The environmental footprint of cryptocurrency networks, particularly the electricity-intensive Bitcoin (BTC) blockchain, has raised growing concern among policymakers, investors, and environmental organizations. This study examines how major global environmental events and climate policy announcements influence Bitcoin’s return and risk dynamics, linking digital asset markets to sustainability debates. Thirteen events between 2010 and 2024—including multilateral agreements (e.g., the Paris Agreement), COP summits, extreme weather disasters, and national policy interventions—are analyzed using an event study framework integrated with the Capital Asset Pricing Model (CAPM) and GARCH-based volatility modelling. We hypothesize that highly visible policy events generate stronger short-run abnormal returns than climate disasters, while disasters produce more persistent effects on volatility. Results confirm this distinction: events such as the U.S. Paris Agreement withdrawal triggered immediate and significant reactions, whereas major weather disasters induced longer-term volatility adjustments. While overall systematic risk remained stable, event-specific responses revealed shifts in Bitcoin’s sensitivity to global equity markets. Climate-related signals shape speculative digital asset markets, with implications for sustainable finance, climate risk assessment, and regulatory policy design. Climate-related news can shape investor perceptions of energy-intensive digital assets, with implications for environmental policy design, sustainable finance strategies, and climate risk assessment. For policymakers, the results highlight the potential of environmental signals to influence speculative markets, supporting the case for integrating financial market behaviour into environmental management and regulatory planning.
Ala Alrawajfi, Mohd Tahir Ismail, Sadam Al Wadi, Saleh Atiewi
Ethereum and other cryptocurrencies are volatile, making Ethereum-USD rate evaluation difficult.Due to unsuccessful data collection and exchange downtimes, financial time series data are incomplete and lacking critical values.Thus, assessments may be incomplete, and trends may be miscalculated.This research builds and tests an ARIMA-random forest data imputation method to overcome these concerns.This innovative strategy uses AutoRegressive Integrated Moving Average (ARIMA) to describe the linear chronologic sequence relationship and random forest to solve nonlinearity.The suggested method uses ARIMA to handle the linear time-dependent data feature and random forest to reduce estimation errors to improve Ethereum-USD closing price estimates.The mean absolute error (MAE) and mean absolute percentage error (MAPE) results demonstrate that the proposed hybrid model significantly outperforms conventional imputation approaches across all missing data levels (10%-50%).For example, at 30% missing data, the hybrid model achieved an MAE of 0.91 and a MAPE of 0.00074, compared to ARIMA's MAE of 2.21 (MAPE 0.00185) and Random Forest's MAE of 2.34 (MAPE 0.00186).Across all scenarios, the hybrid model reduced MAE by up to 60% and MAPE by over 55% relative to the best single-method baseline, indicating superior robustness and accuracy in handling incomplete Ethereum-USD datasets.By providing precise market and result knowledge, these insights help financial analysts, traders, and researchers make accurate, efficient decisions.
This research investigates the dynamics of connectedness among cryptocurrency and various risk factors, including oil price demand and supply shocks, EPU, GPR, and the ADS business conditions index using the quantile time-frequency connectedness approach. The findings reveal that cryptocurrency behaves as a net receiver of shocks in the short term but transitions to a net transmitter over the long term. Critical sources of both short- and long-term shocks are attributed to oil price demand, supply fluctuations, and GPR. However, during extreme events like the COVID−19 pandemic and the Russia-Ukraine war, cryptocurrency, oil shocks, and other indices alternately become net transmitters and receivers of shocks depending on time frames and quantile ranges. During periods of heightened market uncertainty, monitoring the interconnected behavior of these variables is critical for investors and policymakers aiming to predict market shifts and manage risks effectively.
This study examines the intricate relationships between cryptocurrency and various uncertainties related to economic policy and global risk factors. It explores the interactions between cryptocurrency and global risk factors, comparing these with their relationships to different measures of economic policy uncertainty (EPU). We find that cryptocurrency returns are more sensitive to global risk factors than to the country-level EPU. Notably, gold exhibits bidirectional causality with cryptocurrency in returns and volatility. The research sheds light on the dynamic interactions within cryptocurrency markets, underscoring the importance of continuous monitoring and adaptive strategies to navigate the evolving financial landscape of the digital ecosystem.
This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables.
Advanced blockchain technologies and growing environmental and economic uncertainties have Motivated us to investigate the impact of climate policy uncertainty (CPU) and global economic policy uncertainty (GEPU) on five green cryptocurrencies—ADA, EOS, IOTA, XLM, XTZ—selected based on energy efficiency and mining processes. We examined the short- and long-run impacts of alternative assets on these cryptocurrencies using a nonlinear autoregressive distributed lag model. In the long run, these cryptocurrencies are negatively affected by CPU and GEPU, questioning their safe-haven potential. In the short run, ADA, EOS, and XLM share a positive asymmetric relationship with CPU, whereas all cryptocurrencies have a negative asymmetric relationship with GEPU. Therefore, they can be considered a safe haven. In the short and long term, green bonds exert a positive impact, whereas interest rates, the S&P 500, and the gold index negatively impact these cryptocurrencies. In the short run, Bitcoin shows a negative relationship with EOS, IOTA, and XTZ and a positive relationship with ADA and XLM. Over the long term, Bitcoin exhibits a positive correlation with all cryptocurrencies. USD exhibits a positive relationship in the short run and a negative relationship in the long run with all cryptocurrencies. The findings offer practical implications for portfolio construction and investors dealing in the green cryptocurrency market.
Éder Johnson de Area Leão Pereira, Thanmillys Nadhynne de Lima da Conceição, Emanuel Cruz Lima
The urgent need to mitigate climate change has elevated green hydrogen as a sustainable alternative to fossil fuels, while green cryptocurrencies have emerged to address the environmental concerns of traditional cryptocurrency mining. This study investigates the dynamic correlation between the green hydrogen market and selected green cryptocurrencies (Cardano, Stellar, Hedera, Algorand, and Chia) from July 2021 to April 2024, utilizing the Dynamic Conditional Correlation GARCH (DCC-GARCH) model with robustness checks using EGARCH and GJR-GARCH specifications. Our findings reveal significant correlations, with peaks reaching up to 50% in 2022, a period likely influenced by the Russia-Ukraine conflict. Subsequently, a decline in these correlations was observed in 2023. These results underscore the interconnectedness of sustainability-driven markets, suggesting potential contagion effects during periods of global instability. The high persistence of correlation shocks (α + β values approaching unity) indicates that correlation regimes tend to be long- lasting, with important implications for portfolio diversification and risk management strategies. Robustness checks using EGARCH and GJR-GARCH specifications confirmed qualitatively similar patterns, reinforcing the validity of our findings into the evolving landscape of green finance and energy.
This paper investigates the relationship between cryptocurrencies and other financial assets, with a particular focus on the dynamics of information flow between developed and emerging markets. To achieve this objective, the study applies a combined methodology of spillover index analysis and network topology based on graph theory. The analysis covers key cryptocurrencies (Bitcoin and Ethereum), stocks, and conventional currencies over the period November 2017 to September 2022, and distinguishes between short-term and long-run dynamics. The empirical findings show that in the short run, Bitcoin and Ethereum predominantly act as net shock transmitters, whereas in the long run, stocks and conventional currencies, together with Bitcoin and Ethereum, become the principal conveyors of spillover shocks. The network topology analysis corroborates these results by revealing the centrality of these assets in the spillover structure. By integrating spillover and network approaches across different markets and time horizons, this study contributes to the literature by providing a more nuanced understanding of how cryptocurrencies interact with traditional financial assets under varying market conditions.
This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.
Type of the article: Research ArticleAbstractThe rise of decentralized finance (DeFi) presents new opportunities for accessing modern financial services. Despite their transformative architecture, most DeFi applications are currently unregulated, which exposes market participants to unforeseen risks. Therefore, understanding the level of connectedness between DeFi and traditional finance (TradFi) is crucial, particularly in emerging Asian markets where the level of cryptocurrency acceptance is high. Applying the time-varying parameter vector autoregressive model, this study examines the return connectedness between leading DeFi assets and traditional financial sectors in Indonesia, India, and Vietnam – the top three countries in Asia for cryptocurrency adoption. By analyzing TradFi at the industry level, this study captures sector-specific spillover dynamics that are essential to the monitoring of systemwide risk. The empirical results reveal low, time-varying return spillovers between DeFi and traditional financial sectors in the selected emerging Asian markets. The emerging financial sectors exhibit stronger linkages with broader traditional market indicators than with DeFi, in which assets interact primarily with each other. Emerging financial sectors and gold are the recipients of return spillovers, and DeFi assets act as the return transmitters. The current low degree of integration between DeFi and TradFi offers policymakers a window of opportunity to develop a robust financial regulatory framework that addresses issues of market stability and consumer protection while promoting the advancement of financial innovation.AcknowledgmentsWe thank the editors and anonymous reviewers for their valuable and constructive feedback, which has contributed significantly to improving the quality of this manuscript.
Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios.
This paper analyzes the time-varying herding behavior in the non-fungible token (NFTs) and cryptocurrency markets and investigates their interrelationship. Using the daily market data from January 1st, 2020 to April 30th, 2023, our study covers the period characterized by Covid and post-Covid-19 induced global financial market volatility, capturing the dynamics in the global macroeconomic system and the Federal Reserve’s interest rate policy. Based on the rolling window method, our findings show the presence of herding behavior in both markets, where herding behavior in these markets may be influenced by the major events announcements particularly those related to the Federal Reserve's interest rate policy. Vector error correction model (VECM) indicates that the NFT market impacts the price of Ethereum, thereby influencing the broader cryptocurrency market. Such finding contributes to a deeper understanding of the market dynamics. By examining herding behavior, our findings indicate that the NFT market demonstrates relative independence from the volatile prices of the cryptocurrency market, suggesting the potential diversification benefits of incorporating NFTs for investors’ portfolio construction and risk management.
• Wavelet coherence reveals Bitcoin’s persistent link with climate policy uncertainty • Green cryptos show context-dependent coherence with climate policy uncertainty • Partial decoupling of green cryptos from Bitcoin emerges at medium-term scales • Twofold framework uncovers time-scale responses of crypto to policy uncertainty • Emphasizes need for stable climate regulations to curb crypto market volatility As global climate policy uncertainty (CPU) intensifies, understanding its intersection with emerging financial technologies becomes increasingly urgent. This study, therefore, investigates the dynamic relationship between CPU and the cryptocurrency market, focusing on Bitcoin and eight leading green cryptocurrencies (Algorand, Cardano, EOS, Hedera, IOTA, Nano, Stellar, and Tezos) using a wavelet coherence analysis. Specifically, the study employs a twofold framework: first, assessing the responsiveness of Bitcoin and green cryptocurrencies to climate policy uncertainty across time scales; second, examining Bitcoin's interaction with green cryptocurrencies to determine their potential stabilizing or decoupling effects amid regulatory uncertainty. The analysis spans from November 2017 to March 2025, capturing multiple phases of regulatory evolution and market transformation. The findings reveal that Bitcoin exhibits a structurally embedded and persistent coherence with CPU, especially over longer investment horizons. This persistent linkage highlights Bitcoin’s role in exacerbating regulatory volatility due to its significant environmental footprint. Conversely, green cryptocurrencies demonstrate more sporadic and context-dependent coherence, often aligning with major climate policy announcements or periods of regulatory scrutiny. While positioned as sustainable alternatives, these assets remain influenced by Bitcoin’s dominance and broader market sentiment, particularly at medium-term investment scales. The partial synchronization observed across key periods suggests an incomplete decoupling from both CPU and Bitcoin. These results highlight the importance of clear and stable climate regulations to reduce market uncertainty and support innovation in sustainable blockchain technologies.
María de la O González, Francisco Jareño, María Caridad Sevillano
Purpose This study aims to examine how cryptocurrency returns – specifically Bitcoin, Cardano and Tether – respond to unexpected shocks in inflation and interest rates and assess their potential as hedge, safe-haven or diversifier assets against them, comparing them to gold, the traditional safe-haven asset. Design/methodology/approach The research spans two sub-periods (2019–2021 with stable interest rates and 2022–2024 with rising rates) and uses quantile regression to capture the distribution of returns across market conditions. Findings The main findings of this study reveal that, first, Tether shows a consistently negative and statistically significant relationship with both nominal and real interest rates during bull markets, evidencing Tether’s role as a hedge asset against interest rates. Second, Tether together with Cardano throughout the full period and the second sub-period of interest rate hikes, as well as with Bitcoin during the first sub-period could be taken into account by investors to diversify nominal interest rate risk. Third, gold consistently shows a positive and statistically significant relationship with shocks in inflation expectations during economic recessions, suggesting its role as a hedge or even a safe-haven against inflation. Fourth, Bitcoin emerges as a potential safe-haven against inflation in the second subperiod, characterised by an upward trajectory in interest rates and driven in part by inflationary pressures arising from the Russia–Ukraine conflict. Research limitations/implications Future research could explore the impact of government regulation on the adoption and performance of cryptocurrencies, as well as the relationship between cryptocurrencies and other financial markets. Investigating the behavioural aspects of cryptocurrency investors, the environmental impact of green cryptocurrencies and the adoption of cryptocurrencies in emerging markets are also promising areas of research. Practical implications The results underscore the diverse responses of cryptocurrencies to macroeconomic factors, highlighting their role as a portfolio diversifier, hedge or safe-haven asset and suggesting further research into regulatory implications. Therefore, our findings have significant economic implications, particularly for portfolio management and investment strategies. The study shows that including a mix of traditional, green and stable cryptocurrencies can improve portfolio diversification and mitigate risks associated with interest rate and inflation fluctuations. Social implications This research can provide valuable insights for investors and policymakers, helping them to better understand and manage cryptocurrency investments. Policymakers can use our findings to develop regulations that support the adoption of cryptocurrencies while mitigating legal and operational risks. For example, understanding the different roles of different types of cryptocurrencies in hedging against economic variables can inform regulatory decisions that promote financial stability and protect investors. In addition, our study highlights the importance of educating retail investors on the benefits of diversifying their holdings with a mix of cryptocurrencies and traditional assets such as gold. Financial analysts and market participants can use these insights to provide better market analysis and educational resources, helping investors make informed decisions and fostering a more resilient financial ecosystem. Originality/value For market participants, the study highlights the importance of including a mix of traditional, green and stable cryptocurrencies to improve portfolio diversification, especially in times of economic uncertainty. Portfolio managers can use cryptocurrencies such as Tether and gold to hedge against interest rate and inflation risks, respectively, while retail investors should be educated on diversifying their holdings by combining different sorts of cryptocurrencies and gold. Even Bitcoin is emerging as a safe-haven against inflation in times of rising interest rates and inflationary pressures. Institutional investors can develop strategic asset allocation models that include cryptocurrencies and ensure regulatory compliance to mitigate legal and operational risks. Policymakers should create clear regulatory frameworks that balance innovation with investor protection, and financial analysts can provide market analysis and educational resources to help investors make informed decisions.
This study aims to understand the relationship among cryptocurrency, stock, and gold markets. Cointegration, structured VAR, and causality tests were used with daily datasets from 11/09/2017 to 11/17/2023. A cryptocurrency basket is accepted as the cryptocurrency market for this study. The stock markets have a one-way relationship both with the gold and cryptocurrency markets in the short-run. All markets have effects on other markets’ price variances, as well. The price shocks of the markets to each other are not so essential for the prices. However, their own price shocks impact their prices for a few days. The stock market has asymmetric relationships with the gold and cryptocurrency markets. A 1.00 % rise in stock price causes declines in the gold and cryptocurrency prices by 2.35% and 2.42%, respectively. If the gold market or stock market is ignored, a 1.00% rise in gold prices causes a 0.69% rise in cryptocurrency prices, or a 1.00% rise in stock prices raises the cryptocurrency prices by 4.03%.
Cryptocurrency investment is a rapidly growing financial sector, marked by high volatility, decentralized technologies, and significant profit potential. Investors use strategies like long-term holding (“HODLing”), portfolio diversification, and short-term trading. “HODLing” relies on long-term value appreciation but requires resilience to price fluctuations. Diversifying with assets like Bitcoin and Ethereum reduces risk due to their low correlation with traditional investments. The crypto market is highly sensitive to geopolitical, economic, and technological factors, attracting investors during economic instability. Advanced models like LASSO and AutoEncoder aid in price prediction and strategy optimization. Despite high return potential, careful risk management is essential due to volatility and regulatory uncertainty. This study experimentally applies identical cryptocurrency portfolios to different investment strategies, identifying the most profitable approach.