策协调性,合理进行风险分担。     

Analyzing quantile spillover effects among international financial markets

This paper investigates the quantile-based spillover effects among 17 stock markets from January 1993 to January 2022, utilizing a quantile approach based on the variance decomposition of a quantile vector autoregression (QVAR) model. Compared with the traditional mean-based spillover measures, this new quantile approach allows for a nuanced investigation of spillovers at every quantile and capture spillovers under extreme events. The results show that: (1) the total spillover is high and exhibits strong time-varying characteristics, and the tail spillover is higher and more complex in scale and direction; (2) the spillover at each quantile level shows an upward trend, especially during the 2008 crisis and the COVID-19 epidemic; (3) developed countries (or regions) are the net exporters of stock market spillovers, while the developing countries are the net importers; and (4) the 17 stock markets constitute different local financial networks, which may be related to economic conditions and geographical location.     

Multiscale features of extreme risk spillover networks among global stock markets

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time–frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.     

Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak

In this study, we examine oil price extreme tail risk spillover to individual Gulf Cooperation Council (GCC) stock markets and quantify this spillover’s shift before and during the COVID-19 pandemic. A dynamic conditional correlation generalized autoregressive heteroscedastic (DCC- GARCH) model is employed to estimate three important measures of tail dependence risk: conditional value at risk (CoVaR), delta CoVaR (ΔCoVaR), and marginal expected shortfall (MES). Using daily data from January 2017 until May 2020, results point to significant systemic oil risk spillover in all GCC stock markets. In particular, the effect of oil price systemic risk on GCC stock market returns was significantly larger during COVID-19 than before the pandemic. Upon splitting COVID-19 into two phases based on severity, we identify Saudi Arabia as the only GCC market to have experienced significantly higher exposure to oil risk in Phase 1. Although all GCC stock markets received greater oil systemic risk spillover in Phase 2 of COVID-19, Saudi Arabia and the United Arab Emirates appeared more vulnerable to oil extreme risk than other countries. Our empirical findings reveal that investors should carefully consider the extreme oil risk effects on GCC stock markets when designing optimal portfolio strategies, minimizing portfolio risk, and adopting dynamic diversification process. Policymakers and regulators should also enact awareness, oversight, and action plans to minimize adverse oil risk effects.     

Risk spillover between the US and the remaining G7 stock markets using time-varying copulas with Markov switching: Evidence from over a century of data

This paper analyses the risk spillover effect between the US stock market and the remaining G7 stock markets by measuring the conditional Value-at-Risk (CoVaR) using time-varying copula models with Markov switching and data that covers more than 100 years. The main results suggest that the dependence structure varies with time and has distinct high and low dependence regimes. Our findings verify the existence of risk spillover between the US stock market and the remaining G7 stock markets. Furthermore, the results imply the following: 1) abnormal spikes of dynamic CoVaR were induced by well-known historical economic shocks; 2) The value of upside risk spillover is significantly larger than the downside risk spillover and 3) The magnitudes of risk spillover from the remaining G7 countries to the US are significantly larger than that from the US to these countries.     

Economic policy uncertainty and stock market sector time-varying spillover effect: Evidence from China

This paper investigates the volatility spillover effect among the Chinese economic policy uncertainty index, stock markets, gold and oil by employing the time-varying parameter vector autoregressive (TVP-VAR) model. Three main results are obtained. Firstly, the optional consumption, industry, public utility and financial sectors are systemically important during the sample period. Secondly, among the four policy uncertainties, the uncertainty of fiscal policy and trade policy contributes more to the spillover effect, while the uncertainty of monetary policy and exchange rate policy contributes less to the spillover effect. Thirdly, during COVID-19, oil spillovers from other sources dropped rapidly to a very low point, it also had a significant impact on the net volatility spillover of the stock market. This paper can provide policy implication for decision-makers and reasonable risk aversion methods for investors.     

Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network

This paper investigates the systemic risk spillovers and connectedness in the sectoral tail risk network of Chinese stock market, and explores the transmission mechanism of systemic risk spillovers by block models. Based on conditional value at risk (CoVaR) and single index model (SIM) quantile regression technique, we analyse the tail risk connectedness and find that during market crashes, stock market exposes to more systemic risk and more connectedness. Further, the orthogonal pulse function shows that Herfindahl-Hirschman Index (HHI) of edges has a significant positive effect on systemic risk, but the impact shows a certain lagging feature. Besides, the directional connectedness of sectors shows that systemic risk receivers and transmitters vary across time, and we adopt PageRank index to identify systemically important sector released by utilities and financial sectors. Finally, by block model we find that the tail risk network of Chinese sectors can be divided into four different spillover function blocks. The role of blocks and the spatial spillover transmission path between risk blocks are time-varying. Our results provide useful and positive implications for market participants and policy makers dealing with investment diversification and tracing the paths of risk shock transmission.     

Measuring extreme risk spillovers across international stock markets: A quantile variance decomposition analysis

This paper proposes a quantile variance decomposition framework for measuring extreme risk spillover effects across international stock markets. The framework extends the spillover index approach suggested by Diebold and Yilmaz (2009) using a quantile regression analysis instead of the ordinary least squares estimation. Thus, the framework provides a new tool for further study into the extreme risk spillover effects. The model is applied to G7 and BRICS stock markets, from which new insights emerged as to the extreme risk spillovers across G7 and BRICS stock markets, and revealed how extreme risk spillover across developed and emerging stock markets. These findings have important implications for market regulators.     

Country risk and stock market volatility,predictability, and diversification in the Middle East and Africa

With globalization, an understanding of country risk (political risk (PR), financial risk (FR), and economic risk (ER)) and its impact on stock market return volatility and predictability is important for evaluating direct investment and country selection decisions in globally and regionally diversified portfolios. This paper examines these issues in the context of the Middle East and Africa (MEAF) and analyzes 10 stock markets in the region over the period 1984–1999. After examining volatility and predictability, this paper explains how portfolios of stocks can be formed from these countries in order to achieve mean–variance efficient portfolios. This paper generally finds that country political, financial and economic risks significantly determine stock volatility and predictability. The diversification exercise shows that an international investor can still benefit by diversifying into the stock markets of Middle East and African countries.     

On the Link between Volatility and Growth: A Spatial Econometrics Approach

This paper examines the link between macro volatility and economic growth in the lens of spatial econometrics. We present an unconstrained spatial Durbin Ramey-Ramey model. We test the extended model in a panel of 78 countries to investigate all the possible dimensions along which spatial interactions can affect the link between macro volatility and growth. In contrast to previous literature, we split the effects of volatility on growth into direct and indirect effects using partial derivative impacts approach. We found that both the direct and indirect effects of volatility on growth are negative; the latter effect suggesting the transmission of volatility shocks to neighbouring countries. Growth rates observed in neighbouring countries has a positive effect on growth rate of a particular country.     

Dynamic behaviors and contributing factors of volatility spillovers across G7 stock markets

This paper investigates the evolutions and determinants of volatility spillover dynamics in G7 stock markets in a time-frequency framework. We decompose volatility spillovers into short-, medium-, and long-term components, using a spectral representation of variance decompositions. The impacts of hypothesized factors on the decomposed volatility spillovers are also examined, using a linear regression model and fixed effects panel model. We find that the volatility spillovers across G7 stock markets are crisis-sensitive and are, in fact, closer to a memory-less process. The low-frequency components are the main contributors to the volatility spillovers; the high-frequency components are very sensitive to market event shocks. Moreover, our results reveal that the contributing factors have different effects on short-, medium-, and long-term volatility spillovers. There is no systematic pattern of the impacts of the contributing factors on volatility spillovers. However, whether the country is the transmitter or recipient of volatility spillovers could be a potential reason.     

Analysis of the cross-region risk contagion effect in stock market based on volatility spillover networks: Evidence from China

This article uses the stock market regional indexes of 31 provinces (include Province-level municipalities and Minority Autonomous Regions) in mainland China as a sample, and constructs an inter-regional volatility spillover network of China’s stock market based on the GARCH-BEKK model. Through network centrality analysis, Diebold and Yilmaz's spillover index method and block model analysis, we comprehensively analyze the risk contagion effect among different regions in China’s stock market. The empirical results show that: (i) The risk contagion intensity (risk reception intensity) in various regions of China’s stock market has a typical “core-periphery” distribution characteristic due to regions’ different levels of economic development. (ii) There are obvious risk spillover effect in China’s stock market, among which the economically developed regions along the southeastern coast of China, such as Beijing, Shanghai, Zhejiang and Jiangsu, are the main risk transmitters, while the economically undeveloped regions in the Midwest of China, such as Xinjiang, Xizang, Gansu, Nei Menggu and Qinghai are the main risk receivers. (iii) Each region is divided into 4 blocks according to their respective roles in the risk spillover process in China’s stock market. Block 1 that is composed of the economically underdeveloped regions in the Midwest is the “main benefit block”, it acts as a “receiver”. Block 2 that is composed of regions with strong economic growth vitality in the Midwest is a “Bilateral spillover block”, it both plays the role of “receiver” and “transmitter”. Block 3 that is composed of developed regions along the southeast coast, it acts as a “transmitter”; Block 4 that is composed of the relatively fast-growing regions in the Southwest is the “brokers block”, it serves as a “bridge”. The results of this article can provide some reference for investors in financial institutions and decision makers in financial regulators.     

互联网基金对股票市场的影响——基于大数据情绪指数的实证研究

投资者在进行投资决策时易受到自身情绪的影响，并且投资者行为是影响金融市场间波动溢出的直接原因。运用文本挖掘技术对新浪微博2014年4月至2016年7月的博文进行文本分析和随机森林主成分分析并构建微博大数据投资者情绪指数，根据投资者情绪指数研究互联网基金市场对股票市场的影响，结果表明互联网基金市场对股票市场具有波动溢出效应。     

Modelling international sovereign risk information spillovers: A multilayer network approach

Applying the TVP-VAR model, we creatively construct multilayer information spillover networks containing return spillover layer, volatility spillover layer and extreme risk spillover layer among 23 countries in the G20 to explore international sovereign risk spillovers. From the perspective of system-level and country-level measures, this article explores the topological structures of static and dynamic multilayer networks. We observe that (i) at the system-level, multilayer measures containing uniqueness edge ratio and average edge overlap show each layer has unique network structures and spillover evolution behavior, especially for dynamic networks. Average connectedness strength shows volatility and extreme risk spillover layers are more sensitive to extreme events. Meanwhile, three layers have highly intertwined and interrelated relations. Notably, their spillovers all show a great upsurge during the crisis (financial and European debt crisis) and the COVID-19 pandemic period. (ii) At the country-level, average overlapping net-strength shows that countries’ roles are different during distinct periods. Multiplex participation coefficient on out-strength indicates we’ll focus on countries with highly heterogeneous connectedness among three layers during the stable period since their underestimated spillovers soar in extreme events or crises. Multilayer networks supply comprehensive information that cannot obtain by single-layer.     

欧债危机冲击我国股票与债券市场的风险传染研究？

欧债危机对金融市场产生了显著的冲击,引发了巨大的风险。本文通过构建二元GARCH-BEKK模型,实证检验了欧债危机背景下欧洲股票市场、我国股票市场、国债市场与企业债市场之间的波动溢出效应,揭示了欧债危机冲击我国股票市场、国债市场与企业债市场的风险传染路径。实证表明,欧债危机冲击我国股票市场与债券市场的风险传导路径为：欧债危机引发的风险通过欧洲股票市场传导到我国股票市场,然后传导到企业债市场,最后传导到国债市场。     

Contagion effects and risk transmission channels in the housing,stock, interest rate and currency markets: An Empirical Study in China and the U.S.

This paper aims to investigate the crisis linkage and transmission channels within the housing, stock, interest rate and the currency markets in the U.S. and China in the past decade since the 2008 Subprime Mortgage Crisis. Two hybrid models, namely the SWARCH-EVT-Copula and the Bivariate SWARCH-EVT models, are proposed and applied in order to take into account (A) the high/low volatility regimes, (B) the interdependence structure inherited from the joint tail behaviours, as well as, (C) the risk spillover dynamics among financial sectors during market turmoils. We empirically show that the housing and stock markets share the strongest linkage and play central roles in the spreading of shocks. With a highly integrated system, the American financial sectors are under greater exposure to risk contagion and systemic risk during crises than the Chinese markets. Nevertheless, the exchange rate risk of Renminbi remains at an intensive level since its “crawl-like arrangement” and leads to increasing co-movements in the stock and interest rate markets since 2014.