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The purpose of this study is to investigate safe-haven properties of environmental, social and governance (ESG) stocks in global and emerging ESG stock markets during the times of COVID-19 so that portfolio managers and equity market investors could decide to use ESG stocks in their portfolio hedging strategies during times of health and market crisis similar to COVID-19 pandemic.

The study uses a wavelet coherence framework on four major ESG stock indices from global and emerging stock markets, and two proxies of COVID-19 fear over the period from 5 February 2020 to 18 March 2021.

The results of the study show a positive co-movement of the global COVID-19 fear index (GFI) with ESG stock indices on the frequency band of 32 to 64 days, which confirms hedging and safe-haven properties of ESG stocks using the health fear proxy of COVID-19. However, the relationship between all indices and GFI is mixed and inconclusive on a frequency of 0–8 days. Further, the findings do not support the safe-haven characteristics of ESG indices using the market fear proxy (IDEMV index) of COVID-19. The robustness analysis using the CBOE VIX as a proxy of market fear supports that ESG indices do not possess safe-haven properties. The results of the study conclude that the safe-haven properties of ESG indices during the ongoing COVID-19 pandemic is contingent upon the proxy of COVID-19 fear.

The findings have important implications for the equity investors and assetty managers to improve their portfolio performance by including ESG stocks in their portfolio choice during the COVID-19 pandemic and similar health crisis. However, their investment decisions could be affected by the choice of COVID-19 proxy.

The authors believe in the originality of the paper due to following reasons. First, to the best of the knowledge, this is the first study investigating the safe-haven properties of ESG stocks. Second, the authors use both health fear (GFI) and market fear (IDEMV index) proxies of COVID-19 to compare whether safe-haven properties are characterized by health fear or market fear due to COVID-19. Finally, the authors use the wavelet coherency framework, which not only takes both time and frequency dimensions of the data into account but also remains unaffected by data stationarity and size issues.

The purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread.

This study uses wavelet coherence approach because it allows to observe lead–lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021.

The results document a strong positive co-movement between implied volatility indices and two proxies of the COVID-19 fear. However, in all the cases, the infectious disease equity market volatility index (IDEMVI), the COVID-19 proxy, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indices than the COVID-19 fear index (C19FI). This study also finds that the UK’s implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movement of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study’s robustness analysis using partial wavelet coherence provides further strengths to the findings.

The investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK’s stock markets.

This study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et al., 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead–lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al., 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020.

This paper aims to investigate the moderating role of government policy interventions amid the early spread of novel coronavirus (COVID-19) (January–May 2020) on the investor sentiment and stock returns relationship.

This paper uses panel data from a sample of 53 countries to examine the impact of investor sentiment, measured by the financial and economic attitudes revealed by the search (FEARS) index (Da et al., 2015) on the stock return.

The moderating role of government policy response indices with the FEARS index on the global stock returns is further explored. This paper finds that government policy responses have a moderating role in the sentiment and stock returns relationship. The effect holds true even when countries are split based on five classifications, i.e. cultural distance, health standard, government effectiveness, social well-being and financial development. The results are robust to an alternative measure of pandemic search intensity, quantile regression and two measures of stock market activity, i.e. conditional volatility and exchange traded fund returns.

The sample period of this study encompasses the early spread phase (January–May 2020) of the novel COVID-19 spread.

This paper provides some early evidence on whether the government policy interventions are helpful to mitigate the impact of investor sentiment on the stock market. The paper also helps to shed better insights on the role of different country characteristics for the sentiment and stock return relationship.

The crude oil market has experienced an unprecedented overreaction in the first half of the pandemic year 2020. This study aims to show the performance of the global crude oil market amid Covid-19 and spillover relations with other asset classes.

The authors employ various pandemic outbreak indicators to show the overreaction of the crude oil market due to Covid-19 infection. The analysis also presents market connectedness and spillover relations between the crude oil market and other asset classes.

One of the essential findings the authors report is that the crude oil market remains more responsive to pandemic fake news. The shock of the global pandemic panic index and pandemic sentiment index appears to be more promising. It has also been noticed that the energy trader's sentiment (OVX and OIV) was measured at a too high level within the Covid-19 outbreak. Volatility spillover analysis shows that crude oil and other market are closely connected, and the total connectedness index directs on average 35% contribution from spillover. During the initial growth of the infection, other macroeconomic and political events remained to favor the market. The second phase amidst the pandemic outbreak harms the global crude oil market. The authors find that infectious diseases increase investor panic and anxiety.

The crude oil investors' sentiment index OVX indicates fear and panic due to infectious diseases and lack of hedge funds to protect energy investments. The unparalleled overreaction of the investors gauged in OVX indicates market participants have paid an excessive put option (protection) premium over the contagious outbreak of the infectious disease.

The empirical model and result reported amid Covid-19 are novel in terms of employing a news-based index of the pandemic, which are based on the content analysis and text search using natural processing language with the aid of computer algorithms.

原油市場在流行病肆虐的2020年的頭半年經歷史無前例的過度反應。本文旨在顯示全球原油市場在2019冠狀病毒病流行期間的表現及原油市場與其它資產類別之溢出關係.

我們使用各種大流行病爆發的指標，來顯示原油市場因2019冠狀病毒病的感染而過度反應。我們的分析亦涉及市場的關聯性及原油市場與其它資產類別之溢出關係.

我們其中一個基本的發現是: 原油市場仍對大流行病的虛假新聞有更迅速的反應。全球大流行病恐慌性指數及大流行病情緒指數所帶來的震驚似乎是有希望的。大家亦察覺，能源交易商的情緒(OVX及OIV) 在2019冠狀病毒病爆發期間被測量為處於太高的水平。波動溢出分析顯示、原油與其它市場有密切的關係，而總關聯度指數引導平均35%來自溢出量的作用。在感染傳播初期，其它的宏觀經濟和政治事件仍對市場有利。在大流行病爆發期間的第二階段則損害全球原油市場。我們發現，傳染病會增加投資者的恐慌和焦慮.

原油投資者的情緒指數OVX顯示因傳染病及因缺乏對沖基金來保障能源投資而帶來的懼怕和恐慌。於OVX測算到的投資者空前的過度反應顯示市場參與者就這傳染病的感染爆發付出過量的賣權(保障)權利金.

我們的經驗模型和在2019冠狀病毒病肆虐期間匯報的研究結果，從使用以新聞為基礎的流行病指數的角度而言是新穎的。而這些全以內容分析和正文搜尋為基礎、使用自然語言處理，並輔以計算機算法.

This study aims to examine the relationship between the fear index and initial public offering (IPO) aftermarket liquidity in ASEAN during the bearish time, the COVID-19 pandemic.

This study uses random effect panel regression analysis using two proxies of IPO aftermarket liquidity, namely, volume and turnover, on data of 90 IPO companies in the ASEAN-5 countries over four study periods: 30, 60, 90 and 100 days, after their IPOs.

The results indicate that the COVID-19 fear index significantly affects liquidity for all periods. The fear index decreases the stock aftermarket liquidity of ASEAN-5 IPO companies. The findings are consistent with additional tests.

This study initiates research during the COVID-19 pandemic in ASEAN-5 countries. Furthermore, while the other studies examine the stock performance of existing listed companies, this study focuses exclusively on the liquidity of companies that went public through IPOs in 2020.

The purpose of this paper is to capture the investors' mood related to the COVID-19 pandemic and analyze its impact on the stock market returns.

To capture the investor mood related to the COVID-19 pandemic, the authors construct a unique COVID-19 fear index based on the Search Volume Index (SVI) from Google Trends (http://www.Google.com/trends/) of the search terms related to COVID-19 words and phrases as revealed by Google and Internet dictionaries. The COVID-19 fear index was used to investigate its impact on the stock market returns.

The study finds a strong negative association between COVID-19 fear and stock returns. Unlike other studies, the relationship is persistent for a significant period. This relationship is not found to reverse in the following days. The results also highlight that COVID-19 fear strongly impacts the stock market. The sentiment persists for a significant period and is not reversed soon, unlike the regular times in earlier studies.

The study is among the very few studies that constructed COVID-19 fear index using several Google search terms and captured its impact on the stock market returns.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.

This study aims to uncover the main predictors of financial distress in the Gulf Cooperation Council (GCC) countries using a wide range of global factors and asset classes.

This study uses novel approaches that take into account extreme events as well as the nonlinear behavior of time series over various time intervals (i.e. short, medium and long term) and during boom and bust episodes. This study primarily uses the conditional value at risk (CoVaR), the quantile multivariate causality test and the partial wavelet coherence method. The data collection period ranges from March 2014 to September 2022.

US T-bills and gold are the primary factors that can increase financial stability in the GCC region, according to VaRs and CoVaRs. More proof of the predictive value of the oil, gold and wheat markets, as well as geopolitical tensions, uncertainty over US policy and volatility in the oil and US equities markets, is provided by the multivariate causality test. When low extreme quantiles or cross extreme quantiles are taken into account, these results are substantial and sturdy. Lastly, after adjusting for the effect of crude oil prices, this study’s wavelet coherence results indicate diminished long-run connections between the GCC stock market and the chosen global determinants.

Despite the implications of the author’s research for decision makers, there are some limitations mainly related to the selection of Morgan Stanley Capital International (MSCI) GCC ex-Saudi Arabia. Considering the economic importance of the Kingdom of Saudi Arabia (KSA) in the region, the author believes that it would be better to include this country in the data to obtain more robust results. In addition, there is evidence in the literature of the existence of heterogeneous responses to global shocks; some markets are more vulnerable than others. This is another limitation of this study, as this study considers the GCC as a bloc rather than each country individually. These limitations could open up further research opportunities.

These findings are important for investors seeking to manage their portfolios under extreme market conditions. They are also important for government policies aimed at mitigating the impact of external shocks.

This paper aims to analyse COVID-19 indices and blockchain features on Bitcoin and Ethereum returns, respectively. The authors focus on the most used and owned cryptocurrencies that cover Europe, the US and Asian countries.

An autoregressive distributed lag panel (pooled mean group and mean group) is utilized, and a robustness check is incorporated by using a Random Effect Model and Generalized Method of Moments (GMM).

Four new findings were discovered, including (1) the vaccine confidence index (VCI) pushes economic recovery and increased demand for the Bitcoin market, but the opposite result was interestingly observed from Ethereum; (2) the blockchain features were revealed to be essential to Bitcoin, while they were irrelevant to Ethereum for short-run country-specific results; (3) the hash rate and network difficulty moved inversely during the pandemic; and (4) the government played a significant role in taking action during uncertain times and regarding cryptocurrency policies.

VCI is constructed by the most used vaccine type in our sample countries (i.e. Pfizer), as the data for a specific classification by each type is still unavailable.

Providing an evenly distributed vaccination program primary vaccination series against COVID-19 to the citizens is an essential duty of the government. Bitcoin policymakers and investors should watch the COVID-19 vaccine distributions closely as it will affect its return. Ethereum is emphasized to keep developing its smart contract which appeared to outplay other blockchain features. Cryptocurrency investors should be wise in their investment decisions by analysing the news thoroughly.

This research emphasizes that the success in the roll-out of COVID-19 vaccination requires citizens' willingness to participate and their trust in the vaccine's efficacy. Such self-awareness and self-discipline in society can ultimately empower individuals and stabilise the economy. Nevertheless, the implementation of health protocols is still highly required to prevent the spread of new variants of COVID-19.

This is the first study that attempts to construct a VCI which denotes the confidence derived from the administration of full-dose COVID-19 vaccines (an initial vaccine and a second vaccine). The authors further find the impact on cryptocurrency returns. Next, blockchain size is utilized as a new determinant of cryptocurrencies.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.