Impacts of COVID-19 on global stock sectors: Evidence from time-varying connectedness and asymmetric nexus analysis

In this paper, we analyze the impact of the COVID-19 crisis on global stock sectors from two perspectives. First, to measure the effect of the COVID-19 on the volatility connectedness among global stock sectors in the time–frequency domain, we combine the time-varying connectedness and frequency connectedness method and focus on the total, directional, and net connectedness. The empirical results indicate a dramatic rise in the total connectedness among the global stock sectors following the outbreak of COVID-19. However, the high level of the total connectedness lasted only about two months, representing that the impact of COVID-19 is significant but not durable. Furthermore, we observe that the directional and net connectedness changes of different stock sectors during the COVID-19 pandemic are heterogeneous, and the diverse possible driving factors. In addition, the transmission of spillovers among sectors is driven mainly by the high-frequency component (short-term spillovers) during the full sample time. However, the effects of the COVID-19 outbreak also persisted in the long term. Second, we explore how the changing COVID-19 pandemic intensity (represented by the daily new COVID-19 confirmed cases and the daily new COVID-19 death cases worldwide) affect the daily returns of the global stock sectors by using the Quantile-on-Quantile Regression (QQR) methodology of Sim and Zhou (2015). The results indicate the different characteristics in responses of the stock sectors to the pandemic intensity. Specifically, most sectors are severely impacted by the COVID-19. In contrast, some sectors (Necessary Consume and Medical & Health) that are least affected by the COVID-19 pandemic (especially in the milder stage of the COVID-19 pandemic) are those that are related to the provision of goods and services which can be considered as necessities and substitutes. These results also hold after several robustness checks. Our findings may help understand the sectoral dynamics in the global stock market and provide significant implications for portfolio managers, investors, and government agencies in times of highly stressful events like the COVID-19 crisis.     

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.     

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.     

Time and frequency connectedness and portfolio diversification between cryptocurrencies and renewable energy stock markets during COVID-19

The assessment of the time and frequency connectedness between cryptocurrencies and renewable energy stock markets is of key interest for portfolio diversification. In this paper, we utilize weekly data from 07 August 2015 to 26 March 2021 to document the dynamics and portfolio diversification from a fresh cryptocurrencies-renewable energy perspective. Our time-frequency domain spillovers results reveal that renewable energy stocks are the main spillover contributors in the connectedness system and the short-run spillovers dominate their long-run counterparts. Furthermore, investors can gain more profits through short-run transactions in our portfolio design and we can optimize portfolios by investing a large portion in cryptocurrencies. A fascinating fact is that the COVID-19 pandemic can reverse the effectiveness of our hedging strategy.     

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.     

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.     

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.     

The transition of the global financial markets' connectedness during the COVID-19 pandemic

This study contributes to the literature on financial research under the presence of the COVID-19 pandemic. Fresh evidence emerges from using two novel approaches, namely network analysis and wavelet coherence, to examine the connectedness and comovement of financial markets consisting of stock, commodity, gold, real estate investment trust, US exchange, oil, and Cryptocurrency before and during the COVID-19 onset. Moreover, unlike the previous studies, we seek to fill a gap in the literature regarding the ex-post detection of COVID-19 crises and propose the Markov-switching autoregressive model to detect structural breaks in financial market returns. The first result shows that most financial markets entered the downtrend after January 30, 2020, coinciding with the date the World Health Organization (WHO) declared the COVID-19 pandemic as a Public Health Emergency of International Concern. Thus, it is reasonable to use this date as the break date due to COVID-19. The empirical result from network analysis indicates a similar connectedness, or the network structure, in other words, among global financial markets in both the pre-and during COVID-19 pandemic periods. Moreover, we find evidence of market differences as the MSCI stock market plays a central role while Cryptocurrency presents a weak role in the global financial markets. The findings from the wavelet coherence analysis are quite mixed and illustrate that the comovement of the financial markets varies over time across different frequencies. We also find the main and most significant period of coherence and comovement among financial markets to be between December 2019 and August 2020 at the low-frequency scale (>32 days) (middle and long terms). Among all market pairs, the oil and commodity market pair has the strongest comovement in both pre-and during the COVID-19 pandemic phases at all investment horizons.     

Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday

We examine the volatility spillovers among various industries during the COVID-19 pandemic period. We measure volatility spillovers by defining the volatility of each sector in the S&P 500 index and implement a static and rolling-window analysis following the Diebold and Yilmaz (2012) approach. We find that the pandemic enhanced volatility spillovers, which reveals the financial contagion effects on the US stock market. Second, there were sudden, large changes in the dynamic volatility spillovers on Black Monday (March 9, 2020), much of it due to the energy sector shock. These findings have important implications for portfolio managers and policymakers.     

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.     

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.     

Dynamic volatility connectedness between industrial metal markets

Combined with the spillover framework of Diebold and Yilmaz (2009, 2012, 2014) and the TVP-VAR-SV model of Primiceri (2005), this paper studies the dynamic volatility connectedness between six major industrial metal (i.e., aluminum, copper, lead, nickel, tin and zinc) spot and futures markets. The results show that: (1) The total volatility connectedness between industrial metal spot or futures markets has three obvious cyclical change periods with a higher connectedness level; (2) The net connectedness of zinc and copper with other metals has been at a high positive level for a long time, which indicates the two metal markets dominate the industrial metal market; (3) Zinc exhibits the strongest volatility spillovers, while tin exhibits the weakest volatility spillovers, no matter in spot markets or futures markets; (4) The connectedness of realized skewness and kurtosis have similarity with volatility connectedness but the spillover effects of skewness and kurtosis are not as obvious as the volatility spillover effects.     

COVID-19 related media sentiment and the yield curve of G-7 economies

We explore the connectedness of the components of the sovereign yield curve (slope, level and curvature) across G-7 countries and media sentiment about COVID-19. The recent pandemic is a unique opportunity to identifying the transmitters and receivers of risk. Our results indicate that media sentiment along with the US yield curve components are main transmitter of spillovers, whereas Japan is the leading recipient of spillover. Among the European countries, we notice France as a major transmit, whereas Germany and UK switch role as transmitter and receiver alternatively. The results are important for mapping the interconnectedness between countries. In addition, policy makers can use them when devising disaster plans to prepare for future market crises.     

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.     

The impact of COVID-19 on the G7 stock markets: A time-frequency analysis

We examine the co-movement of the G7 stock returns with the numbers of confirmed COVID-19 cases and causalities based on daily data from December 31, 2019 to November 13, 2020. We employ the wavelet coherence approach to measure the impact of the numbers of confirmed cases and deaths on the G7 stock markets. Our findings reveal that both the number of confirmed COVID-19 cases and the number of deaths exhibit strong coherence with the G7 equity markets, although we find heterogeneous results for the Canadian and Japanese equity markets, in which the numbers of COVID-19 cases and the deaths exhibit only a weak relationship. This evidence is more pronounced in the long-term horizon rather than the short-term horizon. Moreover, the lead-lag relationship entails a mix of lead-lag relations across different countries. We present the implications of these findings for both policymakers and the international investment community.     

Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID-19 pandemic

Using the five-minute interval price data of two cryptocurrencies and eight stock market indices, we examine the risk spillover and hedging effectiveness between these two assets. Our approach provides a comparative assessment encompassing the pre-COVID-19 and COVID-19 sample periods. We employ copula models to assess the dependence and risk spillover from Bitcoin and Ethereum to stock market returns during both the pre-COVID-19 and COVID-19 periods. Notably, the COVID-19 pandemic has increased the risk spillover from Bitcoin and Ethereum to stock market returns. The findings vis-à-vis portfolio weights and hedge effectiveness highlight hedging gains; however, optimal investments in Bitcoin and Ethereum have reduced during the COVID-19 pandemic, while the cost of hedging has increased during this period. The findings also confirm that cryptocurrencies cannot provide incremental gains by hedging stock market risk during the COVID-19 pandemic.     

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.     

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.     

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.