Research on the Influence of Securities Markets on Cross-Border Capital Flows Based on the DCC-MVGARCH Model

In the study of the impact of cross-border capital flows, most scholars at home and abroad focus on the method of linear time series mainly based on the vector autoregressive model (VAR), ignoring the volatility of variables in time series. In order to make up for the deficiency, the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model can be used to study the nonlinear time-varying correlation between variables. With the help of Eviews12 software and the DCC-MVGARCH model, this paper studies the impact of securities markets on cross-border capital flows in China from domestic and foreign perspectives in the context of two financial crises and COVID-19. The results indicate that financial crises affect the correlation between the securities markets and cross-border capital flows. China's stock market is positively correlated with short-term capital flows and negatively correlated with long-term capital flows. Its booming bond market promotes short-term capital flows but fails to affect the long-term capital flows, and China's short-term capital flows are increasingly linked to the volatility of foreign stock markets. Therefore, it is necessary to improve the mechanism for better monitoring and analyzing cross-border capital flows, promote further development of financial supervision, and guide market players to face the securities market rationally. © 2023 SPIE.

Sectoral volatility spillovers and their determinants in Vietnam

Using the vector autoregression (VAR) connectedness approach, this paper investigates dynamic volatility spillovers across 14 sectors in Vietnam's stock market over the period 2012–2021. The study also explores the differences in sectoral spillovers before and after the outbreak of Covid-19 pandemic. Additionally, the paper also investigates the effects of the current pandemic and macroeconomic fundamentals on intersectoral connectedness in Vietnam. Our findings show that volatility transmission across sectors fluctuates significantly over the research period and spikes during the Covid-19 pandemic. The total spillover index is approximately 64.23 per cent, indicating that volatility spillovers across the Vietnamese sectors are substantial. The risks from the stock market appear to spread quickly and easily across sectors in Vietnam. Among these 14 sectors, food, fisheries, and oil and gas act as net senders of risks while real estate and pharmacy are the greatest receivers of risk. The findings also confirm that the commerce, transportation, manufacturing, and service sectors are more sensitive to the Covid-19 pandemic crisis than other sectors in Vietnam. Furthermore, the empirical results show that an increase in daily Covid-19 infections increases volatility spillover across sectors. Policy implications have emerged based on these findings from this paper for the Vietnamese government and other emerging countries.

COVID-19 pandemic and capital markets: the role of government responses

This paper analyzes the moderation effect of government responses on the impact of the COVID-19 pandemic, proxied by the daily growth in COVID-19 cases and deaths, on the capital market, i.e., the S&P 500 firm's daily returns. Using the Oxford COVID-19 Government Response Tracker, we monitor 16 daily indicators for government actions across the fields of containment and closure, economic support, and health for 180 countries in the period from January 1, 2020 to March 15, 2021. We find that government responses mitigate the negative stock market impact and that investors' sentiment is sensitive to a firm's country-specific revenue exposure to COVID-19. Our findings indicate that the mitigation effect is stronger for firms that are highly exposed to COVID-19 on the sales side. In more detail, containment and closure policies and economic support mitigate negative stock market impacts, while health system policies support further declines. For firms with high revenue exposure to COVID-19, the mitigation effect is stronger for government economic support and health system initiatives. Containment and closure policies do not mitigate stock price declines due to growing COVID-19 case numbers. Our results hold even after estimating the spread of the pandemic with an epidemiological standard model, namely, the susceptible-infectious-recovered model.

Asymmetric Multifractal Analysis of the Chinese Energy Futures and Energy Stock Markets under the Impact of COVID-19

The volatility and cross-correlations of the energy market and the stock market during the COVID-19 pandemic have been paid close attention by scholars and investors. In this paper, we use the asymmetric multifractal analysis methods to study the fluctuation characteristics, market risks and cross-correlations of the Chinese energy futures market (EFM) and two energy stock markets before and after the COVID-19 outbreak, while the return series of Shanghai fuel oil futures, CSI Energy Index and CSI Mainland New Energy Theme Index are considered. The empirical evidences indicate that the auto- and cross-correlations of the three markets have the asymmetric multifractality, and that the multifractality of the cross-correlations is mainly caused by the fat-tailed distribution of the original series. After the COVID-19 outbreak, the risks of both the traditional energy stock market in the uptrend and the entire new energy stock market become larger, while those of the entire EFM become smaller. In addition, the COVID-19 pandemic has increased the multifractality of the cross-correlations between the energy futures and energy stock markets when the EFM is in downward trend.