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This study aims to examine the multiscale predictability power of COVID-19 deaths and confirmed cases on the S&P 500 index (USA), CAC30 index (France), BSE index (India), two strategic commodity futures (West Texas intermediate [WTI] crude oil and Gold) and five main uncertainty indices Equity Market Volatility Ticker (EMV), CBOE Volatility Index (VIX), US Economic Policy Uncertainty (EPU), CBOE Crude Oil Volatility Index (OVX) and CBOE ETF Gold Volatility Index (GVZ). Furthermore, the authors analyze the impact of uncertainty indices and COVID-19 deaths and confirmed cases on the price returns of stocks (S&P500, CAC300 and BSE), crude oil and gold.

The authors used the wavelet coherency method and quantile regression approach to achieve the objectives.

The results show strong multiscale comovements between the variables under investigation. Lead-lag relationships vary across frequencies. Finally, COVID-19 news is a powerful predictor of the uncertainty indices at intermediate (4–16 days) and low (32–64 days) frequencies for EPU and at low frequency for EMV, VIX, OVX and GVZ indices from January to April 2020. The S&P500, CAC30 and BSE indexes and gold prices comove with COVID-19 news at low frequencies during the sample period. By contrast, COVID-19 news and WTI oil moderately correlated at low frequencies. Finally, the returns on equity and commodity assets are influenced by uncertainty indices and are sensitive to market conditions.

This study contributes to the literature by exploring the time and frequency dependence between COVID-19 news (confirmed and death cases) on the returns of financial and commodity markets and uncertainty indexes. The findings can assist market participants and policymakers in considering the predictability of future prices and uncertainty over time and across frequencies when setting up regulations that aim to enhance market efficiency.

The emergence of a coronavirus disease 2019 (COVID-19) epidemic has had a tremendous impact on the world, and the characteristics of its evolution need to be better understood.

To explore the changes of cases and control them effectively, this paper analyzes and models the fluctuation and dynamic characteristics of the daily growth rate based on the data of newly confirmed cases around the world. Based on the data, the authors identify the inflection points and analyze the causes of the new daily confirmed cases and deaths worldwide.

The study found that the growth sequence of the number of new confirmed COVID-19 cases per day has a significant cluster of fluctuations. The impact of previous fluctuations in the future is gradually attenuated and shows a relatively gentle long-term downward trend. There are four inflection points in the global time series of new confirmed cases and the number of deaths per day. And these inflection points show the state of an accelerated rise, a slowdown in the rate of decline, a slowdown in the rate of growth and an accelerated decline in turn.

This paper has a certain guiding and innovative significance for the dynamic research of COVID-19 cases in the world.

This paper aims to examine the stock market reactions of firms and industries in Malaysia to the government’s COVID-19 movement control order (MCO) announcement. As China is Malaysia’s leading trading partner, the authors also observe if the Chinese Government’s confirmation of human-to-human coronavirus transmission affects firms’ stock market reactions. In addition, this study examines whether the Malaysian Government’s ease of restrictions on economic activities affects firms’ stock market reactions. Finally, this study analyses the effect of COVID-19 number of confirmed cases on firms’ abnormal returns.

This study uses an event study methodology to determine the abnormal returns between day −30 to day 30 of the announcements. In addition, this study uses the regression estimation to determine whether the COVID-19 number of confirmed cases explain the abnormal returns.

This study finds that investors react negatively to the announcement of the MCO and confirmation of the human-to-human transmission of coronavirus over the event windows. However, the cumulative average abnormal returns (CAARs) started to recover when stimulus packages were introduced, and the lockdown measures were eased, allowing businesses to reopen. This study also finds that only firms in the health-care sector reported significant positive CAARs. Stock returns of the utilities and telecommunication firms showed no changes, while eight other sectors fell remarkably. The results also show that the COVID-19 number of confirmed cases adversely affects firms’ abnormal returns.

This study suggests that stock prices incorporate bad and good news surrounding the announcements of major international and local events related to the COVID-19 pandemic. Thus, investors should consider such factors in making investment decisions.

To the best of the authors’ knowledge, this paper is one of the early research works investigating the stock market reactions to the COVID-19 major announcements (MCO, human-to-human transmission and ease of restrictions on economic activities) using an event study methodology in an emerging market, namely, Malaysia. This study is timely in light of the recently increasing calls for researchers to analyse the potential economic impacts of COVID-19 on global capital markets, especially in emerging markets whose evidence is scarce.

This paper aims to investigate the impact of COVID-19 and the stringency of the government policy response on stock market returns globally and at the regional level.

Pooled-ordinary least squares (OLS) and panel data techniques are used to analyse the daily data set across 88 countries in the Americas, Europe, Asia-Pacific, Middle East and Africa for the period of 1 January 2020 to 10 May 2021.

Using pooled-OLS and panel data techniques, the analyses show that both the daily growth in confirmed cases and deaths caused by COVID-19 have significant negative effects on stock returns across all markets. The effects are non-linear and U-shaped. Stock markets react more to the growth of confirmed cases than to the growth in the number of confirmed deaths. The results, however, vary across regions. More specifically, this study finds that the negative effect of confirmed cases is stronger in the Americas and the Middle East, followed by Europe. The negative direct effect of deaths caused by COVID-19 is stronger in the European region, followed by the Middle East, in relation to the rest of the world. The stock market returns in the African region are not, however, statistically significant. The researcher finds evidence that stringent policy responses lead to a significant increase in the stock market returns, both globally and across regions.

The results suggest that the integrity of the government and its interventions complemented by a stable and reliable monetary policy are crucial in providing confidence to firms and households in uncertain times.

COVID-19 has a significant impact on national economies and stock markets, triggering various governments’ interventions across all geographic regions. The pandemic has significantly affected all aspects of life, especially the stock markets. However, their empirical impact on stock returns is still unclear. This paper is the first of its kind to fill this gap by providing an in-depth quantitative analysis of the impact of both COVID-19 and stringency of the governmental policy responses on stock market returns globally and at the regional level. It is also the first to use an advanced analytical framework in analysing the effects of daily growth in both total and newly confirmed cases, and the daily growth in both total and new deaths caused by COVID-19 on them. The dynamic nature of the data on COVID-19 is taken into account. The non-linearity of the effects is also considered.

The purpose of this paper is to investigate the impact of the global coronavirus (COVID-19) pandemic on stock market liquidity, while taking into account the depth and tightness dimensions.

The author used a panel data regression on stock market dataset, representing 314 listed firms operating in six Middle East and North African (MENA) countries from February to May 2020.

The regression results on the overall sample indicate that the liquidity related to the depth measure was positively correlated with the growth in the confirmed number of cases and deaths and stringency index. Moreover, the market depth was positively related to the confirmed cases of COVID-19. The results also indicate that the liquidity of small cap and big cap firms was significantly impacted by the confirmed number of cases, while the stringency index is only significant for the liquidity depth measure. Moreover, the results regarding sectors and country level analysis confirmed that COVID-19 had a significant and negative impact of stock market liquidity.

This paper confirms that the global coronavirus pandemic has decreased the stock market liquidity in terms of both the depth and the tightness dimensions.

While most empirical papers focused on the impact of the COVID-19 global pandemic on stock market returns, this paper investigated liquidity chock at firm level in the MENA region using both tightness and depth dimensions.

This study aims to investigate how the gold return and its volatility respond to the COVID-19 pandemic.

Stochastic volatility (SV) models are conducted to examine the response of gold to the number of new confirmed cases and deaths.

The results indicate that an increase in the change rate of the number of COVID-19 infections or fatalities leads to heightened volatility in gold prices. Moreover, the results suggest that gold volatility is more sensitive to the impacts from high-income countries than by those from middle- and low-income countries. In addition, the asymmetric effect is detected in the gold price volatility, which is contrary to the typical asymmetric effect seen in the stock market. Furthermore, the results remain robust after accounting for the US dollar and the volatility index in relation to gold returns.

This study presents whether and to what extent gold is incorporated in the information related to the number of COVID-19 cases and deaths.

This study augments the existing literature by exploring how the number of COVID-19 infections and fatalities influences gold prices. In addition, it examines the day-of-the-week and asymmetric effects that may contribute to the volatility of gold prices. To the best of the authors’ knowledge, the evolution of gold has not yet been investigated using SV models.

This study analyses the prevalent coronavirus disease (COVID-19) epidemic using machine learning algorithms. The data set used is an API data provided by the John Hopkins University resource centre and used the Web crawler to gather all the data features such as confirmed, recovered and death cases. Because of the unavailability of any COVID-19 drug at the moment, the unvarnished truth is that this outbreak is not expected to end in the near future, so the number of cases of this study would be very date specific. The analysis demonstrated in this paper focuses on the monthly analysis of confirmed, recovered and death cases, which assists to identify the trend and seasonality in the data. The purpose of this study is to explore the essential concepts of time series algorithms and use those concepts to perform time series analysis on the infected cases worldwide and forecast the spread of the virus in the next two weeks and thus aid in health-care services. Lower obtained mean absolute percentage error results of the forecasting time interval validate the model’s credibility.

In this study, the time series analysis of this outbreak forecast was done using the auto-regressive integrated moving average (ARIMA) model and also seasonal auto-regressive integrated moving averages with exogenous regressor (SARIMAX) and optimized to achieve better results.

The inferences of time series forecasting models ARIMA and SARIMAX were efficient to produce exact approximate results. The forecasting results indicate that an increasing trend is observed and there is a high rise in COVID-19 cases in many regions and countries that might face one of its worst days unless and until measures are taken to curb the spread of this disease quickly. The pattern of the rise of the spread of the virus in such countries is exactly mimicking some of the countries of early COVID-19 adoption such as Italy and the USA. Further, the obtained numbers of the models are date specific so the most recent execution of the model would return more recent results. The future scope of the study involves analysis with other models such as long short-term memory and then comparison with time series models.

A time series is a time-stamped data set in which each data point corresponds to a set of observations made at a particular time instance. This work is novel and addresses the COVID-19 with the help of time series analysis. The inferences of time series forecasting models ARIMA and SARIMAX were efficient to produce exact approximate results.

Scholars have not agreed with each other on how people would behave after experiencing a catastrophic event. They could save more as a precautionary action for future difficulties or save less with a carpe diem attitude. This study aims to attempt to shed light on this debate with empirical observations on how the Covid-19 pandemic has affected household saving decisions.

The two waves of the survey data allowed us to investigate both instantaneous and ongoing effects of Covid-19 on household saving decisions. The instantaneous effect refers to the immediate impact of the crisis, while the ongoing effect refers to the lasting impact of the pandemic when economic recovery had started. The variation in the number of confirmed cases across cities during the two waves provides the source of power for identification. The authors extend their analyses of the impact of Covid-19 on the household saving decision by using ordinary least squares models. Due to the ordered nature of survey responses, the authors also rerun all baseline models using the ordered probit regression method.

This paper studied the impact of the Covid-19 pandemic on household saving decisions in China. This study found that households in the most affected cities would save more during the Covid-19 but tend to save less when the disaster started fading away. Combining findings in Kun et al. (2013) and Filipski et al. (2015), people do become more pessimistic during and after the Covid-19, possibly driving their observed precautionary and cape diem behaviors during the two points of time. Heterogeneity analysis shows that specific households would dramatically change their saving behavior. These observations might be useful for policymakers who concern the economic recovery after this pandemic disaster.

Understanding how the Covid-19 pandemic would affect household consumption vs saving decisions is important for the economic recovery after this disaster comes to an end. The analyses presented in this research could be useful for policymakers who concern appropriate policies aiming to boost consumption and economic activities after Covid.

This study aims to understand how the COVID-19 pandemic dramatically impacts the maturity of all industrial sectors globally. This paper analyses the general patterns of managing maturity in terms of performance and risk-taking of S&P 500 industrial sectors while determining their association with COVID-19.

To analyse the immediate response of COVID-19 on maturity management, the authors gather time-series daily index data of S&P sectors from October 2019 until June 2020 from Bloomberg. The authors select this study period to show the immediate effect of COVID-19 on industrial sector maturity management. The performance and volatility of stock are proxies for managing the maturity of each sector. The authors use vector auto-regression (VAR) methodology to determine the impact of global coronavirus.

This study’s findings suggest that the information technology sectors outperform the other sectors; in contrast, the utility sector exhibits the worst performance during a pandemic. Furthermore, the real estate sector depicts a higher level of systematic risk pattern than other sectors. Interestingly, the empirical result of VAR shows that almost every sector is significantly negatively affected by this pandemic; however, the consumer discretionary sector is immune to it.

Overall, this study’s findings for individual economic sectors demonstrate that the managing maturity of each sector acts differently to the coronavirus outbreak. This study offers insights to researchers, policymakers, regulators, financial report users, investors, employees, clients and society.

This paper contributes to the existing literature on managing the maturity of industry sectors in terms of observing their trends during the financial crisis.

This study examines the influence of various COVID-19 catastrophes variables on the stock market liquidity, considering the market depth and market tightness in the technology industry of the four biggest ASEAN capital markets.

The study utilised the panel data regression analysis obtained from 177 listed technology companies across the four ASEAN countries from March 2, 2020 to June 30, 2021 using the random effect and weighted least squares. The study also supported the result with robustness test, implementing the quantile regression to further present companies' segmentation within the variables.

The regression results indicate that daily growth COVID-19 confirmed cases and stringency that adversely impacted the stock market liquidity. Confirmed deaths were also found to have a detrimental effect on the stock market liquidity. On the other hand, recoveries and vaccination of COVID-19 enhance the stock market liquidity to escalate.

The study affirms that stock market liquidity is bound to be driven by the COVID-19 variables, but only to be limited to the technology industry observed in four major ASEAN capital markets. Awareness by investors and government could be shifted towards the rise of confirmed cases, recoveries, vaccination and stringency as it improves the liquidity of capital market in aggregate. However, rise of confirmed deaths negatively affect the liquidity. All in all, government and stock market regulator should promote transparency to boost investors' confidence in trading.

This study initiates the investigation in the four biggest ASEAN capital markets, particularly in the technology industry, regarding the COVID-19 catastrophes and stock market liquidity in terms of both market depth and market tightness. Further, this study enriches the impact of COVID-19 by taking the recovery cases and vaccination of COVID-19 as additional consideration.