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.
Taha Zaghdoudi, Kais Tissaoui, M. Maâloul, Younés Bahou · 5 authors
This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin’s 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 bitcoin’s 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 bitcoin’s 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 bitcoin’s 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 “blockchain” and “economy”. 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 2012–April 2024 for GEPU,VIX, and Bitcoin and April 2016–April 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 Yilmaz’s (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík’ (J Financ Econ 16:271–296, 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 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.
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 technology’s 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 technology’s impact on industrial processes’ 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.
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.
This paper explores the transformative impact of artificial intelligence (AI), blockchain technology, and big data analytics on the sustainable finance sector. These technologies are driving significant advancements in decision-making, regulatory compliance, socially responsible investing (SRI), transparency, efficiency, risk management, financial inclusion, and the identification of sustainable growth opportunities. AI enhances predictive analysis and automates ESG compliance, fostering informed investment strategies and ensuring adherence to sustainability standards. Blockchain introduces unprecedented transparency and efficiency, particularly through smart contracts and decentralized finance (DeFi), facilitating direct funding of sustainable projects and transparent carbon credit trading. Big data analytics empower financial institutions with predictive risk management models and insights for enhancing financial inclusion and identifying sustainable investment opportunities. Through detailed examination, this study underscores how these technologies collectively support the alignment of financial investments with sustainability goals, contributing to the development of a sustainable global economy. This confluence not only streamlines operational processes and compliance but also opens new avenues for sustainable growth and investment, underpinning the financial sector's role in achieving a more sustainable and inclusive future.
Bu çalışmanın temel amacı küresel ekonomi politika belirsizliği (GEPU) endeksinin bitcoin üzerindeki etkisini incelemektir. Değişkenler arasındaki ilişkiyi ortaya koymak için Ağustos 2010 – Mart 2023 dönemine ait veriler kullanılmıştır. Küresel ekonomi politika belirsizliği (GEPU) endeksi ile bitcoin arasındaki ilişkiyi açıklamak için normal dağılmama durumunu dikkate alan RALS eşbütünleşme testleri kullanılmıştır. Değişkenlerin I(1) düzeyinde durağanlaştığı saptanmış, daha sonra RALS-ADL ve RALS-EG2 testleri uygulanmıştır. RALS-ADL ve RALS-EG2 eşbütünleşme testleri sonuçlarına göre GEPU endeksi ile bitcoin arasında eşbütünleşme ilişki olduğu tespit edilmiştir. Modelin uzun dönem katsayısına göre GEPU endeksindeki yüzde bir birimlik artış bitcoini 0.092 oranında artırdığı saptanmıştır.
This article primally explores the short-term fluctuation and long-term implications of the international Bitcoin price (BP) on the Chinese green bond (GB) market, within the sample period of 2014:M10–2023:M07. Bitcoin is the most important cryptocurrency and has a carbon-intensive feature, and its price suffers from great volatility and is closely related to the green finance market. Meanwhile, although China is the largest bitcoin mining state, it is pursuing a dual carbon target, which promotes its green bond market’s development. Thus, it is valuable to investigate the influence of BP on GBs in China. Based on the quantile autoregressive distributed lag approach, this paper indicates that the positive and negative impacts of BP on the GB market are significant in the long-term but not apparent in the short-term. These results emphasize the importance for market participants to obtain a better understanding of how BP affects GB under various market circumstances. Implementing specific policies, such as regulatory mechanisms for Bitcoin trade, market-oriented reform for the bond market, and information disclosure, can alleviate shocks from BP and accelerate the development of the GB market.
Abstract The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic.
In the era of globalisation, the use of technology and concerns for sustainability is eminent in the supply chain management practices. The current study focuses on sustainable and green supply practices in different stages of supply chain management and how they can be facilitated by blockchain technology (BT). The study has addressed the existing gaps in the area namely, the lack of research assessing stage-wise green supply chain for environmental performance focusing on BT. The current study aims to assess the impact of BT on different stages of the green supply chain and a firm's environmental performance. The study also focuses on analyzing the impact of green supply chain stages on environmental performance. The study uses PLS-based structural equation modelling approach to investigate the hypothesised relationships between BT adoption and stage-wise green supply chain practices. The data was collected from individuals from medium-sized enterprises from the manufacturing industry in India. The findings reveal a positive association between blockchain adoption and green supply chain management practices leading to enhancement in environmental performance. Furthermore, the study indicates a positive relationship between blockchain integration and different stages of the green supply chain, underscoring its multi-faceted impact on environmental performance. The findings imply that the BT adoption can facilitate the realization of sustainable supply chain practices and performance improvement.
This empirical research study aims to investigate the asymmetric spillovers among crypto and key financial assets such as gold, equity, bonds, and the dollar-to-ruble exchange rate volatility, focusing on new developments during the Russia-Ukraine conflict in 2022. Utilizing time- and frequency-domain methodologies, this study conducts an in-depth analysis employing daily frequency data from January 01, 2018, to May 30, 2023. The study employs value at risk and conditional value at risk estimations to assess potential losses in the portfolio during the crisis. The findings reveal that Bitcoin exhibits hedging ability, enabling investors to diversify risk among the underlying financial assets. The study observes a significant increase in Bitcoin investments during the crisis, leading to heightened volatility and uncertainty. Negative news has a stronger impact compared to positive news, underscoring the importance of prudent asset allocation for risk mitigation. The implications of our findings are particularly significant for financial policymakers and trade partners of Russia. The study urges them to differentiate their short- and long-term strategies and procurement contracts. In the long run, policymakers should be cognizant of the influence of the riskiness of crypto assets during economic crises, guiding the formulation of prudent policies and investment decision-making initiatives.
Thobekile Qabhobho, Cwayita Mpuku, Izunna Anyikwa, Andrew Phiri
since the onset of the cOViD-19 pandemic, african currencies, cryptocurrencies, and commodity markets have undergone significant fluctuations, displaying fat-tail properties that lies at the outer ends of the normal probability curve.the recent Russia-Ukraine war has further disrupted these markets, generating considerable interest among academics and practitioners.Our study delves into tail-end returns and volatility connectedness between Bitcoin, crude oil, gold, and four african currencies amidst the cOViD-19 and Russia-Ukraine war.employing a quantile vector autoregressive (QVaR) approach, we analyze tail-end spillover effects between markets from 4 november 2019, to 7 september 2022.Our findings reveal heightened connectedness at the quantile ends of co-movements, with left-tail spillovers being more pronounced for returns, while right-tail spillovers dominate for volatility.Bitcoin, and to a lesser extent gold and oil, emerge as effective tail-ended hedges for the egyptian Pound and nigerian naira but not for other african currencies like the algerian Dinar and south african Rand.consequently, users of egyptian and nigerian currencies in international financial markets can seek hedging opportunities in traditional cryptocurrencies and commodities during recent Black swan events, unlike those using south african and algerian currencies.additionally, our results suggest limited diversification benefits associated with (i) currencies linked to oil-exporting or oil-importing countries, (ii) currencies linked to shariah-compliant financial systems, but do indicate diversification benefits in high-inflation environments.these findings hold relevance for investors seeking improved hedging strategies against african currency risk and for african policymakers aiming to enhance intra-continental trade, foreign direct investment, and cross-border business expansions.
Finding effective carbon reduction pathways is an important breakthrough in combating climate warming. In recent years, green financial policies have been recognized as important institutional initiatives globally. In China, the impact of green financial policies on carbon emission reduction and their paths remains to be explored. On the basis of panel data from 30 provinces in China from 2012 to 2018, this paper examines the carbon emission reduction effect of China’s green finance reform and innovation pilot policy (GFRIPP) from the perspective of financial decentralization. China’s GFRIPP has a significant carbon emission reduction effect. Unlike the provinces that did not implement the GFRIPP, the provinces that implemented the GFRIPP experienced a 0.053 reduction in their carbon emission intensity growth rate, and financial decentralization weakened the carbon emission reduction effect of the policy. The impact of GFRIPP on carbon emissions also has significant regional heterogeneity. The carbon emission reduction effect of GFRIPP in the central and western regions is more significant than in the eastern region and is more vulnerable to the weakening impact of financial decentralization. On the basis of the above conclusions, the role of green finance in promoting carbon emission reduction should be given more attention, the communication between central and local policies should be strengthened, a scientific green financial system should be formulated and implemented, and carbon neutrality must be achieved.
Since the onset of the COVID-19 pandemic, leading cryptocurrencies have undergone significant price fluctuations, prompting widespread interest in the interdependence and spillover effects among cryptocurrency markets, as well as in identifying the key cryptocurrencies that drive market movements. This study contributes to the existing literature by utilising innovative vector wavelet coherence (VWC) and wavelet local multiple correlation (WLMC) frameworks to investigate the time-frequency co-movements among four cryptocurrencies (Bitcoin, Ethereum, Tether, and Binance). By exploring the co-movements across multiple time scales over a period from 01/01/2020 to 10/04/2023 through continuous and discrete wavelet coherency analysis, we identify four key empirical findings. Firstly, the returns connectedness is stronger than the volatility connectedness. Secondly, high-frequency co-movements are more erratic and correspond to positive and negative unexpected news, while low-frequency co-movements vary with changes in US monetary policy. Thirdly, Tether and Binance exhibit the weakest returns and volatility connectedness with other cryptocurrencies. Lastly, Ethereum and Tether (not Bitcoin) are the primary cryptocurrencies that account for returns and volatility movements in the market. We discuss the implications of these findings for various stakeholders in cryptocurrency markets.
Oktay Özkan, Salah Abosedra, Arshian Sharif, Andrew Adewale Alola
Abstract The objective of this paper is to assess the dynamic volatility connectedness between fossil energy, clean energy, and major assets i.e., Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500 from September 17, 2014 to October 11, 2022. The main motivation of the study relates to examining the dynamic volatility connectedness mentioned during periods of important events such as the recent coronavirus pandemic and the Russia–Ukraine conflict which has shown the vulnerability of economic and financial assets, energy commodities, and clean energy. The novel Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) approach is employed for the investigation of the sample period mentioned. Empirical analysis reveals that both the total and net volatility connectedness between assets is time-varying. The highest connectedness among the assets is observed with the onset of the coronavirus (COVID-19) pandemic, and it increases with some important international events, such as the Russia–Ukraine conflict, the referendum of Brexit, China–US trade war, and Brexit day. On average, the result shows that 32.8% of the volatility in one asset spills over to all other assets. The DCC-GARCH results also indicate that crude oil, bonds, and Bitcoin act as almost pure volatility transmitters, whereas the Dollar index, gold, and S&P500 act as volatility receivers. On the other hand, clean energy is found neutral to external shocks until the first quarter of 2020 and after that time, it starts to behave as a volatility transmitter. Based on the obtained results, we offer some specific policy implications that are beneficial to the US economy and other countries. Graphical Abstract Dynamic volatility connectedness between fossil energy, clean energy, and major assets (Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500)
Abstract We analyze the connectedness between major cryptocurrencies and nonfungible tokens (NFTs) for different quantiles employing a time-varying parameter vector autoregression approach. We find that lower and upper quantile spillovers are higher than those at the median, meaning that connectedness augments at extremes. For normal, bearish, and bullish markets, Bitcoin Cash, Bitcoin, Ethereum, and Litecoin consistently remain net transmitters, while NFTs receive innovations. However, spillover topology at both extremes becomes simpler—from cryptocurrencies to NFTs. We find no markets useful for mitigating BTC risks, whereas BTC is capable of reducing the risk of other digital assets, which is a valuable insight for market players and investors.
Green finance is becoming more and more important as a way to fund environmentally friendly initiatives and lower carbon emissions. Green bonds have emerged as a significant financing tool in this context, and it is critical to understand how they interact with other components of the finance ecosystem, such as cryptocurrency and carbon markets, particularly during recent crises such as the COVID-19 outbreak and the Ukraine invasion. This study aims to empirically investigate the lead-lag associations between major cryptocurrency markets and green finance measured in terms of green bonds. For empirical estimation, the wavelet analysis and spectral Granger-causality test are employed to analyze the daily data, covering the period from 2018 to 2023. The results show that the correlation between the returns of the green bond market and cryptocurrencies is not stable over time, which rises from the short- to long-run horizon. However, the co-movements between these assets tend to be different and, in some cases, strong, especially during recent crises. Furthermore, the Granger causality test demonstrates the existence of a bi-directional causality between the prices of the cryptocurrencies and green bonds. These findings have significance for portfolio managers, investors, and researchers interested in investing strategies and portfolio allocation, suggesting that green markets may be used as a hedge and diversification tool for cryptocurrencies in the future.
This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, the Fourier Granger Causality, and the Fourier Toda–Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.