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.     

Common shocks,common transmission mechanisms and time-varying connectedness among Dow Jones Islamic stock market indices and global risk factors

The purpose of this paper is twofold. First, we examine the importance of permanent versus transitory shocks as well as their domestic and foreign components in explaining the business cycle fluctuations of seven Dow Jones Islamic stock markets (DJIM), namely U.S., U.K., Canada, Europe, Asia-Pacific, Japan and GCC, over the period from April 2003 to November 2018, using the permanent-transitory (P-T) decompositions approach of Centoni et al. (2007). Second, we investigate the spillover mechanisms of these shocks across Islamic stock markets and a set of global risk factors, using the Diebold and Yilmaz (DY) (2012) approach. The P-T decomposition results show that the DJIM U.S., U.K., Europe and GCC indices are sensitive to both domestic and foreign shocks, while the DJIM Canada, Japan and Asia-Pacific are most sensitive to domestic shocks. The empirical results of the DY approach indicate that: (i) the return and volatility spillover intensity increase during financial turmoil, supporting evidence of the contagion phenomenon, (ii) the DJIM U.S. is the main transmitter of return and volatility spillovers, while the DJIM GCC is identified as the main receiver of both return and volatility spillovers, (iii) the seven Dow Jones Islamic stock indices are weakly linked to movements of global risk factors, and (iv) there is evidence of possible portfolio diversification between the selected Islamic stock markets and the oil commodity market.     

Asymmetric volatility connectedness among U.S. stock sectors

This paper examines the dynamic asymmetric volatility connectedness among ten U.S. stock sectors (Consumer Goods, Consumer Services, Financials, Health Care, Materials, Oil and Gas, Technology, Telecom, Real Estate Investment Trust (REIT), and Utilities). We use the methodology of Diebold and Yilmaz (2012, 2014, 2016) and the realized semivariances introduced by Baruník et al. (2017) to five-minute data. The results show evidence of time-varying spillovers among U.S. stock sectors which is intensified during economic, energy and geopolitical events. Moreover, the spillovers under bad volatility dominates the spillovers under good volatility, supporting evidence of asymmetry. Financials, Materials, Oil and Gas, REIT, Technology, Telecom and Utilities are net receiver of spillover under good volatility (positive semivariance). In contrast, Oil and Gas shift to net contributor of spillover under bad volatility (negative semivariance). Moreover, the connectedness network among sectors exhibits asymmetric behaviors. These results have important implications for risk management.     

Risk spillovers and hedge strategies between global crude oil markets and stock markets: Do regime switching processes combining long memory and asymmetry matter?

This paper investigates risk spillovers and hedge strategies between global crude oil markets and stock markets. In the paper, we propose a multivariate long memory and asymmetry GARCH framework that integrates state-dependent regime switching in the mean process with multivariate long memory and asymmetry GARCH in the variance process. Our results first show that there are linear risk spillovers running from the US stock markets to the WTI oil market in the short term. However, the linear risk spillover effect running from the oil market to the US stock market can only exist in the long term. In addition, there is a bidirectional linear risk spillover effect between the European stock markets and the Brent oil market in the short and long terms. Furthermore, there is no linear risk spillover effect between the Dubai oil market and the Chinese stock market. Second, the nonlinear risk spillovers running from the WTI oil market to the US stock market can be found in the tranquil regime. Moreover, there is also a nonlinear risk spillover effect running from the European stock markets to the Brent oil market in the tranquil regime. In addition, the nonlinear risk spillover effect running from the Brent oil markets to the European stock market can be found in the crisis regime. Furthermore, there is bidirectional nonlinear Granger causality between the Dubai crude oil market and the Chinese stock market in the tranquil regime. Finally, dynamic hedge effectiveness shows that the regime switching process combined with long memory and asymmetry behavior seems to be a plausible and feasible way to conduct hedge strategies between the global crude oil markets and stock markets.     

Spillover and connectedness among G7 real estate investment trusts: The effects of investor sentiment and global factors

This paper examines the dynamic spillover interconnectedness of G7 Real Estate Investment Trusts (REITs) markets. We use the spillover index of Diebold and Yilmaz (2012), the time-varying parameters vector-autoregression (TVP-VAR) model, and the quantile regression approach. The result show that REITs network connectedness is dynamic and experiences an abrupt increase in the first wave of COVID-19 outbreak (2020Q1). We also observe a substantial abrupt decrease in connectedness during the success of vaccination programs (end 2021). The connectedness among assets is much stronger during COVID-19 than before. The REITs of Japan and Italy are net receivers of spillover and those of US and UK are net transmitters of spillovers before and during COVID-19. Conversely, the REIT of Canada and Germany (France) switches from net receivers (contributors) of spillovers before the pandemic to net contributors (receivers) during the COVID-19. Finally, we show that News Sentiment index, Geopolitical Risk index, Economic Policy Uncertainty index, US Treasury yield, and Stock Volatility index influence the spillover magnitude across quantiles.     

Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock market

This paper studies the asymmetric spillover effect of important economic policy uncertainty (EPU) on the S&P500 index. We use monthly EPU indexes from Australia, Canada, China, Japan, the U.K. and the U.S. and the realized volatility of the U.S. stock market to study the asymmetric pairwise directional spillovers on the U.S. stock market from 2000 to 2019. We find that S&P500 index volatility is a net recipient of spillovers from important EPU indexes. Japanese EPU has the strongest spillover effect on the U.S. stock markets, while EPU from the U.K. plays a very limited role. By decomposing the volatility into good and bad volatility, we find that the relationship between bad stock market volatility and EPU is stronger than between good volatility and EPU. Time-varying spillover characteristics show that bad volatility reacts more strongly to shocks in EPU following the debt crisis and trade negotiations. Several robustness checks are provided to verify the novelty of these findings.     

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.     

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.     

Cross-region risk spillover between the stock and stock index futures markets under exogenous shocks

By integrating the stock and futures markets of mainland China and Hong Kong into the same financial system, we explore the cross-region risk spillovers between the stock market and stock index futures market under the impact of exogenous events. We find evidence of significant risk spillovers between the two stock markets, and confirm that exogenous shocks, including the adjustments of regulatory policies of mainland China and 2019 Hong Kong Protest, can significantly affect the volatility spillover across assets and markets. Our findings can potentially help regulators and investors understand the cross-region risk conduction and assess portfolio risk after exogenous event.     

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.     

Spatial spillover effects and risk contagion around G20 stock markets based on volatility network

Employing the spatial econometric model as well as the complex network theory, this study investigates the spatial spillovers of volatility among G20 stock markets and explores the influential factors of financial risk. To achieve this objective, we use GARCH-BEKK model to construct the volatility network of G20 stock markets, and calculate the Bonacich centrality to capture the most active and influential nodes. Finally, we innovatively use the volatility network matrix as spatial weight matrix and establish spatial Durbin model to measure the direct and spatial spillover effects. We highlight several key observations: there are significant spatial spillover effects in global stock markets; volatility spillover network exists aggregation effects, hierarchical structure and dynamic evolution features; the risk contagion capability of traditional financial power countries falls, while that of “financial small countries” rises; stock market volatility, government debt and inflation are positively correlated with systemic risk, while current account and macroeconomic performance are negatively correlated; the indirect spillover effects of all explanatory variables on systemic risk are greater than the direct spillover effects.     

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.     

Upside/Downside spillovers between oil and Chinese stock sectors: From the global financial crisis to global pandemic

This paper examines the effects of the COVID-19 outbreak, recent oil price fall, and both global and European financial crises on dependence structure and asymmetric risk spillovers between crude oil and Chinese stock sectors. Using time-varying symmetric and asymmetric copula functions and the conditional Value at Risk measure, we provide evidence of positive tail dependence in most sectors using copula and conditional Value-at-Risk techniques. We can see the average dependence between oil and industries during the oil crisis. Moreover, we find strong evidence of bidirectional risk spillovers for all oil-sector pairs. The intensity of risk spillovers from oil to all stock sectors varies across sectors. The risk spillovers from sectors to oil are substantially larger than those from oil to sectors during COVID-19. Furthermore, the return spillover is time varying and sensitive to external shocks. The spillover strengths are higher during COVID-19 than financial and oil crises. Finally, oil do not exhibit neither hedge nor safe-haven characteristics irrespective of crisis periods.     

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.     

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.     

Dynamic spillovers and linkages between gold,crude oil,S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switch between positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.     

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。     

Similarity of emerging market returns under changing market conditions: Markets in the ASEAN-4, Latin America,Middle East,and BRICs

We studied the risk-return distances of 18 emerging stock markets in the period from January 2000 to December 2013. Distances are linked to volatility and time-varying correlations estimated in standard and asymmetric DCC models. Our results revealed a positive relationship between risk-return distances and volatility, which means that during more volatile periods, the risk-return characteristics in emerging markets exhibit lower similarity to the characteristics found in developed markets. This result seems to be in sharp contrast to most empirical studies using correlations. Within the portfolio framework, our results suggest that diversification into emerging stock markets may still provide desirable benefits to international investors.     

Risk spillover analysis across worldwide ESG stock markets: New evidence from the frequency-domain

With the increasing global awareness of green environmental protection, the international environmental, social, and governance (ESG) stock markets are developing rapidly together with rising risk linkages across worldwide markets. Therefore, this study explores the risk spillover characteristics of international ESG stock markets in the time and frequency domains and constructs a risk linkage network to further explore the risk contagion mechanism. The results show that in most cases, the developed North American market is the core of outward risk spillover in international ESG stock markets. The entire system presents a small-world structure, and the internal regions display different risk spillover characteristics. Moreover, international ESG markets generally have strong time–frequency spillover and medium-frequency (a month to a year) spillover. In contrast, the high- (a day to a month) and low-frequency (more than one year) spillovers are located at relatively low levels, but they will rise significantly under sudden financial events. The empirical results expand the ESG stock market's theoretical framework and provide a reference for investors and market regulators to reduce the investment risk of ESG.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.