This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributionsânormal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gammaâwere considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volumeâvolatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management.
This article presents a comprehensive analysis of the impact of cryptocurrencies on the economic and environmental security of the G7 countries, exploring both the potential risks and prospects. The study focuses on the United States, Canada, the United Kingdom, France, Germany, Italy, and Japan, offering a detailed exploration of the increasing adoption of cryptocurrencies in these nations. Despite the benefits such as enhanced financial inclusion and cross-border transaction efficiency, cryptocurrencies pose significant challenges, including their use in illicit activities like money laundering and terrorism financing. The research critically examines the substantial energy consumption associated with certain cryptocurrency mining processes, particularly Proof-of-Work mechanisms, and their consequent environmental impacts, including carbon emissions, electronic waste, and air pollution. It investigates the corresponding energy policies and regulatory responses emerging within the G7 to address these concerns, alongside the development of more energy-efficient alternatives like Proof-of-Stake and the push for renewable energy in mining. The article critically examines these dual aspects, highlighting the measures implemented by regulators and policymakers to mitigate risks. It also delves into the evolving landscape of Central Bank Digital Currencies (CBDCs) and their potential role in enhancing financial system efficiency and security, including considerations for their energy footprint. The study employs a robust methodological framework, combining statistical analysis of market trends, case studies, and policy analysis to provide a balanced view of the current state and future trajectory of cryptocurrencies in the G7 countries. By offering a nuanced understanding of both the opportunities and threats posed by digital currencies, including their energy and environmental dimensions, this article contributes to the ongoing discourse on their integration into global financial systems and their implications for sustainable economic security.
Among the plethora of literature on interlinkages in markets, more focus has been on peripheral factors. This study attempts to fill this gap by exploring volatility as driver for interlinkages between Bitcoin, Ethereum, Tether, USD-Coin, Binance Coin (BNB), and the crypto-volatility-index (CVI) from April 2019 to August 2022. Using various wavelet techniques, the study depicts significant interlinkages across short-term, medium-term, and long-term horizons, with relatively stronger interlinkages in the long term. The findings confirm that while CVI does not drive these interlinkages, Ethereum, Bitcoin, and CVI play dominant roles in the short, interim, and medium-term periods, respectively, offering new insights into the dynamism of cryptocurrency markets.
This study delves into the differences between traditional financial markets, as proxied by their corresponding future contracts, and the cryptocurrency market, focusing on Bitcoin, during major global events: the COVID-19 pandemic, the Russia-Ukraine war, and the IsraelâPalestine conflict. It reveals Bitcoinâs increased trading volume post-COVID-19, highlighting its appeal as a digital safe haven. This trend persists during subsequent crises, suggesting a strategic shift towards cryptocurrencies as diversification tools. Despite volume fluctuations, Bitcoinâs price stability reflects investor confidence in its long-term viability. The significant change in EuroStoxx 50 returns during the IsraelâPalestine conflict, highlights localized geopolitical influences on markets. The study underscores the importance of considering both global and regional factors in investment decisions. It emphasizes cryptocurrenciesâ growing significance in the global financial market, particularly during crises, and suggests further exploration into investor behavior and regulatory effects. Understanding these dynamics is crucial for navigating the evolving financial landscape.
ABSTRACT This study aims to conduct an inâdepth analysis of the complex nonlinear dependence relationships between cryptocurrencies and gold within the stocks of BRICS countries. The study employs a GARCHâEVTâVineâCopula and wavelet coherence models to evaluate the interconnectedness, tail risk and Coâmovement pattern of these assets before and after the outbreak of COVIDâ19. The findings reveal that, prior to COVIDâ19, significant tail dependence existed between China's stock market, the cryptocurrency index, and the indices of India and Russia, while other indices exhibited only weak dependence. However, after the outbreak of COVIDâ19, the tail dependence among variables became more pronounced. The South African stock market appears to have emerged as the center of extreme lowerâtail risk spillovers among the studied variables. During the COVIDâ19 outbreak, cryptocurrency markets demonstrated stronger coherence with global stock markets than gold, especially in the US market, potentially compromising their diversification effectiveness. Furthermore, our empirical results were validated by the Kupiec test and the Christoffersen test. The results of this study not only enhance the theoretical understanding of risk management in emerging markets during periods of extreme market crises but also provide valuable insights for policymakers in formulating strategies to ensure financial market stability.
This study employs novel quantile time-frequency connectedness approach to explore the dynamic connectedness among sustainable assets (sustainable, green bond, and clean energy index), traditional assets (traditional index and crude oil), and cryptocurrency. This method assesses the impact of uncertain events on asset relationships. Findings indicate median connectedness of 36.94% in the short run and 4.81% in the long run, with short-term dynamics dominating system transmission. The traditional index is the primary transmitter of short-run shocks, while the green bond index leads in long-run shocks. Diversification across asset classes is recommended for effective hedging and optimal returns during extreme market conditions.
Purpose : The purpose of our study was to examine the property of long memory in the mean and volatility of daily returns on two major cryptocurrencies â Bitcoin and Ethereum â over the period ranging from January 1, 2017, to December 1, 2017. The growing body of research on this relatively new asset class, cryptocurrencies, motivated us to conduct this study. Methodology : We used autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) models to study long-memory in both mean returns and volatility of Bitcoin and Ethereum. The study also employed BaiâPerron and QuandtâAndrews tests to identify structural breaks in both cryptocurrencies, allowing for a more detailed examination of the property of long-memory in sub-samples. Findings : Our results confirmed the absence of long memory in mean returns on Bitcoin for the whole sample period as well as both sub-sample periods, implying that its market is efficient. Mean returns on Ethereum exhibited long memory over the complete sample period; however, the sub-sample analysis revealed a shift toward market efficiency, as long memory was not present in the second sub-sample. The results from FIGARCH analysis confirmed the prevalence of long-memory in the volatility of returns on both Bitcoin and Ethereum. Implications : The mean returns for both cryptocurrencies did not show persistence in the second sub-sample period, which was characterized by heightened economic uncertainty. This implies that external events did not have predictive power over cryptocurrencies. However, the presence of long memory in volatility implied that past volatility levels affected present volatility levels in both cryptocurrencies. Therefore, investors and policymakers could use volatility predictions to assess riskiness in their portfolios and the cryptocurrency market to formulate regulations, respectively. Originality : The paper contributed to the rather sparse body of literature pertaining to properties of financial time series with respect to cryptocurrency. An important contribution of this paper is to provide a comprehensive study, accommodating a structural break, over a period that includes periods of low and high economic uncertainty.
ABSTRACT This study aims at bridging critical gaps in the existing cryptocurrency research by exploring combinations of technological, macroeconomic and behavioural factors, namely, economic agents' expectations and the size of influence that each of them has on the Bitcoin price movements. In contrast to the existing studies that focused on individual determinants and estimated aggregate effects thereof, in this study, fuzzyâset qualitative comparative analysis (fsQCA) is applied to determine configurations of drivers to determine the Bitcoin price and used necessary condition analysis (NCA) to quantify the magnitude of the effects using the monthly data between 2011 and 2022. Findings show that economic agents' expectations such as OECD's Business Confidence Index, Consumer Confidence Index and Composite Leading Indicator emerge as influential variables of Bitcoin, surpassing traditional drivers like Gold and Financial Stress Index. Among these, Business Confidence Index and Composite Leading Indicator exhibit a very large effect on Bitcoin prices, and from the technology variable group, Average Block Size exhibits a very large effect on Bitcoin prices. fsQCA indicates that nine distinct configurations contribute to high Bitcoin prices and eight configurations lead to low Bitcoin prices, thus depicting equifinality in Bitcoin price determination. These insights can provide policymakers and investors with a better understanding of the Bitcoin price dynamic by finding out necessary variables and equifinal pathways towards either high or low prices, thus promoting better risk management activities, as well as regulatory approaches to this highly dynamic asset class.
ABSTRACT In this article, we discuss how far Bitcoin has come since its formulation 16 years ago and explore promising areas for future research in finance. The future research topics fall into the following three broad areas: institutional and country adoption, criminality, and Bitcoin's relationship to stablecoins. After a brief discussion of the existing literature, we provide a list of open research questions for future research to explore.
Erdinç AkyÄąldÄąrÄąm, Ahmet Faruk Aysan, OÄuzhan Ăepni, Shaen Corbet
This study investigates the influence of news-based sentiment on the returns of Decentralized Finance (DeFi) coins using a sample of 27 coins from January 2017 to March 2022. Our results indicate that news sentiment significantly impacts DeFi returns, with negative sentiment exerting a stronger influence than positive sentiment. Transaction volume and network security also emerge as critical drivers of DeFi coin returns. Smaller coins are more sensitive to news sentiment, showing greater return volatility. The impact of news-based sentiment is more pronounced during weekdays, likely due to reduced participation by institutional investors and trading algorithms. These findings have important implications for investors and policymakers, suggesting multiple pathways for market manipulation under specific conditions. ⢠We investigate the relationship between DeFi coins and news-based sentiment. ⢠Negative sentiment has a greater impact on returns. ⢠Transaction volume and network security drive returns. ⢠Smaller DeFi coins are more susceptible to news sentiment and greater return volatility. ⢠DeFi returnsâ sensitivity to news-media sentiment is significantly elevated during weekdays.
We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we evaluate the fit of two competing models of market microstructure: the Mixture of Distributions Hypothesis (MDH) and the Intraday Trading Invariance Hypothesis (ITIH). Using intraday data at different levels of aggregation, we investigate the relationship between return volatility per transaction and trade size. We find evidence favoring the MDH in the crypto futures market.
This study explores the sustainability-enhancin g financial effects of blockchain on carbon-linked digital markets. Drawing on a panel dataset of daily transactions from leading tokenized carbon platforms between 2020 and 2023, the study applies a fixed-effects Difference-in-Differences (DiD) framework to assess how the introduction of blockchain-based infrastructure influences carbon asset prices and trading volumes. Our findings reveal that higher transaction costs, often viewed negatively, may actually signal trusted infrastructure in illiquid sustainability markets, boosting investor confidence. The results confirm that blockchain adoption improves pricing efficiency under specific liquidity conditions, while exhibiting limited short-term effects on volume. It offers new evidence on how blockchain can strengthen carbon markets; reduce transactional inefficiencies, and advance climate action and sustainable development goals (SDGs). These insights inform policymakers, regulators, and investors aiming to design resilient, efficient, and scalable digital carbon markets.
Jeffrey Chu, Stephen Chan, Yuanyuan Zhang, Nicholas Lord
This study examines the short-term impact of the Russia-Ukraine war on the high frequency digital<br/>asset markets. We apply an event study approach, focusing on the initial months of the war and<br/>analyse hourly returns of cryptocurrencies, DeFi tokens, and metaverse tokens. We find that<br/>negative war-related events have both an immediate and sustained impact on cryptocurrencies<br/>and DeFi tokens, likely due to a series of negative events leading to positive returns. In contrast to<br/>stocks and commodities like gold, cryptocurrencies and DeFi tokens exhibit positive and significant<br/>cumulative returns following negative war-related events. This suggests that these assets could<br/>serve as diversifiers or hedges against such events, similar to the âpolitical propertyâ observed for<br/>oil. Importantly, these findings provide preliminary insights into the ongoing Russia-Ukraine<br/>conflict and help to understand the impact of military conflict on cryptocurrency markets more<br/>broadly.
Remy Jonkam Oben, Mehdi Seraj, Ĺerife Zihni EyĂźpoÄlu
Purpose The evolution of financial technology has been rapid, culminating in the mainstream acceptance and adoption of blockchain technology over the past decade. By providing the foundational infrastructure on which smart contracts and decentralized applications can be built and operated, the Ethereum blockchain facilitated the emergence of decentralized finance (DeFi). Not only have DeFi instruments increased portfolio options for investors, but they also have the potential to influence volatility transmissions both in traditional financial markets and within the digital space. To better inform policymaking, risk management and portfolio construction, this study aims to investigate both the time-based and frequency-based volatility connectedness among four leading DeFi instruments and 12 traditional financial markets. Design/methodology/approach This study analyzes weekly price data ranging from October 05, 2020, to March 04, 2024. The study employs advanced econometric frameworks (DieboldâYilmaz and BarunĂkâKrehlĂk models) to estimate both the time-based and frequency-domain volatility connectedness among the studied financial instruments. Findings Empirical results show that the DeFi instruments are highly interconnected, the very-large financial markets are highly interconnected and there are low connections between DeFi instruments and traditional financial markets. Moreover, the larger (smaller) stock markets are net volatility transmitters (receivers). Overall, the volatility connectedness among all the studied instruments is moderate (48.4% on average), with the instruments being most (least) connected in the long (short) term. Originality/value This study expands the literature by including major DeFi assets that have been largely overlooked. Also, the study introduces novelty by incorporating global markets. In fact, to the best of the authorsâ knowledge, it is the first study to analyze both time- and frequency-based volatility connectedness among DeFi assets and global financial markets.
Silvia Edelweiss Crusco dos Santos, HĂŠlder SebastiĂŁo, Nuno Silva
Using daily data from November 9, 2017 to December 31, 2022, this paper uses Granger causality in the mean and the distribution to investigate the transmission of information between return, volume, volatility, and illiquidity for Bitcoin and the nine most important altcoins in terms of market capitalization. Additionally, the forecastability of Bitcoin returns is examined using linear models with different predictor spaces estimated using LASSO and the performance of several trading strategies devised upon those forecasts is assessed. The causal relationships between returns, volumes and volatilities of Bitcoin and each altcoin are more evident in the left tail of the distribution, where Bitcoin acts mostly as a transmitter of information, and in the right tail for causality regarding illiquidity. In bullish markets, Bitcoin acts mostly as a receiver of information. The best Bitcoin trading strategy is based on the model which incorporates the information on all cryptocurrencies, exhibiting a cumulative return of 331% and an annualized Sharpe ratio of 94.59%, considering an enter/exit threshold of 0.25% and after 0.5% round-trip transaction costs. These results are statistically significant when compared with the buy-and-hold strategy, which renders a cumulative return of 121% and a Sharpe ratio of 64.74%. These results point out the importance of considering information from other cryptocurrencies to forecast and trade on Bitcoin.
Remy Jonkam Oben, Mehdi Seraj, Ĺerife Zihni EyĂźpoÄlu
Purpose This study investigates volatility and returns spillovers among US technology stocks, decentralized finance (DeFi) tokens and conventional cryptocurrencies, while also examining strategies for optimal portfolio allocation. Design/methodology/approach The study analyses daily financial market data from October 05, 2020 to February 09, 2024 by employing the Diebold and Yilmaz (2012) and dynamic conditional correlations generalized auto-regressive conditional heteroscedasticity (DCC-GARCH) models. Findings Empirical findings showed that the US technology stocks were highly interconnected both in returns and volatilities (same as the crypto assets), while technology stock-crypto asset market connections were quite low. Moreover, the technology stocks (crypto assets) were generally net volatility and return receivers (transmitters). Overall, market connectedness was high (65.6% for volatility and 77.2% for return). Portfolio optimization results showed that technology stock-crypto asset (all-DeFi, all-cryptocurrency, all-technology stock and DeFi-cryptocurrency) portfolios were attractive to risk-averse (risk-neutral and risk-seeking) investors. Originality/value This is the first study to comprehensively analyze volatility and return connectedness and provide insights into portfolio optimization across traditional technology, DeFi and cryptocurrency markets. The insights from this study will aid in risk management, optimal portfolio diversification and formulation of regulations and policies to promote market stability.