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.     

The outbreak of COVID-19 and stock market liquidity: Evidence from emerging and developed equity markets

The outbreak of the novel corona virus has heightened concerns surrounding the adverse financial effects of the outbreak on stock market liquidity and economic policies. This paper contributes to the emerging strand of studies examining the adverse effects of the virus on varied aspect of global markets. The paper examines the causality and co-movements between COVID-19 and the aggregate stock market liquidity of China, Australia and the G7 countries (Canada, France, Italy, Japan, Germany, the UK and the US), using daily three liquidity proxies (Amihud, Spread and Traded Value) over the period December 2019 to July 2020. Our empirical analysis encompasses wavelet coherence and phase-differences as well as a linear Granger causality test. Linear causality test results suggest that a causal relationship exists between the number of cases of COVID 19 infections and stock market liquidity. To quantitatively examine the degree of causality between COVID-19 outbreak and stock market liquidity, we employ the continuous wavelet coherence approach with results revealing the unprecedented impact of COVID-19 on stock market liquidity during the low frequency bands for countries that were hard hit with the COVID-19 outbreak, i.e., Italy, Germany, France, the UK and the US. Further, evidence shows that there is a heterogeneous lead-lag nexus across scales for the entire period of the study.     

Impact of COVID-19 pandemic on stock markets: Conventional vs. Islamic indices using wavelet-based multi-timescales analysis

We empirically explore the effect of the COVID-19 pandemic on Islamic and conventional stock markets from a global perspective. We also explore the co-movement between Islamic and conventional stock markets. Two comparable pairs of conventional and Islamic stock indices – Dow Jones Index and FTSE Index are considered in this study. Employing Wavelet-based multi-timescales techniques on the daily data from 21st January to 27th November 2020, our findings indicate that the pandemic creates identical volatility in both stock markets. Our findings further suggest that both markets are strongly associated and tend to co-move highly during our sample period, rebutting the decoupling hypothesis of the Islamic stock market from the conventional market. However, the Shariah screening process fails to provide immunity to Islamic stock markets against financial crises. Our findings suggest that investors should be aware that Islamic stocks' conservative features do not present a superior investment alternative, especially in economic turmoil.     

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.     

How have the dependence structures between stock markets and economic factors changed during the COVID-19 pandemic?

This study investigates how the dependence structures between stock markets and economic factors have changed during the COVID-19 pandemic using the dynamic model averaging approach. A series of economic factors such as commodity markets, cryptocurrency, monetary policy, international capital flows, and market uncertainty indices are considered. We find that the importance of economic variables and the sign and size of their coefficients are significantly different from those before the COVID-19 pandemic. The stock markets are most influenced by economic factors during the COVID-19 outbreak.     

The COVID-19 pandemic uncertainty,investor sentiment,and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.     

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.     

Dynamic connectedness of China’s green bonds and asset classes

This paper quantifies the co-movement and time-varying integration between China's green bonds and other asset classes across different time domains using the wavelet coherence and time-frequency connectedness model based on the time-varying parameter VAR (TVP-VAR). First, we predominantly detect a strong positive co-movement of green and conventional bonds, especially in the medium and long term. Second, strong bidirectional spillovers exist between green bonds and treasury, corporate, and financial bonds regardless of the time horizon. Lastly, cross-market spillovers between the green bonds and the stock, energy, low-carbon stock market were quite limited in the short-run but strengthened towards the long-term except during the 2015 China stock market crash and the COVID-19 recession when short-term integration rose sharply. The results document some practical enlightenment for investors and policymakers with various time horizons.     

Time-frequency connectedness of crude oil,economic policy uncertainty and Chinese commodity markets: Evidence from rolling window analysis

This article investigates the time–frequency connectedness of economic policy uncertainty (EPU), WTI crude oil and Chinese commodity markets during the period between 2004 and 2020. Rolling window wavelet vector autoregression and connectedness networks are developed to evaluate the time-varying characteristics of the connectedness. The empirical results are as follows: First, the total connectedness between EPU, oil and commodities becomes stronger as the time scale increases. Second, the net connectedness of EPU and WTI in the system is positive, indicating that EPU and WTI are contributors to information and will affect financial markets across time scales. Third, the connectedness remains at a high level during financial crises across all scales, and the contribution of EPU and crude oil to commodities increases significantly. Specifically, compared with other commodity sectors, grains are greatly affected by EPU under the condition that the energy sector is seriously affected by crude oil. Overall, investors and policy makers should consider connectedness in terms of time and frequency when making a decision.     

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.     

Impact of the COVID-19 outbreak and its related announcements on the Chinese conventional and Islamic stocks’ connectedness

In this paper, we assess the impacts of the COVID-19 counts (infected cases, deaths and recovered) and related announcements on the Islamic and conventional stocks interplays in the Chinese market. We test whether Islamic stocks are perceived as assets providing diversification benefits in time of COVID-19 pandemic. Doing so, we implement a multivariate GJR-GARCH model under dynamic conditional correlation (DCC) as well as multiple and partial wavelet coherence methods to recent Chinese daily data ranging from 2 December 2019 to 8 May 2020 and COVID-19 related announcement for the period. Our results from multivariate GJR-GARCH models reveal that COVID-19 infected cases and deaths do impact mean DCCs between Islamic and conventional stocks, number of recovered do not have such impact, while none of the above have any significant impact on the DCCs fluctuations. However, when we analyze the impact of COVID-19 related announcement on the variation of conditional correlation between two stocks (i.e. DCC volatility) our findings show that 7 out of 10 such announcements (mainly those with serious health treats or economic implications) do effect those volatilities in Chinese equity market. The empirical findings from partial and multiple wavelet coherences provide robust evidence of instability in the co-movement between Islamic and conventional indexes for different scales and over dissimilar sub-periods. Indeed, the weakening of co-movements is especially notable in the very short and short-run where operating the short-term investors. Our empirical findings offer several key propositions for policy makers and portfolio managers in China with broad implications applicable to other markets.     

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.     

Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic

Since the level of markets’ information efficiency is key to profiteering by strategic players, Shocks; such as the COVID-19 pandemic, can play a role in the nature of markets’ information efficiency. The martingale difference and conditional heteroscedasticity tests are used to evaluate the Adaptive form of market efficiency for four (4) major stock market indexes in the top four affected economies during the COVID-19 pandemic (USA, Brazil, India, and Russia). Generally, based on the martingale difference spectral test, there is no evidence of a substantial change in the levels of market efficiency for the US and Brazilian stock markets in the short, medium, and long term. However, in the long term, the Indian stock markets became more information inefficient after the coronavirus outbreak while the Russian stock markets become more information efficient. Intuitively, these affect the forecastability and predictability of these markets’ prices and/or returns. Thereby, informing the strategic and trading actions of stock investors (including arbitrageurs) towards profit optimization, portfolio asset selection, portfolio asset adjustment, etc. Similar policy implications are further discussed.     

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 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 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.     

Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach

This study examines the asymmetric multifractality and the market efficiency of the stock markets in the countries that are the top crude oil producers (USA, KSA, Canada and Russia) and consumers (Brazil, China, India, and Japan) using an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method. The results show evidence of an asymmetric multifractal nature for all markets. Moreover, the multifractality is stronger in the upward movement of the market returns, except in China. The degree of efficiency of the stock markets is shown to be time-varying and experienced a decrease during the 2008 global financial crisis (GFC), but an upside trend occurred during the recent oil price crash followed a significant decline during COVID-19. The stock markets have an anti-persistent feature during GFC and COVID-19, whereas they exhibit a long-term persistent feature during oil price crash. More interestingly, the efficiency of the stock markets of crude oil producers is lower in general than that of oil consumers. Furthermore, the efficiency of the stock market is lower in the downward movement of the market returns than in the upward movement. Asymmetry and oil price uncertainty index are the key driver of the stock markets and can serve as predictor of the stock market dynamics of top oil producers and top oil consumers particularly during COVID-19 and oil price crash.     

A description of the COVID-19 outbreak role in financial risk forecasting

This study aims to describe the risk of the system composed on the market indexes of the countries that were more affected by COVID-19. Our sample encompasses the thirty-five countries with more cases and/or deaths caused by COVID-19 until November 2020. As a second contribution, we describe the risk of each market index individually. As a general pattern, we note that losses and individual and systemic risks peaked in March 2020. We verify that countries that were epicenters of the COVID-19 pandemic experienced critical levels of risk, which is partially explained by more stringent confinement measures since these are the ones whose labor markets will suffer more in the medium and long run. We perceived a market recovery, arguably due to the low-interest rates and expansive actions taken by central banks. Nonetheless, we also observed that the systemic risk returned to pre-pandemic levels at the end of 2020.     

Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.     

Animal Behavior in Capital markets: Herding formation dynamics,trading volume,and the role of COVID-19 pandemic

This paper provides new evidence on herding behavior. Using daily frequency data for 336 US listed firms over a five-year period, we investigate three important elements of financial herding behavior. First, trading volume, representing market interest, as a significant variable in capital markets apart from stock prices. Second, herding dynamics since herding formation is a dynamic process. Third, the reaction of possible financial herding to exogenous events-threats, as we use the pandemic event in order to investigate a market under stress. Even though the benchmark herding model used does not provide evidence of herding behavior, our results verify the significance of the above herding elements. We also find that trading volume and positive changes in trading volume result in increased cross-sectional absolute deviation (CSAD). Most importantly, we find that herding behavior is evident during the COVID-19 pandemic confirming that investors tend to herd during major crisis periods.