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According to the Government Response tracker (oxCGRT) index, the strictest policy responses to the coronavirus pandemic from January 2020 to May 2022 belong to Italy, China, Hong Kong, Greece, Austria, Peru, Singapore and Malaysia. The main question is: “this level of strictness has been able to reduce the uncertainty of the stock market?”

To achieve this goal, the authors investigated the effect of oxCGRT index, and the growth rate of COVID-19 confirms cases on stock market uncertainty from January 2020 to May 2022 in the GARCH, EGARCH and TGARCH models.

Among these countries, the oxCGRT index has reduced uncertainty in the stock market only in Malaysia and Singapore. This result says an appropriate pattern of applying government policy responses is more important than the degree of stringency.

The study will contribute to the existing literature by examining the impact of the comprehensive oxCGRT index on the uncertainty of the stock market.

The paper aims to examine some economical, political and health system indicators on the transmission of the COVID-19 transmission within the national system. The main objective is to investigate what are the most effective indicators which have led to the declared numbers by countries.

This study combined multiple sets of data to describe best the economical status of the health system including the government spending on the health system to draw some conclusion regarding the behavior of the pandemic.

Complex emergencies and internal conflicts negatively affected the quality of the reported cases and the size of the pandemic. The health work force was the most determinant factor of the health system. It can sometimes be impossible to understand the epidemic only with epidemiological data or health system one; economical aspects of health system and political situation have to be added to the equation.

The research according to the authors’ knowledge is the most comprehensive comparison so far that investigate the non-covid aspects from a political side in particular in complex emergencies and war situation added health system indicators.

The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic.

The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model.

The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model.

At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

During the COVID-19 pandemic, lockdowns were implemented to achieve two goals: (1) to reduce the number of COVID-19 cases and (2) to reduce the number of COVID-19 deaths. In this paper, the authors aim to look at empirical evidence on how effectively lockdowns achieved these goals among small island developing states (SIDS) and for one specific SIDS economy, Guam.

The authors reviewed existing studies to form two hypotheses: that lockdowns reduced cases, and that lockdowns reduced deaths. Defining a lockdown as a positive value for Oxford COVID-19 government response tracker, OxCGRT's stringency index, the authors tested the above hypotheses on 185 countries, 27 SIDS economies and Guam using correlation and regression analyses, and using different measures of the strictness, duration and timing of the lockdown.

The authors found no evidence to support the hypothesis that lockdowns reduced the number of cases based on data for all 185 countries and 27 SIDS economies. While the authors found evidence to support the hypothesis in the case of Guam, the result required an unrealistically and implausibly long time lag of 365 days. As to the second hypothesis that lockdowns reduced the number of deaths, the authors found no evidence to support it for 185 countries, 27 SIDS economies as well as Guam.

From the review of the existing literature, the authors are the first to conduct this type of study among SIDS economies as a group and on Guam.

According to the Government Response tracker (oxCGRT) index, the overall government response, stringency, economic support, containment and health policies to COVID-19 from January 2020 to December 2022. The main objective of this paper is to explore how stock market performance is affected by these polices, respectively.

The authors employ EGARCH and autoregressive distributional lag (ARDL) models to test the impact of epidemic prevention policy implementation on stock market returns, volatility and liquidity and make cross-country comparisons for six important world economies.

Firstly, the implementation of various preventive policies hurts stock market returns and increases volatility, but there are a few indicators that have no effect or have an easing effect in some countries. Secondly, health policies exacerbate market volatility and have a stronger effect than other policy indicators. Thirdly, In China and the USA, anti-epidemic policies have been shown to worsen liquidity, while in Japan they have been shown to improve liquidity.

First, enrich the growing body of COVID-19 research by comprehensively examining whether and how government prevention policies affect stock market returns, volatility and liquidity. Second, explore the impact of different types of intervention policies on stock market performance, separately.

This study uncovers how government interventions, in terms of stringency and support, shape coronavirus disease 2019's (COVID-19) detrimental impact on organizations' performance. Specifically, this paper studies whether stringency and support play complementary or substitutive roles in lowering COVID-19's impact on organizations' performance.

The authors gathered primary data from USA manufacturing companies and combined this with secondary data from the Oxford COVID-19 Government Response Tracker (OxCGRT) to test the proposed model with structural equation modeling (SEM).

The results show that the stringency approach increases the detrimental impact on both operational and financial performance, while economic support (to households) and fiscal spending (to organizations) work differently on lowering the impacts of COVID-19. Further, these combinative effects only influence the firm's operational performance, albeit in opposite directions.

This study advances the knowledge of government interventions by examining stringency and support's direct and interaction effects on firm performance as a result of the COVID-19 pandemic. The findings contribute to the literature by uncovering the unique roles of both supportive policies, thus differentiating economic support (to individuals/households) from fiscal spending (to organizations) and providing important academic, managerial and policy insights into how government should best initiate and blend stringency and support policies during the COVID-19 pandemic.

Different countries have responded to the pandemic with distinct domestic and international travel restrictions. The purpose of this paper is to investigate the stringency of the coronavirus disease 2019 (COVID-19) countermeasures in Japan against their G20 cohorts. Primary data were monitored at a ski resort in Kyushu regarding the social acceptance of initial COVID-19 countermeasures, ranging from hygiene and local “lockdowns” to border control measures.

The stringency of the COVID-19 countermeasures was examined using data from the Oxford COVID-19 Government Response Tracker (OxCGRT) and triangulated with the early stage social acceptance of survey respondents in Aso Kuju National Park in February 2020 that consisted of 165 valid Japanese language questionnaires.

An one-way analysis of variance (ANOVA) identified significant differences in social acceptance for countermeasures, with more-concerned respondents agreeing more strongly with “low-tech” health protocols, such as washing hands (M = 3.7) or wearing a mask (3.4). More concerned visitors were significantly more likely to modify their travel plans (2.9) or cancel their trip altogether (2.7). Male day trippers were less likely to be concerned by the COVID-19 pandemic.

This paper's originality is derived from a triangulation of the stringency of Japan's initial COVID-19 countermeasures via a combination of comparison with G20 cohorts and social acceptance of domestic snowboarders and skiers. Moreover, by shining a light on the trade-off between public health and human rights, the paper provides a current review of the ethical dimension of a travel restriction debate that is often overlooked in the ongoing pandemic.

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model's forecasting performance and extract some instability measures based on the factor model's eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability.

This study aims to examine the impact of the stringency of COVID-19 protocols on the volatility of sectoral indices during the period 03:2020–05:2021. Specifically, this study investigates the role of economic disturbances on sectoral volatility by applying a range of conditional volatility techniques.

For this analysis, two approaches were adopted. The first approach considers COVID stringency as a factor in the conditional variance equation of sectoral indices. In contrast, the second approach considers the stringency indicator as a possible determinant of their estimated conditional volatility.

Results show that the stringency of the protocols throughout the pandemic phase led to an instantaneous spike followed by a gradual decrease in estimated volatility of all the sectoral indices except pharma and health care. Specific sectors such as bank, FMCG, consumer durables, financial services, IT, media and private banks respond to protocols expeditiously compared to other sectors.

The key contribution of this study to the existing literature is the innovative approach. The inclusion of the COVID stringency index as a regressor in the variance equation of the conditional volatility techniques was a distinctive approach for assessing the volatility dynamics with the stringency of COVID protocols. Furthermore, this study also adopts an alternative approach that estimates the conditional volatility of the indices and then tests the effect of the stringencies on estimated volatility in a regression framework.