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After the COVID-19 outbreak, the Federal Reserve has undertaken several monetary policies to alleviate the pandemic consequences on the markets. This paper aims to evaluate the effects of the Federal Reserve monetary policy on the cryptocurrency dynamics during the COVID19 pandemic.

We examine the response and feedback effects via an event study methodology. For this purpose, abnormal returns (AR) and cumulative abnormal returns (CARs) around the first FOMC (Federal Open Market Committee) announcement related to the COVID-19 pandemic for the top five cryptocurrencies are explored. We, further investigate the effect of the eight FOMC statement announcements during the COVID19 pandemic on these cryptocurrencies (Bitcoin, Ethereum, Tether, Litecoin, and Ripple). In the above-mentioned crypto-currency markets, we investigate the presence of bubbles by using the PSY test. We then examine the concordance of the dates of these bubbles with the dates of the FOMC announcements.

The empirical results show that the first FOMC event has a negative significant effect after 4 days of the announcement date for all studied cryptocurrencies except Tether. The results also indicate that cumulative abnormal returns are significant during the event windows of (−3,8), (−3,9), and (−3,10). Besides, we find that Bitcoin, Ethereum and, Litecoin lived short bubbles lasting for a few days. However, Ripple and Tether markets present no bubbles and no explosive periods.

This paper presents trained proof that FOMC announcements have a positive effect on volatility's predictive capacity. This work therefore promotes the study of the data quality of volatility in future research as well.

The justified effect of the FOMC announcements on cryptocurrency as a speculative asset has practical implications for investors in building their trading strategies in anticipation of the next FOMC announcement. Therefore, this study implies that the FOMC announcements contain very relevant information for investors in the cryptocurrency market. This research may not only encourage a better understanding of the evolution of the expectations of policymakers, but also facilitate a better understanding of how these expectations are developed.

The COVID-19 pandemic has disturbed the stability of financial markets, inciting the Fed to take some monetary regulations. To the best of our knowledge, this study is the first one that analyses the response of five major cryptocurrencies to FOMC announcements during COVID 19 pandemic and associates these dates with bubble occurrences.

The potential growth in cryptocurrencies has raised serious ethical and religious issues leading to a new investment rethinking. This paper aims to identify the influence of religiosity on cryptocurrency acceptance through an extended technology acceptance model (TAM) model.

In the first phase, this research develops a conceptual model that extends the theory of the TAM by integrating the religiosity component. In the second phase, the proposed model is tested using search volume queries in daily frequencies from 01/01/2018 to 31/12/2022 and structural equation modeling (SEM).

The empirical results demonstrate a significant positive effect of religiosity on the intention to use cryptocurrency, the users' perceived usefulness (PU) and ease of use (PEOU). Besides, the authors note that PEOU positively influences the intention. Furthermore, religiosity indirectly affects the intention through the PEOU and positively impacts the intention through the PU. In the same way, PEOU has a considerable indirect effect on the intention through PU.

This study has practical and theoretical contributions by providing insights into the cryptocurrency acceptance factors. In other words, it contributes to the literature by extending TAM models. Practically, it helps managers determine factors affecting the intention to use cryptocurrencies. Therefore, they can adjust their industry according to the suitable characteristics for creating successful projects.

Identifying the effect of religiosity on cryptocurrency users' choices and decisions has a social added value as it provides an understanding of the evolution of psychological variants.

The findings emphasize the importance of integrating big data to analyze users' attitudes. Besides, most studies on cryptocurrency acceptance are investigated based on one kind of religion, such as Christianity or Islam. Nevertheless, this paper integrates the effect of five types of faith on the users' intentions.

The mining process is essential in cryptocurrency networks. However, it consumes considerable electrical energy, which is undoubtedly harmful to the environment. In response, energy-conserving cryptocurrency projects with reduced energy requirements or based on renewable energies have been developed. Recently, the COVID-19 pandemic and the Russian invasion of Ukraine ignited an unprecedented upheaval in financial products, especially in cryptocurrency and energy markets. Therefore, the paper aims to explore the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict.

This paper investigates the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict. Their competitiveness is compared with conventional ones by analyzing their efficiency through multifractal detrended fluctuation analysis and automatic variance ratio during the COVID-19 and Russian invasion periods.

The empirical results show that all investigated energy-conserving cryptocurrencies negatively responded to the pandemic and positively reacted to the Russian invasion. On the other hand, all conventional cryptocurrencies reacted negatively to the COVID-19 pandemic and the amid-Russian attack. Besides, Bitcoin and SolarCoin were the least inefficient before the outbreak of COVID-19. Nevertheless, the Ethereum market became the most efficient after the pandemic spread. Similarly, the efficiency of Ripple was the most significant during the conflict between Russia and Ukraine. The energy crisis caused by Russia benefited the efficiency of the studied energy-conserving cryptocurrencies.

This research is of interest to investors seeking opportunities in these energy-conserving cryptocurrencies and policymakers working to implement reforms to improve their market efficiency and promote long-term financial market growth.

To the best of the authors' knowledge, the behavior of cryptocurrencies based on renewable and reduced energy during the recent conflict between Russia and Ukraine has not been explored.

After the COVID-19 outbreak, the Federal Reserve has undertaken several monetary policies to alleviate the pandemic consequences on the stock markets leading to a misunderstanding on the cryptocurrency market response. This paper aims to evaluate the effects of the Federal Reserve monetary policy on the Islamic and conventional cryptocurrency dynamics during the COVID-19 pandemic. We, specifically, examine the associate bubbles and feedbacks effects.

This paper developed a novel methodology that detects market bubbles using the statistical indicators defined by Psychological (PSY) tests. It also investigated the effect of the Federal Open Market Committee (FOMC) announcements on conventional and Islamic cryptocurrencies compatible with Islamic laws “Shari’ah” by using the event-driven regression.

The empirical results show that the FOMC announcements have a positive significant effect after one day of the event and a negative effect before two days of the announcement on the conventional cryptocurrency markets. However, the reaction of Islamic cryptocurrencies to these events is not significant except for Hello Gold after one day of the announcement. Besides, the Hello Gold and X8X cryptocurrencies present no bubbles during this period. However, Bitcoin and Ethereum markets have short-lived bubbles.

The main contribution of this study is the investigation of the response and vulnerability to pandemic shocks of a new category of cryptocurrencies backed by tangible assets. This work has practical implications as it provides new insights into trading opportunities and market reactions.

To our knowledge, this work is the first study that compares the response of Islamic and conventional cryptocurrency markets to FOMC announcements during the COVID-19 pandemic and examines the presence of bubbles in these markets. Besides, the originality of this work is derived from the novelty of the data employed and the method used (PSY tests) in this study.

Sustainable development hinges on a crucial shift to renewable energy, which is essential in the fight against global warming and climate change. This study explores the relationships between artificial intelligence (AI), fuel, green stocks, geopolitical risk, and Ethereum energy consumption (ETH) in an era of rapid technological advancement and growing environmental concerns.

This research stands at the forefront of interdisciplinary research and forges a path toward a comprehensive understanding of the intricate dynamics governing green sustainability investments. These objectives have been fulfilled by implementing the innovative quantile time-frequency connectedness approach in conjunction with geopolitical and climate considerations.

Our findings highlight coal market dominance and Ethereum energy consumption as critical short- and long-term market volatility sources. Additionally, geopolitical risks and Ethereum energy consumption significantly contribute to volatility. Long-term factors are the primary drivers of directional volatility spillover, impacting green stocks and energy assets over extended periods. Additionally, SHapley Additive exPlanations (SHAP) findings corroborate the quantile time-frequency connectedness outcomes.

This study highlights the critical importance of transitioning to sustainable energy sources and embracing digital finance in fostering green sustainability investments, illuminating their roles in shaping market dynamics, influencing geopolitics and ensuring the long-term sustainability required to combat climate change effectively.

The study offers practical sustainability implications by informing green investment choices, strengthening risk management strategies, encouraging interdisciplinary cooperation and fostering digital finance innovations to promote sustainable practices.

The implementation of the quantile time-frequency connectedness approach, in line with considering geopolitical and climate factors, marks the originality of this paper. This approach allows for a dynamic analysis of connectedness across different distribution quantiles, providing a deeper understanding of variable interactions under varying market conditions.

Cryptocurrencies lack fundamental values and are often subject to behavioral bias leading to market bubbles. This study aims to investigate the contribution of the coronavirus pandemic to the creation of market bubbles.

This study identifies four major cryptocurrency market bubbles by using the Phillips et al. (2016) (hereafter PSY) test. Subsequently, the co-movements of the coronavirus proxies with PSY measurement using the wavelet approach were studied.

Short-lived bubbles are detected at the beginning of the studied period, and more extended bubble periods are identified at the end. Besides, the empirical results show evidence of significant negative co-movement between each pandemic proxy and each cryptocurrency bubble measurement.

Given the complex financial dynamics of the cryptocurrency markets due to some behavioral biases in some circumstances, investors can benefit from the date stamping of the bubbles bursting to make the best trading positions. In the same way, governments could support the healthy development of cryptocurrencies by preventing bubbles during such pandemics.

The financial bubble is commonly attributed to a change in investor behavior. Because traders and investors think they can resell the asset at a higher price in the future. This study explored the contribution of the COVID-19 pandemic in the creation of these bubbles by date stamping their occurrence and explosive periods. To the best of the authors’ knowledge, this study is the first attempt that explores the contribution of the COVID-19 pandemic to the creation of bubbles caused by a change in the investors’ behavior.

Unlike previous crisis where investors tend to put their assets in safe havens like gold, the recent coronavirus pandemic is characterised by an increase in the Bitcoin purchasing described as risk heaven. This paper aims to analyse the Bitcoin dynamics and the investor response by focusing on herd biases. Therefore, the main objective of this work is to study the degree of efficiency through multifractal analysis in order to detect herd behaviour leading to build the best predictions and strategies.

This paper develops a novel methodology that detects the presence of herding biases and assesses the inefficiency of Bitcoin through an inefficiency index (MLM) by using statistical indicators defined by measures of persistence. This study, also, investigates the nonlinear dynamical properties of Bitcoin by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA) leading to deduce the effect of COVID-19 on the Bitcoin performance. Besides, this work performs an event study to capture abnormal changes created by COVID-19 related events capable to analyse the Bitcoin market response.

The empirical results of the generalized Hurst exponent GHE estimation indicates that Bitcoin is multifractal before this pandemic and becomes less fractal after the outbreak. Using an efficiency index (MLM), Bitcoin is found to be more efficient after the pandemic. Based on the Hausdorff topology, the authors showed that this pandemic has reduced the herd bias.

The uncertainty of COVID-19 disease and the lasting of its duration make it difficult to make the best prediction.

The main contribution of this study is the evaluation of the Bitcoin value after the COVID19 outbreak. This work has practical implications as it provides new insights on trading opportunities and social reactions.

To the authors’ knowledge, this work represents the first study that analyses the Bitcoin response to different events related to COVID-19 and detects the presence of herding behaviour in such a crisis.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

The purpose of this paper is to present the Islamic stock and Sukuk market efficiency and focus on the presence of investor herding behaviour (HB) captured by Hurst exponent estimation.

The Hurst exponent was estimated with various methods. The authors studied the evolving efficiency of the “Dow Jones” indices from 1 January 2010 to 30 December 2016 using a rolling sample of the Hurst exponent. In addition, they used a time-varying parameter method based on the Hurst of delayed returns. After that, the robust Hurst method was considered. In the next step, the efficiency of the different activity types of Islamic bonds was studied using an efficiency index. Finally, the Hurst exponent estimates were applied to assess the presence of HB.

The results show that, firstly, there’s a strong correlation between the “DJIM” and “DJSI” prices and returns. Secondly, by using robust Hurst estimate, it is observed that the “DJIM” is the most efficient market. The Hurst exponent estimation results show that HB is more intensive in the Islamic stock market. These results indicate also the inexistence of this behaviour in the studied Sukuk market.

Sukuk as Islamic financial assets is recent. Their relative time series are not long enough to apply the long memory approach. Furthermore, this work can be extended to study other Islamic financial markets.

Herding affects risk-return characteristics of assets and has an impact on asset pricing models. Practitioners are interested in understanding herding and its timing as it might create profitable trading opportunities.

This work analyses the impact of Islamic principles on the financial markets and their ability to understand some behavioural biases.

This study contributes to the literature by identifying the efficiency and the presence of HB with Hurst exponent estimation in Islamic markets.

Nowadays, Bitcoin is facing many environmental problems arising from the proof of work based on blockchain. For this reason, Bitcoin Green (BITG) has been created and would solve these issues. The purpose of this paper is to visualize the users’ perception toward BITG through Twitter text analysis.

The big data used in this study includes two sources. The first data were extracted from the “Google Trends” engine during the period between 20 September 2015 and 15 September 2020. The second data were extracted from the Twitter application. This research explores the perceived ease of use, the perceived usefulness, the social influence, the perceived control and the user attitudes toward BITG. Therefore, lexicon-based sentiment analysis techniques combined with different dictionaries are built to visualize the drivers of investor attitudes toward the BITG using Twitter text messages. Besides, this study has checked the validity of two main assumptions using the normality (Jarque-Bera) and Kruskal-Wallis rank sum tests capable to conclude whether users mostly perceive BITG as a sustainable technology.

This empirical work affords insights into users’ intentions by exploring the drivers of BITG perception. The results show that users positively perceive the use of BITG as a sustainable blockchain. Besides, its usefulness is more appreciated from its ethical and technological characteristics, and its perceived application is mainly based on investment and coin offering use. Similarly, users are mostly showing positive emotions toward BITG.

Tweets related to “BITG” are not as voluminous as the other cryptocurrencies like Bitcoin and Ethereum, which make it difficult to extract all the characteristics and use cases.

To the best of the authors’ knowledge, this work is the first one that uses the theory of planned behavior and the theory of acceptance model to explore cognitive factors in understanding investor intentions in adopting BITG.