Narrow Results

We are the first to explore the effect of economic policy uncertainty (EPU) and the COVID-19 pandemic on the correlation between the cryptocurrency index CRIX and the world stock market portfolio, as well as the hedging properties of CRIX. To this end, we mainly apply the dynamic conditional correlation model with mixed data sampling regressions, a threshold vector autoregressive model and the generalized impulse response function. We demonstrate that the correlation is influenced by the uncertainty stance of the economy and behaves differently in low-, medium- and high-uncertainty periods. Most of the abnormal market relations exist in high levels of EPU or during the COVID-19 period, and the impact of global EPU is greater than that of EPU originating in the United States, Europe, Russia and China. Moreover, the CRIX can serve as a hedge asset against the world stock market. The high (low) level of EPU has a significantly positive (negative) effect on the optimal hedge ratio of CRIX, which increases significantly during the COVID-19 period. Our findings have implications for risk management, portfolio allocations and hedging strategies.

This longitudinal study investigated the managerial effectiveness of the “Magnificent Seven” stock firms in enhancing Economic Value Added (EVA) before and after the COVID-19 pandemic. Utilizing a within-subject ANOVA analysis on data spanning from 2016 to 2023, this research aimed to explore how managerial decisions within these firms influenced economic profits over time. The study utilized the Stern-Stewart formula for EVA to calculate the average EVA data from 10-K filings of these technology-oriented firms, representing a significant portion of the S&P 500 index, to determine the impact of the COVID-19 pandemic on their financial performance. Results indicated a large significant effect difference in EVA pre- and post-COVID-19 pandemic, with an increase in both the mean value and variability of EVA. The partial eta squared value indicated that the time period accounted for approximately 26.4% of the variance in EVA. This suggested that the COVID-19 pandemic had a significant impact on these firms’ EVA, reflecting positively on their managerial decision-making effectiveness in creating economic value. The conclusion highlighted the importance of management efficiency key decisions included leveraging artificial intelligence, maintaining operational agility to mitigate supply chain disruptions, and fostering a culture of innovation in navigating the complexities introduced by the pandemic. It underscored the significant role of external factors such as consumer behavior changes and government policies in influencing company performance. The study recommended exploring how different sectors responded to the pandemic’s challenges, particularly those hit hardest by the crisis. This study contributed to the understanding of EVA as a reliable measure of a company’s financial health and managerial effectiveness in uncertain times.

The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand’s rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand’s residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term.

This paper studies the COVID-19 pandemic’s impact on Indonesia’s labor market, using the exogenous timing of the pandemic in a seasonal difference-in-differences framework. We use multiple rounds of Indonesia’s National Labor Force Survey to establish a pre-pandemic employment trend and attribute any difference from this trend to the estimated effect of the pandemic on employment outcomes. We find mixed impacts of the pandemic on Indonesia’s labor market. While the pandemic has reduced the gender gap in employment participation due to the “added worker effect” among women, it has also lowered overall employment quality among both women and men. The increase in female employment was mainly driven by women in rural areas without a high school education entering either informal agricultural employment or unpaid family work. For men, the pandemic had negative employment impacts for all subgroups. Among the employed, both women and men work fewer hours and earn lower wages.

Globally, the coronavirus disease (COVID-19) pandemic has sparked unexpected and violent outbursts against doctors, nurses, and other health personnel. In the Indian context, studies on violence against doctors and other medical staff largely focus on supply-demand imbalances in health care, overcrowding, drug shortages, negligence of critical care patients, lack of diagnostic and other essential devices (e.g., X-ray and ultrasound equipment and oxygen cylinders), deaths of patients, and bribery and corruption (collusion between doctors and pharmaceutical companies). While these factors explain such violence against medical personnel partly, we argue that it is largely rooted in a lack of trust in doctors and hospitals, which eroded rapidly during the COVID-19 pandemic. We analyze the covariates of trust in public and private health-care providers based on an all-India panel survey and delineate policies to rebuild trust, especially in public health care.

The purpose of this study is to understand the speed and quality of China’s economic growth during the COVID-19 pandemic. This study defines high-quality economic growth as a growth rate with a low variance and adopts quarterly time-series data for China’s real GDP growth rate to analyze its regime switches and structural changes by estimating a conditional Markov chain model. The study further utilizes the estimated parameters to predict the smoothed probabilities of each of four joint states for China’s real GDP growth rate from 2021 Q4 to 2025 Q3. The primary finding of this study is that, during the period from 2020 Q1 to 2021 Q3, the outbreak of COVID-19 shocked China’s economy and its economic growth rate fluctuated dramatically, which resulted in its variance being relatively high. However, China’s economic growth rate is very likely to gradually stabilize (as evidenced by a low variance) and to grow at a medium-to-high speed with high quality during the forecast period.

One of the most severe and troubling diseases these days is COVID-$19$ pandemic. The COVID-$19$ pandemic’s dangerous effects are extremely rapid, and infection normally results in death within a few weeks. As a consequence, it is important to delve deeper into the complexities of this elusive virus. In this study, we propose a Caputo-based model for increasing COVID-19 strains. The memory effect and hereditary properties of the fractional variant for the model enable us to fully comprehend the dynamics of the model’s features. The existence of unique solution using the fixed-point theorem and Arzelá–Ascoli principle as well as the stability analysis of the model by means of Ulam–Hyer stability (UHS) and generalized Ulam–Hyer stability (GUHS) have been discussed. Furthermore, the parameters of the model are estimated using $3$ months data points chosen from Nigeria using the nonlinear least-squares technique. The best-suited parameters and the optimized Caputo fractional-order parameter $\alpha$ are obtained by running simulations for both models. The proposed model is shown to comprehend the dynamical behavior of the virus better than the integer-order version. In addition, to shed more light on the model’s characteristics, various numerical simulations are performed using an efficient numerical scheme.

The deficiency in data collection and reporting has led to the emergence of uncertainty in the data in the pandemic process. These matters cause that traditional statistical and mathematical models have been functionless and unreliable in this issue. In this respect, this study presents an ensemble prediction model with innovative and contemporary properties to model COVID-19 cases in the UK and USA inclusively throughout the whole pandemic process. The proposed ensemble prediction model is composed of an assembly of Type-1 fuzzy regression functions with elastic net regularization (E-T1FRF) and radial basis function neural networks (RBFNNs). Thus, the proposed ensemble model can successfully model the uncertainty in the data with the fuzzy modelling perspective of T1FRF and also thoroughly adapt to the patterns in the data thanks to the flexible modelling ability of RBFNN based on data. With the proposed ensemble prediction model, the cases in the entire pandemic process from the beginning of March 2020 to the end of June 2022 were modelled and predicted for 23 different periods in one-month prediction steps. The proposed model produced predictions with MAPE values below 3% in all but periods except for three periods. Also, the average MAPE values for all periods were obtained at around 2% for the UK and only 1.5% for the US. These results, at a reasonable level of error, demonstrated the practicality of the usage of the proposed community model for other countries and provided valuable information for future action.

This paper examines the relationship between ex-ante stock liquidity and abnormal returns during various phases of COVID-19 led market uncertainties in India. We find that the volume-based liquidity supports stock more significantly during the crisis than in periods of calm. However, contrary to existing empirical evidence, price-based liquidity penalizes stocks during a crisis. Moreover, during periods of calm and recovery, the inverse relationship of liquidity-abnormal return reverses. Further analysis shows that this change of price-based liquidity to abnormal return relationship is more prominent in firms with higher ex-ante liquidity. In contrast, highly illiquid firms appear immune.

We analyze the volatility spillover effect from the Chinese stock market to different stock markets in the G20 countries. We employ dynamic conditional correlation and vector autoregression (VAR) to analyze adjusted daily closing stock indices extending from 1st October 2019 to 30th June 2020. The result reveals that there is short-run volatility in sample stock return except Australia and South Korea. Similarly, there is long-term volatility persistence in sample countries’ stock exchange except Australia, Saudi Arabia, Russia, and France. However, Australia is only the country where there is no short- and long-run information transmission derived from China. Therefore, there is a portfolio diversification opportunity in this country during COVID-19. Overall, this paper shows significant interdependencies between the Chinese and the G20 markets which furnish momentous implications to the stakeholders of markets.

This paper investigates the asymmetric cointegration between possible domestic determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of West Texas Intermediate (WTI) and newly-launched Shanghai crude oil futures (SC) via the Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results show the long- and short-run impacts of stock markets, interest rate, coronavirus panic and corn futures on WTI futures prices, while economic policy uncertainty is a driver for the long-run WTI price dynamics. However, the influence of stock market, interest rate and COVID-19 panic on SC is significant in the short term. There also exists short- and long-run positive responses of China’s crude oil futures to corn prices. Overall, the impacts of domestic price drivers are heterogeneous across market circumstances (bullish, bearish and normal) and countries. These empirical evidences provide practical implications for investors and policymakers.

In late 2019, the coronavirus began to spread around the world and impact international politics and economies significantly. In the face of the pandemic, stock markets around the world fluctuated sharply. The study aims to investigate the impact of the pandemic on the predictive variables of a stock prediction model, formed using chip-based variables and sentiment variables derived from comments posted on a social media platform. This study first performs feature engineering analysis to identify the indicators suitable for constructing the prediction model. The analysis then establishes a set of phrase rules to assign sentiment scores to the opinions expressed in replies and evaluates the effect on the accuracy of predictions. The results show that the major chip-based indicators affecting changes in the stock market differ before and after the pandemic. Hence, prediction models should be established separately for analysis in either period. In addition, the results indicate that the model relying on reply-based sentiment scores as a predictive variable provides more accurate predictions of stock price change.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has brought unexpected economic downturns and accelerated digital transformation, leading to stronger financial fraud motives and more complicated fraud schemes. Although scholars, practitioners, and regulators have begun to focus on the new characteristics of financial fraud, a systematic and effective anti-fraud strategy during the pandemic still needs to be explored. This paper comprehensively analyzes the lessons of anti-fraud that we should learn from the COVID-19 pandemic. By exploring the complex motives and schemes of fraud, we summarize the characteristics of financial fraud activities and further analyze the regulatory challenges posed by financial fraud during the outbreak. To better cope with the fraudulent activities during the pandemic, policy proposals on how to improve the supervision of financial fraud activities are put forward. In particular, the panoramic data and graph-based techniques are powerful tools for future fraud detection.

Currently, the whole world is still suffering from the COVID-19 (variants) pandemic that has been lasting for over three years and resulted in countless losses. Since its outbreak at the end of 2019, China has first suffered, but quickly gained the upper hand through strict epidemic control and emergency management. Despite several recurrences of regional epidemics in 2022, both economic growth and people’s livelihoods have been effectively safeguarded in China, with a regular prevention system established and social life recovery accomplished orderly. How does China deal with this pandemic? What practical measures taken and decisions made could be adopted and shared with other countries? In this paper, these issues are summarized and discussed as progressive lessons for the rest of the world. Though there’s a major shift of pandemic policy in late 2022 and early 2023 in China, this study still provides insightful and beneficial implications to support decision- and policy-makings in future fights against any possible COVID-19 variants until the end of the pandemic.

What drives China’s approach to disputes in the South China Sea? While conventional wisdom often attributes Beijing’s actions primarily to material interests, this overlooks the possible influence of non-material factors such as China’s desire to uphold its national reputation. Contrary to the assumptions of traditional prospect theory, China’s behavior is not driven solely by calculations of gains and losses but also by considerations for its international reputation. Beijing may temper its assertiveness when its reputation is declining but still manageable, as demonstrated by its increased willingness to resolve disputes multilaterally after its global image was tarnished following the outbreak of the COVID-19 pandemic. Contrarily, China may resort to more assertive actions when these do not significantly hurt its reputation or when the damage seems irreversible, as seen in its unwavering stance during the 2012 Scarborough Shoal standoff with the Philippines.

COVID-19 disease, a deadly pandemic ravaging virtually throughout the world today, is undoubtedly a great calamity to human existence. There exists no complete curative medicine or successful vaccines that could be used for the complete control of this deadly pandemic at the moment. Consequently, the study of the trends of this pandemic is critical and of great importance for disease control and risk management. Computation of the basic reproduction number by means of mathematical modeling can be helpful in estimating the potential and severity of an outbreak and providing insightful information which is useful to identify disease intensity and necessary interventions. Considering the enormity of the challenge and the burdens which the spread of this COVID-19 disease placed on healthcare system, the present paper attempts to study the pattern and the trend of spread of this disease and prescribes a mathematical model which governs COVID-19 pandemic using Caputo type derivative. Local stability of the equilibria is also discussed in the paper. Some numerical simulations are given to illustrate the analytical results. The obtained results shows that applied numerical technique is computationally strong for modeling COVID-19 pandemic.

Today, the entire world is witnessing an enormous upsurge in coronavirus pandemic (COVID-19 pandemic). Confronting such acute infectious disease, which has taken multiple victims around the world, requires all specialists in all fields to devote their efforts to seek effective treatment or even control its disseminate. In the light of this aspect, this work proposes two new fractional-order versions for one of the recently extended forms of the SEIR model. These two versions, which are established in view of two fractional-order differential operators, namely, the Caputo and the Caputo–Fabrizio operators, are numerically solved based on the Generalized Euler Method (GEM) that considers Caputo sense, and the Adams–Bashforth Method (ABM) that considers Caputo–Fabrizio sense. Several numerical results reveal the impact of the fractional-order values on the two established disease models, and the continuation of the COVID-19 pandemic outbreak to this moment. In the meantime, some novel results related to the stability analysis and the basic reproductive number are addressed for the proposed fractional-order Caputo COVID-19 model. For declining the total of individuals infected by such pandemic, a new compartment is added to the proposed model, namely the disease prevention compartment that includes the use of face masks, gloves and sterilizers. In view of such modification, it is turned out that the performed addition to the fractional-order Caputo COVID-19 model yields a significant improvement in reducing the risk of COVID-19 spreading.

This paper presents how volatility propagates through the cryptocurrency market. Our paper provides evidence for volatility connectedness on cryptocurrencies. The different econometric techniques, including the stochastic volatility (SVOL) model and time-varying parameter VAR models using a quasi-Bayesian local likelihood (QBLL), are applied to measure the volatility of the cryptocurrency market. Using high-frequency, intra-day data of the largest cryptocurrencies over 2018–2021, we detect the great volatility of the cryptocurrency market are the beginning of 2019, the beginning of 2020, and throughout the year of 2021. The total connectedness values suggest that the cryptocurrency market becomes volatile as the new strains of the COVID-19 appear at the end of 2021. However, by using directional connectedness, we reveal that there are negative and positive spillovers from a specific cryptocurrency to other cryptocurrencies. The great fluctuations in the period before the COVID-19 health crisis stem from the positive resonance (symmetric) between the volatility of each cryptocurrency, while this health crisis leads to substantially positive and negative spillovers (asymmetric) of cryptocurrencies, and this makes market volatility weaker than it actually is.

In this paper, we employ a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to study interlinkages between the cryptocurrency and Vietnam’s stock market by characterizing their connectedness starting from January 1, 2018, to December 31, 2021. We report that the COVID-19 health shocks impact the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that the cryptocurrency market significantly impacts Vietnam’s stock market, especially those with the largest market capitalization like Bitcoin and Ethereum. This market can be held accountable for Vietnam’s stock market volatility. In encountering the COVID-19 pandemic, the effect of the three cryptocurrencies reduced before 2020, around the end of 2019 and the beginning of 2020. However, from the end of 2020–2021, while cryptocurrencies continued their roles as net transmitters for Vietnam’s stock market.

This paper analyzes the impact of government economic interventions to ameliorate the COVID-19 pandemic on the survival of small, micro, and medium enterprises (SMMEs) in South Africa. We use the Cox Proportional Hazards approach and cross-sectional data from King Cetshwayo District Municipality covering 641 SMMEs. The study finds that tax relief was the most important intervention used to sustain SMMEs during the pandemic. Other interventions, such as cash grants and cheap credit, were also used during the period but had a small impact. Our findings support the interventions used by the South African government in mitigating the negative consequences of the pandemic-induced lockdown on small businesses. However, we also note that the magnitude at which the interventions were made could have been lower than what is optimal. The paper recommends the need to increase and have sustainable targeted expenditure during the difficult times to enhance the resilience of SMMEs to accelerate economic development and growth.