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.     

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.     

The asymmetric effect of crude oil prices on stock prices in major international financial markets

The aim of this paper is to explore the potential asymmetric impacts of positive and negative shocks in crude oil prices on stock prices in six major international financial markets which include China, Hong Kong, America, Japan, Britain, and Germany. We test for these asymmetric effects on 8 major international financial markets indices over the 2007M01–2020M03 periods. Our independent measures include the prices of Brent crude oil futures and West Texas Intermediate (WTI) futures. We use the nonlinear ARDL (NARDL) model proposed by Shin et al. (2014), which can capture both short- and long-run nonlinearities through positive and negative partial sum decompositions of the explanatory variables. This research finds that positive and negative fluctuations of oil price have asymmetric effects on stock price index in four financial markets, but the performance of the asymmetry is different. Specifically, the impacts of volatility in oil prices on two indices of Chinese stock prices are different, and the asymmetric effects of oil price volatility on stock price indices in China and other financial markets are significantly different.     

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.     

Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.     

Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China

We develop a skewness-dependent multivariate conditional autoregressive value at risk model (SDMV-CAViaR) to detect the extreme risk transmission channels between the Chinese stock index futures and spot markets. The proposed SDMV-CAViaR model improves the forecast performance of extreme risk by introducing the high-frequency realized skewness. Specifically, the realized skewness has a significant impact on the spillovers, but the realized volatility and realized kurtosis do not, which implies that the jump component plays an important role in extreme risk spillovers. The empirical results indicate there are bidirectional extreme risk spillovers between the stock index futures and spot markets, the decline of one market has direct and indirect channels to exacerbate the extreme risk of the other market. Firstly, the market decline will directly increase the extreme risk of related markets by decreasing market returns. Besides, the decline will indirectly increase the extreme risk by increasing the negative realized skewness and extreme risk spillovers.     

Global and regional integration of the Middle East and North African (MENA) stock markets

We investigate financial integration of MENA region to facilitate a more in-depth exploration of the structure of interdependence and transmission mechanism of stock returns and volatility between MENA and world stock markets. The EGARCH-M models with a generalized error distribution are employed to consider both leverage effect of negative shocks and leptokurtosis prevalent in the MENA stock markets. The estimation results of multivariate AR-GARCH models indicate that there are large and predominantly positive volatility spillovers and volatility persistence in conditional volatility between MENA and world stock markets. Own-volatility spillovers are generally higher than cross-volatility spillovers for all the markets.     

Better to give than to receive: Predictive directional measurement of volatility spillovers

Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we propose measures of both the total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across US stock, bond, foreign exchange and commodities markets, from January 1999 to January 2010. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis, which began in 2007. As the crisis intensified, so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of the Lehman Brothers in September 2008.     

Volatility spillovers among the U.S. and Asian stock markets: A comparison between the periods of Asian currency crisis and subprime credit crisis

This paper examines the changing nature of volatility spillovers among the U.S. and eight East Asian stock markets between two financial crises: the Asian currency crisis and the U.S. subprime credit crisis. Our empirical results suggest that volatility is not always spilled over from the directly affected markets to surrounding markets in crisis periods. The East Asian markets who directly suffered from the Asian currency crisis are the ones to which volatility is spilled over from other markets during the Asian currency crisis period, whereas uni-directional volatility spillovers from the U.S. market to other markets are observed during both crisis periods. This difference can be explained by a pre-determined hierarchy in which volatility spillovers tend to start from the U.S. market regardless of the geographical origin of the crisis. Furthermore, our results reveal that the markets in three major Asian financial hubs, i.e., Japan, Hong Kong and Singapore, are the markets to which volatility is spilled over uni-directionally from several other countries during the subprime credit crisis period, but not during the Asian currency crisis period. We attribute this difference to crisis-specific (currency or credit crisis), market-specific (credit derivatives market participation and foreign currency reserves), and time-specific (more integrated global market) factors.     

President’s Tweets,US-China economic conflict and stock market Volatility: Evidence from China and G5 countries

This study provides empirical evidence that the tweets from US President Donald J. Trump influence the trading decisions of investors worldwide. We examine the effects of Trump’s tweets related to China on stock market volatility in China and the G5 countries. Our results show that Trump’s original tweets related to the US-China economic conflict expand volatility in stock markets worldwide, and the US-China trade friction intensifies this effect. Furthermore, Trump’s tweets with different sentiments have different impacts on the returns of global stock markets. Our findings confirm that international investors may make their investment decisions based on information conveyed in these tweets.     

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.     

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.     

Modeling dynamic conditional correlations with leverage effects and volatility spillover effects: Evidence from the Chinese and US stock markets affected by the recent trade friction

In this article, we investigate the dynamic conditional correlations (DCCs) with leverage effects and volatility spillover effects that consider time difference and long memory of returns, between the Chinese and US stock markets, in the Sino-US trade friction and previous stable periods. The widespread belief that the developed markets dominate the emerging markets in stock market interactions is challenged by our findings that both the mean and volatility spillovers are bidirectional. We do find that most of the shocks to these DCCs between the two stock markets are symmetric, and all the symmetric shocks to these DCCs are highly persistent between Shanghai’s trading return and S&P 500′s trading or overnight return, however all the shocks to these DCCs are short-lived between S&P 500′s trading return and Shanghai’s trading or overnight return. We also find clear evidence that the DCC between Shanghai’s trading return and S&P 500′s overnight return has a downward trend with a structural break, perhaps due to the “America First” policy, after which it rebounds and fluctuates sharply in the middle and later periods of trade friction. These findings have important implications for investors to pursue profits.     

The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange,stock, and bond markets

We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in our information set, including implied volatility backed out from option prices. Realized volatility is separated into its continuous and jump components, and the heterogeneous autoregressive (HAR) model is applied with implied volatility as an additional forecasting variable. A vector HAR (VecHAR) model for the resulting simultaneous system is introduced, controlling for possible endogeneity issues. We find that implied volatility contains incremental information about future volatility in all three markets, relative to past continuous and jump components, and it is an unbiased forecast in the foreign exchange and stock markets. Out-of-sample forecasting experiments confirm that implied volatility is important in forecasting future realized volatility components in all three markets. Perhaps surprisingly, the jump component is, to some extent, predictable, and options appear calibrated to incorporate information about future jumps in all three markets.     

New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach

Because of the acceleration in marketization and globalization, stock markets in the BRICS (Brazil, Russia, India, China, and South Africa) countries are affected by various global factors, for example, oil prices, gold prices, global stock market volatility, global economic policy uncertainty, financial stress, and investor sentiment. This paper offers new insights into the short- and long-run linkages between global factors and BRICS stock markets by applying the quantile autoregressive distributed lags (QARDL) approach. This novel methodology enables us to test short- and long-run linkages accounting for distributional asymmetry. That is, the nonlinear dynamic relationship between the global factors and BRICS stock prices depends on market conditions. Our empirical results show that the effects of gold prices and global stock market volatility on BRICS stock prices are more significant in the long run than in the short run. A decrease in global stock market volatility is associated with higher stock prices, while gold prices demonstrate upward co-movement in dynamic correlations with stock markets. Irrational factors, such as economic policy uncertainty, financial stress, and investor sentiment, play a critical role in the short term, and negative interdependence is dominant. Finally, the rolling-window estimation technique is used to examine time-varying patterns between major global factors and BRICS 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.     

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

Region-wide connectedness of Asian equity and currency markets

This study systemically analyzes the dynamics of interdependence between the Asian equity and currency markets. The novelty of our study is that unlike other studies that explore either co-movements among equity markets or co-movements among currency markets, we pay particular attention to the interdependence between the two in terms of both return and volatility connectedness. We find that the contribution of crossspillovers between the Asian equities and currencies is substantial for the region-wide connectedness of both the returns and volatilities. We also find that the short-term spillovers are far more important for the return spillovers, while the long-term spillovers are far more important for the volatility spillovers, presumably reflecting the long-lasting effects of volatility shocks. All the results consistently underline the pivotal role of cross-interdependence between equity and currency markets, both as channels for integrating Asian financial markets and as sources of financial contagion across these markets. Our findings will provide useful guidance for portfolio risk management to adopt better hedging strategies for foreign exchange risks involved in the international investment of Asian equities.     

Simultaneous stochastic volatility transmission across American equity markets

Information flows across international financial markets typically occur within hours, making volatility spillovers appear contemporaneously in daily data. Such simultaneous transmission of variances is featured by the stochastic volatility model developed in this paper, in contrast to usually employed multivariate ARCH processes. The arising identification problem is solved by considering heteroscedasticity of the structural volatility innovations. Estimation takes place in an appropriately specified state space setup. In the empirical application, unidirectional volatility spillovers from the US stock market to three American countries are revealed. The impact is strongest for Canada, followed by Mexico and Brazil, which are subject to idiosyncratic crisis effects.     

International market links and volatility transmission

This paper gauges volatility transmission between stock markets by testing conditional independence of their volatility measures. In particular, we check whether the conditional density of the volatility changes if we further condition on the volatility of another market. We employ nonparametric methods to estimate the conditional densities and model-free realized measures of volatility, allowing for both microstructure noise and jumps. We establish the asymptotic normality of the test statistic as well as the first-order validity of the bootstrap analog. Finally, we uncover significant volatility spillovers between the stock markets in China, Japan, UK and US.