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This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.

The authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.

The results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.

This study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.

This finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

This study aims to investigate the impact of the COVID-19 pandemic on both of stock prices and investor's sentiment in China during the onset of the COVID-19 crisis.

In this study, the ADCC-GARCH model was used to analyze the asymmetric volatility and the time-varying conditional correlation among the Chinese stock market, the investors' sentiment and its variation. The authors relied on Diebold and Yilmaz (2012, 2014) methodology to construct network-associated measures. Then, the wavelet coherence model was applied to explore the co-movements between these variables. To check the robustness of the study results, the authors referred to the RavenPack COVID sentiments and the Chinese VIX, as other measures of the investor's sentiment using daily data from December 2019 to December 2021.

Using the ADCC-GARCH model, a strong co-movement was found between the investor's sentiment and the Shanghai index returns during the COVID-19 pandemic. The study results provide a significant peak of connectivity between the investor's sentiment and the Chinese stock market return during the 2015–2016 and the end of 2019–2020 turmoil periods. These periods coincide, respectively, with the 2015 Chinese economy recession and the COVID-19 pandemic outbreak. Furthermore, the wavelet coherence analysis confirms the ADCC results, which revealed that the used proxies of the investor's sentiment can detect the Chinese investors' behavior especially during the health crisis.

This study provides two main types of implications: on the one hand, for investors since it helps them to understand the economic outlook and accordingly design their portfolio strategy and allocate decisions to optimize their portfolios. On the other hand, for portfolios managers, who should pay attention to the volatility spillovers between investor sentiment and the Chinese stock market to predict the financial market dynamics during crises periods and hedge their portfolios.

This study attempted to examine the time-varying interactions between the investor's sentiment proxies and the stock market dynamics. Findings showed that the investor's sentiment is considered a prominent channel of shock spillovers during the COVID-19 crisis, which typically confirms the behavioral contagion theory.

This paper investigates the impact of global sentiment and various coronavirus disease 2019 (COVID-19)-related media coverage news (Media-Hype index; Panic Index; Media Coverage Index, infodemic index and coronavirus statistics) on the dynamics of bitcoin returns during the COVID-19 pandemic using an asymmetric framework.

The authors use an asymmetric framework based on quantile regression (QR) and quantile-on-quantile regression.

QR results show that COVID-19 panic news negatively affects bitcoin market returns at times of extreme bearish. However, COVID-19 bullish sentiment negatively impacts bitcoin market returns during bullish market conditions. Quantile-on-quantile approach's (QQA) empirical results show that the effects of COVID-19-related news on bitcoin returns were heterogeneous, mainly negative and varied across quantiles.

The authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic.

The authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic.

This study contributes to understanding the dynamics of bitcoin returns using various COVID-19 media news.

This study seeks to investigate role of the coronavirus disease 2019 (COVID-19) pandemic on clean energy stocks for the United States for the period 21 January 2020–16 August 2021.

At the empirical stage, the Fourier-augmented vector autoregression approach has been used.

According to the empirical results, the response of the clean energy stocks to the feverish sentiment, lockdown stringency, oil volatility, dirty assets, and monetary policy dies out within a short period of time. In addition, the authors find that there is a unidirectional causality from the feverish sentiment index and the lockdown stringency index to the clean energy stock returns; and from the monetary policy to the clean energy stocks. At the same time, there is a bidirectional causality between the lockdown stringency index and the feverish sentiment index. The empirical findings can be helpful to both practitioners and policy-makers.

Among the COVID-19 variables used in this study is a new feverish sentiment index, which has been constructed using principal component analysis. The importance of the feverish sentiment index is that it allows us to examine the impact of the aggregate level of fear in the economy on clean energy stocks.

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.

This article examines the susceptibility of cryptocurrencies to coronavirus disease 2019 (COVID-19) induced panic in comparison with major stock indices.

The author employs the Bayesian structural vector autoregression to examine the phenomenon in Bitcoin, Ethereum and Litecoin from 2nd January 2020 to 30th June 2021. A similar analysis is conducted for major stock indices, namely S&P 500, FTSE 100 and SSE Composite for comparison purposes.

The results suggest that cryptocurrencies returns suffered immensely in the early days of the COVID-19 outbreak following declarations of the disease as a global health emergency and eventually a pandemic in March 2020. However, the returns for all three cryptocurrencies recovered by April 2020 and remained resistant to further COVID-19 panic shocks. The results are dissimilar to those of S&P 500, FTSE 100 and SSE Composite values which were vulnerable to COVID-19 panic throughout the timeframe to June 2021. The results further reveal strong predictive power of Bitcoin on prices of other cryptocurrencies.

The article provides evidence to support the cryptocurrency as a safe haven during COVID-19 school of thought given their resistance to subsequent shocks during COVID-19. Thus, the author stresses the need for diversification of investment portfolios by including cryptocurrencies given their uniqueness and resistance to shocks during crises.

The author makes use of the novel corona virus panic index to examine the magnitude of shocks in prices of cryptocurrencies during COVID-19.

This paper aims to examine the dynamic return and volatility connectedness for six major industrial metals (tin, lead, nickel, zinc, copper and aluminium) and the coronavirus media coverage index (MCI).

To that purpose, this study applies the fresh time-varying parameter vector autoregression methodology (TVP–VAR model) during the sample period between 2 January, 2020, and 16 April, 2021, that is, covering the three waves of the COVID-19 pandemic crisis.

This study’s results show interesting findings. First, dynamic total return and volatility connectedness changes over time, highlighting a significant increase during the third wave of the pandemic. Second, the MCI index is a leading net transmitter in terms of return and volatility at the introduction of the SARS-CoV-2 coronavirus crisis. Third, this study clearly distinguishes two profiles among industrial metals: copper and tin/zinc as net transmitters and lead and aluminium as net receivers. Finally, the most relevant differences between them are concentrated not only at the beginning of the COVID-19 pandemic (first wave) but also during the second and third waves of the coronavirus outbreak.

To the best of the authors’ knowledge, this is the first research that explores the dynamic return and volatility connectedness in the industrial metal market, applying the TVP–VAR methodology during the first waves of the COVID-19 pandemic crisis.

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.

The purpose of this article is to examine the impact of panic and hysteria news on the volatility of microchip stocks during Covid-19.

The authors use the P-GARCH (1,1) and random effects regression to model/examine the impact of Covid-19 panic and hysteria news on the overall microchip sector and individual firms. They further utilize the SVAR model to examine volatility spill-over from the microchip sector to the automobile and main technology sectors. Their time frame ranges from 6th January 2020 to 30th June 2021 to capture the effects of both waves of Covid-19.

The study results firstly reveal that Covid-19 panic and hysteria news have tremendous potential to model the volatility of microchip sector stock thus confirming the information discovery hypothesis. The authors secondly demonstrate the influence of Covid-19 cases, deaths and policy stringency on stock returns of individual microchip companies in different countries. Finally the authors confirm the presence of volatility spill-over from the microchip sector to other technology sectors.

The authors provide evidence to support the profundity of bad news in predicting stock behavior. The study results depict how Covid-19 has affected microchip stocks so that policy initiatives can be taken to protect the industry. The presence of volatility spill-over signifies the importance of diversifying portfolios by mixing technology and non-technology stocks.

The research strand on Covid-19 and individual sectoral stocks has received limited scholarly attention despite unparallel effects of the pandemic on different sectors.

We investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns.

We used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrency’s countries and the data spans from 22 March 2021 to 6 May 2022.

This research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figures’ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns.

This study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returns’ new determinant.