This study aims to analyze the impact of internal variables, including total Ethereum, number of transactions, fees per transaction, and number of active wallets, as well as external variables, namely the price of Bitcoin and the price of gold, on global Ethereum prices. The study utilizes daily data covering the period from December 31, 2016, to December 31, 2021. The data analysis employs time series data with the assistance of Eviews 10 and the error correction model (ECM) method. The study's findings indicate that total Ethereum, number of transactions, fees per transaction, number of active wallets, price of Bitcoin, and price of gold collectively exert a significant influence on Ethereum prices. However, when examined individually, total Ethereum demonstrates a negative impact and lacks statistical significance on Ethereum prices. Similarly, the number of transactions exhibits a negative and significant effect on Ethereum prices. Conversely, transaction fees, number of active wallets, and the price of Bitcoin have a positive and significant impact on Ethereum prices. Meanwhile, global gold prices do not exhibit any influence on Ethereum prices.
Muhammad Mahmudul Karim, Abu Hanifa Md. Noman, M. Kabir Hassan, Asif M. Khan · 5 authors
Purpose This paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic. Design/methodology/approach The MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons. Findings The authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons. Practical implications The study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios. Originality/value This study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.
Blockchain technology (BC) and big data analytics capability (BDAC) are two crucial emerging technologies that have attracted significant attention from businesses and academia. However, their combined effect on business model innovation (BMI), along with the moderating role of environmental uncertainty and the mediating influence of corporate entrepreneurship, remains underexplored. To fill this gap, the present study investigates the combined effects of BDAC and blockchain adoption on BMI and explores the mediating role of corporate entrepreneurship as well as the moderating effect of environmental uncertainty. Drawing on the dynamic capability view (DCV) and the related literature, this study investigates these relationships using a conceptual framework hypothesising that (1) BDAC and blockchain adoption affect BMI through corporate entrepreneurship and (2) environmental uncertainty moderates these relationships. Consistent with the main theoretical arguments, our results, based on a sample of 284 employees working in Australian firms, indicate direct and indirect impacts of both BDAC and blockchain adoption on BMI. Corporate entrepreneurship was found to play a partial mediating role in the relationship between the two technologies, while BMI and environmental uncertainty were found to be significant moderators. These findings have significant theoretical and practical implications for companies striving to innovate their BMI. The results suggest that the synergistic effects of BDAC and blockchain technologies together create entrepreneurial activities and strategies to generate value, thus enabling BMI. Furthermore, the mediating role of corporate entrepreneurship and the moderating effect of environmental uncertainty have important theoretical implications for innovative BMI and management. As such, this study highlights the potential of BDAC and blockchain technologies to drive sustainable business practices, offering insights into how these technologies can contribute to economic, social, and environmental sustainability through innovative business models.
This study employs the Shanghai Environment and Energy Exchange as a case study to investigate the effects of blockchain technology applications on transaction prices within the carbon trading market. Utilizing an event study methodology, the research demonstrates that blockchain technology significantly enhances the transparency, security, and efficiency of the carbon market, thereby exerting a positive influence on transaction prices. Nonetheless, the study also identifies several challenges associated with blockchain applications, including increased costs, heightened energy consumption, transaction delays, and substantial learning costs. To mitigate these issues, the study proposes optimizing blockchain architecture, incorporating Layer 2 technologies to expedite transaction processes, and developing innovative regulatory frameworks.
This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoinâs energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoinâs energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoinâs future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoinâs energy consumption.
Purpose: What matters is that blockchain may be used to record anything of value, not only financial transactions. It is becoming increasingly clear that blockchain technology will drastically alter several industries, notably finance. Without a question, the financial industry is leading the way in the use of blockchain technology. Blockchain is quickly transforming the world economy. Given that distributed ledger technology, or blockchain, has the potential to always have a positive impact on society and the economy, this impact is crucial. Actually, there are more than just economic advantages to the blockchain, and some organizations have already begun to use its technological capabilities to solve issues in the real world. In order to determine the influence of applied blockchain on economic sustainability as well as the benefits and drawbacks of blockchain application for economy, this study will do so. Design/ methodology/ approach: The terms "blockchain" and " economy" were used to find qualitative information in earlier work. Finally, 50 articles in the fields of business, management, and accounting that had undergone peer review as well as book chapters and conference proceedings were chosen. White papers and unreviewed books were removed as non-scientific sources. Qualitative data were collected from previous literature using the keywords âblockchainâ and âeconomyâ. Then, preliminary data was used, by conducting 15 interviews with experts and managers in the financial sector in Egypt about their opinion on the adoption of artificial intelligence and its impact on economic sustainability. Findings: From the interviews, the study collected more detailed information about the blockchain. This could be represented in the three main themes; theme of blockchain advantages, theme of blockchain disadvantages and challenges and theme of blockchain opportunities. Finally, some recommendations were made to decision makers as well as future researchers in this field according to the study results. Received: 06 February 2024 Accepted: 12 June 2024 Published: 30 June 2024
This research analyzes the dynamic relationships between the economic and political uncertainty index and the fear index in global markets and cryptocurrencies using the wavelet-based DCC-GARCH method, considering different time scales. Monthly data sets for the periods 2012âApril 2024 for GEPU,VIX, and Bitcoin and April 2016âApril 2024 for Ethereum are used in the study. Findings are obtained in terms of the volatility interaction between cryptocurrencies (Bitcoin and Ethereum) and GEPU and VIX, as well as four different time scales representing the short, medium, and long term. As a result of the analysis based on raw data, it was found that there is no volatility interaction between cryptocurrencies and GEPU and VIX returns. However, there is a volatility interaction between past volatility shocks and current period volatility shocks in the 4-8 and 16-32 month investment cycle periods of VIX, Bitcoin, GEPU, and Ethereum and time scales. These results, which show that volatility shocks persist in both 4-month and 16-month investment cycles, have significant implications for investors and policymakers. They highlight the need for comprehensive information about changes in the global economy and politics, and they are expected to provide insights for both investors and policymakers.
Majid Mirzaee Ghazani, Ali Akbar Momeni Malekshah, Reza Khosravi
Abstract We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmazâs (Int J Forecast 28:57â66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per BarunĂk and KĆehlĂkâ (J Financ Econ 16:271â296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects.
This study investigates the asymmetric impacts of Bitcoin prices on Bitcoin energy consumption. Two series are shown to be chaotic and non-linear using the BDS Independence test. To take into consideration this nonlinearity, we employed the QNARDL model as a traditional technique and Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) as non-conventional approaches to study the link between Bitcoin energy usage and Bitcoin prices. Referring to QNARDL estimates, results show that the relationship between Bitcoin energy use and prices is asymmetric. Additionally, results demonstrate that changes in Bitcoin prices have a considerable effect, both short- and long-run, on energy consumption. As a result, any upsurge in the price of Bitcoin leads to an immediate boost in energy use. Furthermore, the short-term drop in Bitcoin values causes an increase in energy use. However, higher Bitcoin prices reduce energy use in the long run. Otherwise, every decline in Bitcoin prices leads to a long-term reduction in energy use. In addition, the performance metrics and convergence of the cost function provide evidence that the XGBoost model dominates the SVM model in terms of Bitcoin energy consumption forecasting. In addition, we analyze the effectiveness of several modeling approaches and discover that the XGBoost model (MSE: 0.52%; RMSE: 0.72 and R2: 96%) outperforms SVM (MSE: 4.89; RMSE: 2.21 and R2: 75%) in predicting. Results indicate that the forecast of Bitcoin energy consumption is more influenced by positive shocks to Bitcoin prices than negative shocks. This study gives insights into the policies that should be implemented, such as increasing the sustainable capacity, efficiency, and flexibility of mining operations, which would allow for the reduction of the negative impacts of Bitcoin price shocks on energy consumption.
The linkage between the metal market and crypto assets is a topic of concern. Utilizing a novel TVP-VAR framework, this study examines the volatility transmission between NFTs and precious/industrial metals. The results show strong connectivity, with most NFTs being net transmitters, and industrial metals being mostly net spillover recipients. Moreover, the connectivity was affected by the COVID-19 epidemic and the Russia-Ukraine War. Considering the potential reference value that this empirical fact may bring to market participants, this paper divides the time samples and uses the DCC-GARCH t-copula method to analyse the portfolio construction of every sub-sample. The research investigates and compares the hedge ratio, optimal weights, and hedging effectiveness of NFT-industrial metals and NFT-precious metals. These findings can bring potential inspiration to investors, market regulators, and policymakers.
Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.
The recent global crises have heightened financial market instability, surging the need for diversification, hedging, and safe haven assets to mitigate stock market risks. This study employs a Quantile Vector Autoregression (Q-VAR) approach to analyze the interconnectedness between gold-backed cryptocurrencies and G7 stock market indices during crises spanning from December 1, 2020, to July 5, 2023. Our findings indicate a robust association between digital gold and financial assets, with a total connectedness index (TCI) of 58.64%. Remarkably, G7 stock indices emerge as significant contributors to market fluctuations compared to digital assets, exerting influence ranging from 24% to 37%, thereby underscoring the potential of gold-backed cryptocurrencies for effective diversification strategies. Dynamic analysis during crises indicates the pivotal role of DGX as a safe haven, alongside identifying NIKKEI as a significant net receiver. Furthermore, the total net directional connectedness examination corroborates the status of gold-backed cryptocurrencies as net receivers, reaffirming their safe-haven abilities. Intriguingly, an in-depth examination across quantiles validates symmetrical dynamic connectedness, with G7 indices predominantly functioning as net transmitters of spillover. Our empirical findings underscore the compelling safe-haven potential in gold-backed cryptocurrencies, offering valuable insights for investors, policymakers, and portfolio optimization during turbulent market conditions.
In this article, we attempt to analyze and compare the safe-haven features of gold, Bitcoin and gold-backed cryptocurrency against the stock and banking indices of G7 countries during the outbreak of adverse events. To do so, we examine dynamic relationships between different assets and we compute optimal hedge ratios for different couples using the corrected Asymmetric Dynamic Conditional Correlation-Exponential Generalized Autoregressive Conditional Heteroscedasticity and corrected Asymmetric Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity models. We clearly show that gold and gold-backed cryptocurrency maintain higher weights in optimal portfolios compared to Bitcoin. We also report that shocks due to unexpected events increasingly affect dynamic correlations, asset weights and hedge ratios. This underscores the need for regular demand for rebalancing the hedge positions and effective risk management. We thereafter show that the relationship between Bitcoin (gold-backed cryptocurrency) and G7 indices is highly affected by the outbreak of COVID-19 pandemic. Such findings highlight the hedging and safe-haven features of different asset classes against stock markets. They could have insightful implications for investors who want to minimize investment risks and policymakers who are worried about the financial consequences of different unexpected events.
Blockchain technology is expected to have a radical impact on most industries by boosting security, transparency, and efficiency. This work considers the potential benefits of blockchain-focused applications in industrial process monitoring. The research design facilitates a detailed bibliometric analysis and delivers insights into the intellectual structure of blockchain technologyâs application in industry via scientometric approaches. The work also approaches numerous sources in various industrial sectors to identify the transformative role of blockchain in industrial processes. Aspects such as blockchain technologyâs impact on industrial processesâ transparency are discussed, while the paper does not ignore that success stories in applying blockchain to industrial sectors are often exaggerated due to a highly competitive environment that the cryptocurrency domain has become. Finally, the work presents major research avenues and decision-making areas that should be tackled to maximize the disruptive potential of blockchain and create a secure, transparent, and inclusive future.
Elie Bouri, Mahdi Ghaemi Asl, Sahar Darehshiri, David Gabauer
Abstract This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.
Abstract Research purpose. This study analysed the three cryptocurrencies with the largest market capitalization: Bitcoin, Ether (cryptocurrency built upon the Ethereum project's blockchain technology), and Binance coin, which account for 60% of the total cryptocurrency market capitalization. The purpose of this research was to measure the impact of monetary policy on the price of these cryptocurrencies using an adjusted R squared. Design / Methodology / Approach. As dependent variables, we used interest rates controlled by the European Central Bank and the Federal Reserve and reports from the European Central Bank and the Federal Open Market Committee. A robust Elastic Net Regression with Autoregressive Integrated Moving Average (ARIMA) residuals machine learning approach was applied to obtain robust regression coefficients and corresponding standard errors. To ascertain the robustness of the model, a technique known as rolling window cross-validation was employed. Findings. The results of this study show that monetary policy decisions and announcements significantly impact the price of cryptocurrencies. The impact on cryptocurrencies is likely to be significant both in the period of economic stability (2018-2020) and in the period of economic shocks (2020-2022). This relationship is likely to be indirect, acting through investor sentiment. Originality / Value / Practical implications. The results of this study may be useful to monetary policymakers, as they reveal the link between their actions and the price of cryptocurrencies. Our model will also be useful for mutual fund managers and private investors, as they can anticipate the price dynamics of cryptocurrencies when assessing monetary policy frameworks.
Purpose This paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the RussiaâUkraine conflict and the Silicon Valley Bank (SVB) collapse. Design/methodology/approach The authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the RussianâUkrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our resultsâ robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities. Findings The results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run. Originality/value This paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the RussiaâUkraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets.
Ijaz Younis, Himani Gupta, Anna Min Du, Waheed Ullah Shah · 5 authors
Decentralized finance (DeFi) has become of significant interest for investors in both the financial and digital sectors. We use a time-varying parameter vector autoregression (TVP-VAR) approach to estimate the static and dynamic connections between and within DeFi, G7 banking, and equity markets. We focus on critical events such as the COVID-19 pandemic, the cryptocurrency bubble, and the Russia-Ukraine conflict. The results highlight interconnectedness and significant spillovers within and between the markets, especially during the COVID-19 pandemic. Notably, there were significant spillover effects from the G7 banking and equity markets to Japan and DeFi assets. The findings demonstrate a robust connection between DeFi platforms, G7 banking, and stock markets throughout these tumultuous periods. Policymakers, investors, and entrepreneurs are recommended to keep a close eye on changes in traditional banking and equity markets to adjust the risk of DeFi assets.