This study investigates the global adoption of Bitcoin by analyzing its price elasticity of demand (PED) across 46 countries or regions, with a focus on the interplay between economic, regulatory, and technological factors. Utilizing robust econometric techniques, including Huber regression, the research identifies significant variations in Bitcoin demand elasticity between developed and developing economies. The findings reveal that developed economies exhibit a mix of elastic and inelastic demand, driven by market maturity and discretionary consumption, while developing economies predominantly demonstrate inelastic demand, reflecting necessity-driven adoption amidst economic constraints. Key determinants of adoption include regulatory frameworks, such as legality, taxation, and anti-money laundering measures, alongside technological readiness indicators like blockchain infrastructure and internet penetration. These results underscore the critical influence of non-price factors on Bitcoin’s adoption dynamics and provide valuable insights for policymakers, investors, and industry stakeholders aiming to balance innovation with market stability. By offering a nuanced understanding of Bitcoin demand, this research contributes to the broader discourse on cryptocurrency adoption and its socioeconomic implications.
Research background: In today’s digital age, traditional environmental, social, and governance (ESG) development paths are gradually facing challenges, including from digital technologies. In particular, the potential roles of artificial intelligence (AI), cloud computing (CC), and blockchain (BC) in the ESG market have not been fully explored. Purpose of the article: This study explores the deep integration of digital technology and ESG by evaluating the correlation and spillover effects among AI, CC, BC, and eight global ESG indices. Methods: This study explores the spillovers between AI, CC, BC, and eight global ESG indices by cross-quantilogram and quantile time-frequency connectedness approaches. Findings & value addition: The lower quantile of ESG returns has a weak positive (strong negative) correlation with the lower (upper) quantile of digital technology. Next, the spillover effects vary with time, frequency, and quantile levels. Meanwhile, the North America and Asia-Pacific developed ESG indices serve as the transmitter and receiver of spillover effects, respectively. Furthermore, the dependence between digital technology and ESG returns is insignificant before the COVID-19 crisis but increases after it. This quantile-dependent asymmetry fundamentally challenges linear assumptions prevalent in current ESG-technology integration theories. Overall, this study contributes by integrating AI, CC, BC, and ESG into a unified framework, and analyzing their interaction mechanisms. Furthermore, it dynamically analyzes the asymmetry over long and short-term horizons, and highlights the hedging role of digital technology in stabilizing ESG markets. Moreover, we provide novel insights about the interconnectedness between these markets, offering valuable guidance on risk management. Consequently, regulators should urgently explore the development of digital asset-based ESG derivatives as targeted risk mitigation tools. Positioned at the cutting-edge, this work sets a methodological benchmark for analyzing non-linear, frequency-sensitive interdependencies within the rapidly evolving ESG-digital nexus, transforming the theoretical framework from static linearities to dynamic non-linearities. Finally, this study proposes some reasonable suggestions, including raising risk awareness, promoting digital transformation, building integration and innovation platforms, and leveraging ESG’s diffusion role.
Cryptocurrencies are one of the new financial assets that might provide some hedge, safe havens and diversification benefits towards traditional financial assets.However, the impact of COVID-19 towards their properties was also acknowledged in the literature and showed that COVID-19 significantly changed their properties against other financial assets.However, the comparison of the co-movement for the cryptocurrency and financial assets in the three different periods (pre-COVID-19, during COVID-19, and post-COVID-19) is relatively limited.Therefore, this study aimed to study the differences in the co-movement between Bitcoin and stock indices in ASEAN-5 markets in these three periods.The study period spanned from early January 2018 until the end of June 2024, and the conditional correlation was obtained through the MGARCH-DCC approach.These conditional correlation series were then divided into three periods, and statistically compared their statistical differences using an independent t-test.The results found that the comovement between Bitcoin and market indices was significantly different between pre-COVID-19 and during COVID-19 in all ASEAN-5 markets.Besides, when comparing pre-COVID-19 and post-COVID-19, the result showed that the co-movement between Bitcoin and market indices in Malaysia and Thailand was significantly reduced, while significantly enhanced between Bitcoin and market indices in Indonesia and the Philippines markets.Moreover, the results further revealed the significant differences between the co-movement of Bitcoin and market indices in Malaysia, Singapore and Thailand markets.Some useful implications were obtained from the study's findings, and it is expected to be beneficial to the literature and also to stakeholders.
In this study, the fractal structure, efficiency, and long memory features of Bitcoin are investigated according to different investment horizons. The study utilized daily returns from 01.01.2017 to 22.11.2023, applying the maximum overlap discrete wavelet transform, Rescaled Range (R/S) analysis, and volatility models. The analysis results revealed a deviation of Bitcoin returns from the average and a negative correlation, indicating a lack of permanent behaviour in the series. The analysis demonstrates the rejection of the efficient market hypothesis and reveals a chaotic structure in the Bitcoin market. Furthermore, we observed a hyperbolic rate of decrease in returns at long-term investment horizons due to information shocks. This indicates that past returns can predict future returns. This suggests that instead of the efficient market hypothesis, the fractal market hypothesis is valid due to the existence of recurring trends. Finally, we determined the most appropriate volatility models for Bitcoin. The analysis shows that information shocks in Bitcoin returns at medium- and long-term investment horizons decrease over time, and past returns can predict future returns. However, volatility and information shocks are transitory at short- and medium-term investment horizons but can vary. All analysis methods yield consistent and compatible results, suggesting their potential extension to other cryptocurrency markets beyond the Bitcoin market.
As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China's energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China's energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China's energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market.
The primary focus of this study is to monitor significant changes compared to the author's previous articles, with the objective of identifying alterations in the legality of cryptocurrency; the extent of its volatility; its profitability; and its use as a medium of exchange. The author asserts that, in 2025, the legality of cryptocurrencies underwent significant changes on a global scale. The regulatory approach to digital assets varies across nations, with some adopting a regulatory framework that encompasses these assets, while others have opted for a prohibitionist stance. The profitability of mining has been observed to decrease in consequence of rising time and energy costs, whilst the volatility index has been noted to decrease due to the entry of institutional investors and the adoption of merchant strategies. In summary, the profitability of crypto asset acquisition has reached a state of maturity. The focus has shifted from the initial hype to the development of effective strategies, the optimal timing of transactions, and the conducting of thorough research.
Cui-Ping Wen, Kai‐Hua Wang, Chi‐Wei Su, Xin Li · 5 authors
This study examines the impacts of bitcoin price (BTP), crude oil price (COP), and economic policy uncertainty (EPU) on China’s green bonds (GBs) in a period from 2014: M10 to 2024: M04 using the quantile autoregressive distributed lag model. Results demonstrate that BTP and EPU positively and negatively affect GBs in the long-term across all quartiles, respectively, while COP enhibits insignificance. In the short-term, all variables positively affect GBs and are concentrated in the low quantiles. This study constructs a multivariate framework to explore financial linkages across markets and examines variable interactions, enriching the theoretical framework of the GB market.
Pedro Henrique Filgueiras dos Santos Oliveira, Daniel Muller Rezende, Saulo Moraes Villela, Heder S. Bernardino · 6 authors
One of the main events involving the world economy in 2022 was the beginning of the war between Russia and Ukraine. This event offers an opportunity to analyze how a large-magnitude world event can affect the use of cryptocurrencies. Ethereum is one of the most prominent and widely used cryptocurrency platforms and, as such, provides a valuable case study for this scenario. This work investigates the behavior of accounts and their transactions on the Ethereum network during this event. For this purpose, we collect all Ethereum transactions during two distinct periods: (i) during the month the conflict began, and (ii) during the previous year. We organized a dataset with the accounts involved in these transactions and the subset of these accounts that interacted with a service within Ethereum named Flashbots Auction. Flashbots Auction is crucial as it addresses issues regarding transaction ordering and miners exploiting that ordering to make profit. Then, we model temporal graphs in which each vertex represents an account, and each edge represents a transaction between two accounts. We analyzed the behavior of these accounts via graph metrics for both groups during each observed time window. The results show changes in account behavior and activity, as well as variations in daily transaction volume.
Susanna Levantesi, Gabriella Piscopo, Alba Roviello
Accurate estimation of cryptocurrency market volatility is crucial for investors. The Crypto Volatility Index (CVI) was developed to measure the market’s expectations for the 30-day implied volatility of Bitcoin and Ethereum to address the growing demand for reliable predictions. This study explores the relationship between the CVI and the volatility of traditional financial markets, including the Gold Volatility Index (GVZ), the Crude Oil Volatility Index (OVX), and the S&P500 Volatility Index (VIX). Three other variables are also analyzed: the USD to EUR exchange rate (USDEUR), the Federal Reserve interest rate (FED), and the NASDAQ index. The aim of the research is explanatory: the input variables and the CVI are observed contemporaneously to catch the complex relation between them. Using Pearson correlation, distance correlation, and mutual information, we demonstrate the presence of non-linear relationships between some variables in the dataset. Explanatory analysis is conducted using machine learning techniques, specifically the Random Forest (RF) algorithm and Gradient Boosting Machines (GBM) to account for these potential non-linear interactions. These methods are better suited than standard linear models for identifying complex relationships. In particular, the RF algorithm reaches a better level of accuracy than GBM and avoids overfitting.
This study examines the dynamic connectedness between Bitcoin and various financial assets, including the stock market, gold, oil, bonds, and exchange rates, as well as explores portfolio strategies involving these assets. The study covers the period from January 2, 2015, to March 1, 2024. The quantile connectedness approach and portfolio strategies are utilized in the analysis. The findings are as follows: Intermarket volatility spillover significantly increases under extreme conditions. Bitcoin emerges as a transmitter during bullish markets and acts as a receiver in bearish and normal market conditions. Gold serves as a receiver in extreme conditions and a transmitter in normal conditions. Unlike gold, oil acts as a transmitter under extreme conditions and functions as a receiver under normal conditions. Among the fundamental markets, the stock market is the most significant shock transmitter. In risk-mitigating portfolios, the proportion of Bitcoin is low, while the proportions of gold and the dollar index are high. Bitcoin has been found to have low hedging properties. <br />Implications for Central European Audience: Since the emergence of Bitcoin in 2008, the cryptocurrency market has developed rapidly. Bitcoin and cryptocurrencies have come to occupy an important place in financial markets in terms of value and volume. Bitcoin can affect portfolio management in the financial system in terms of diversification, hedging, risk management, portfolio strategies, and linkages between financial assets. This study investigates the linkages, hedging and portfolio strategies between Bitcoin and the stock market, gold, oil, bond and exchange rate markets. The results of the study are important for portfolio managers, risk managers, financial analysts and economic managers.
Bitcoin's return volatility from 2014 to 2022 reveals significant changes in response to political and macroeconomic developments, particularly during the 2016 and 2020 U.S. presidential elections. In 2016, Bitcoin exhibited modest price movement and low volatility, while in 2020, the asset experienced dramatic price increases and heightened volatility, reflecting increased market maturity and institutional interest. Political uncertainty, regulatory shifts, and market sentiment played crucial roles in shaping volatility dynamics during these periods. Using GARCH(1,1) and EGARCH(1,1) models, time-varying volatility patterns and asymmetric effects of market shocks are analyzed. GARCH results confirm volatility clustering and high persistence, whereas EGARCH captures leverage effects, showing that negative shocks influence volatility more than positive ones. Visualizations of conditional variance support these findings, indicating that Bitcoin reacts more intensely to adverse news, especially during politically turbulent periods. Residual diagnostics suggest model adequacy and enhance the reliability of insights. These results underscore Bitcoin's evolving role as a financial asset increasingly affected by global events and investor sentiment, offering valuable implications for market participants and policymakers monitoring risk in cryptocurrency markets.
This paper proposes a comprehensive deep-learning framework, SentiStack, for Bitcoin price forecasting and trading strategy evaluation by integrating multimodal data sources, including market indicators, macroeconomic variables, and sentiment information extracted from financial news and social media. The model architecture is based on a Stacking-LSTM ensemble, which captures complex temporal dependencies and non-linear patterns in high-dimensional financial time series. To enhance predictive power, sentiment embeddings derived from full-text analysis using the DeepSeek language model are fused with traditional numerical features through early and late data fusion techniques. Empirical results demonstrate that the proposed model significantly outperforms baseline strategies, including Buy & Hold and Random Trading, in cumulative return and risk-adjusted performances. Feature ablation experiments further reveal the critical role of sentiment and macroeconomic inputs in improving forecasting accuracy. The sentiment-enhanced model also exhibits strong performance in identifying high-return market movements, suggesting its practical value for data-driven investment decision-making. Overall, this study highlights the importance of incorporating soft information, such as investor sentiment, alongside traditional quantitative features in financial forecasting models.
Cryptocurrencies have experienced exponential growth within the last decade, with market capitalization hovering above the one-trillion-dollar mark since 2022. One area of concern for current and potential crypto users and investors is their unprecedented price volatility. As cryptos become interlinked with the regulated financial system, questions emerge regarding the possibility of linkages of their prices to the external environments. Financial and macroeconomic factors of inflation, economic growth, interest rates, currency exchange rates, equity market returns, corporate bond yields, gold and oil prices are examined against the cryptocurrency returns. This study encompasses a multi-analytical approach, firstly with the empirical tests of Spearman’s correlational analysis to discover the most pertinent relationships, followed by the PCA analysis to reduce redundancy. The predictive regression model of the Granger Causality test, a vector autoregression (VAR) time series forecasting method, is applied to examine whether the highly effective factors Granger cause the crypto price movements. The Machine Learning Random Forest Regression is also applied where a nuanced understanding of the external factors affecting cryptos prices is gained. The findings of this study pertain to more recent times when the pandemic crisis has subsided and stable economies are in place. The results examined four major cryptos of Bitcoin, Binance Coin, Ripple and Tether, where most behaviours suggest that users and investors are willing to take on riskier assets during periods of economic growth, a strong equity market complements crypto demands and gold and oil are good substitutes for cryptos. Tether, a stablecoin, was the least impacted by external factors and behaved similarly to a fiat currency. This investigation into external factors will empower cryptocurrency users and investors with valuable insights into the crypto price mechanisms, enabling them to refine their investing and portfolio diversification strategies.
The environmental concerns associated with energy-intensive cryptocurrencies have led to the rise of clean cryptocurrencies, which aim to balance financial innovation and sustainability. This study investigates whether clean cryptocurrencies improve portfolio resilience while promoting environmental goals in the cryptocurrency market. Using dynamic correlation-based hedge and safe-haven regression models, relative risk ratio analysis with higher-order moments risks, and multiple portfolio optimization strategies, we assess the impact of integrating clean cryptocurrencies into portfolios composed mainly of traditional cryptocurrencies. The results show that clean cryptocurrencies consistently reduce tail risk during periods of market stress; however, this risk reduction does not always result in higher returns or better risk-adjusted performance. These findings have important implications for both investors and policymakers. Clean cryptocurrencies can help investors manage tail risk and align with ESG goals, but their implementation requires a careful assessment of return expectations and investment constraints. Policymakers are encouraged to create a regulatory framework that fosters sustainable digital asset development while protecting investors and ensuring market stability. This study contributes to a deeper understanding of clean cryptocurrencies’ role in sustainable investment strategies within the evolving digital asset landscape.
Abdullah A. Aljughaiman, Mosab I. Tabash, Suzan Sameer Issa, Abdulateif A. Almulhim
Most prior studies explain cross-country volatility interconnectedness without accounting for exogenous global uncertainty factors that influence equity returns. This study is the first to explore how major global uncertainty indicators such as U.S. and European financial market uncertainty indices (CBOE volatility index (VIX), VSTOXX-50), Global Financial Stress Indices (FSI) and Bitcoin Sentiment Indices (BSI) transmit shocks to the conditional volatility of Gulf Cooperation Council (GCC) stock markets. Using a novel ‘Extended Joint’ time-varying parameter vector autoregression (TVP-VAR) connectedness framework, the analysis addresses rolling-window limitations, enhances robustness to outliers, accommodates structural shifts and explains the shock transmission mechanism for the overall investment horizon. To capture transitory (short-term) and enduring (long-term) shock transmission channels from global uncertainty indicators toward the GCC financial system, a frequency-domain TVP-VAR is also employed. Furthermore, for the portfolio optimization, we also employ the hedge ratio and optimal portfolio weight strategy based on the DCC-GARCH-t copulas. Findings reveal that the conditional volatility of equity markets in Oman, Qatar, Saudi Arabia and the UAE is more sensitive to shocks from global uncertainty indicators such as VIX, VSTOXX-50 and the FSI, while Bahrain’s market shows relatively lower exposure. Kuwait’s equity market volatility exhibits the highest long-term sensitivity to FSI, VIX and VSTOXX-50, whereas the UAE demonstrates the highest sustained exposure to VIX and VSTOXX-50. Results from the DCC-GARCH-t copula model indicate that in stable periods (pre-COVID-19), optimized portfolio allocations significantly improved diversification, reducing risk by up to 83%. However, during financial stress events like COVID-19, hedge ratio strategies provided more effective risk mitigation, with reductions ranging from 3% to 43%.
Bu çalışmada ortaya ilk çıkarılan on kripto para getiri ve işlem hacimleri ile birlikte varil başına Batı Teksas (WTI) ham petrol getirileri arasındaki ilişki test edilmiştir. Analiz için 29 Nisan 2013 – 04 Ağustos 2024 arası günlük veriler kullanılmıştır. Çalışmada ampirik olarak Granger ve Toda Yamamoto Nedensellik Analizi' nden yararlanılmıştır. Her iki analize göre WTI ile Bitcoin (BTC) arasında negatif tek yönlü ilişkiye rastlanmıştır. Granger nedensellik analizine göre WTI ile Ethereum (ETH) arasında, Toda Yamamoto nedensellik analizine göre ise WTI ile Filecoin (FIL) getirisi arasında negatif çift yönlü bir nedensellik ilişkisi olduğu sonucuna ulaşılmıştır. Elde edilen bulgular enerji fiyatlarında yaşanan dalgalanmaların küresel finansal istikrara etkilerini ortaya koymuştur. Enerji piyasalarındaki sürdürülebilirlik hedefleri ile blok zinciri teknolojisinin çevresel etkilerini en aza indirgemek için uluslararası regülasyonların geliştirilmesi ve bütüncül politikalar oluşturulması gerekmektedir. Bu öneriler kripto para birimlerinin, enerji piyasalarından kaynaklanan volatiliteye karşı daha dayanıklı hale getirilmesi için stratejik bir yol haritası sunmaktadır.
The rise of cryptocurrencies over the past decade has transformed the global financial landscape, introducing new paradigms in investment, value storage, and monetary exchange. This study investigates the role of cryptocurrencies—specifically Bitcoin (BTC) and Ethereum (ETH)—as alternative investment assets within modern portfolio frameworks. As digital currencies continue to gain legitimacy and acceptance among retail and institutional investors, it becomes imperative to examine their financial performance, volatility characteristics, and correlation with conventional asset classes such as equities, bonds, and commodities. This research adopts a hybrid methodological approach, combining rigorous quantitative analysis with qualitative review. Using historical market data from 2015 to 2024, it evaluates key performance indicators such as average returns, standard deviation, Sharpe and Sortino ratios, Value at Risk (VaR), and maximum drawdown. It further explores the utility of cryptocurrencies in enhancing portfolio efficiency through diversification benefits, while also considering risk mitigation through dynamic asset allocation and rebalancing. The study extends beyond price metrics to include macroeconomic factors, such as inflation trends and monetary policy shifts, which influence crypto markets. It also addresses behavioral finance phenomena—including herd behavior, market sentiment, and media impact—that contribute to the observed volatility and price surges. The emergence of decentralized finance (DeFi), stablecoins, and central bank digital currencies (CBDCs) are also discussed to contextualize the evolving ecosystem and its implications for future investment strategies. Key findings indicate that while cryptocurrencies have historically outperformed traditional assets in terms of absolute returns, they also exhibit significantly higher volatility and downside risk. Despite these risks, their low to moderate correlation with conventional financial instruments enhances their value as diversification tools in modern portfolios. However, the study cautions that this benefit may diminish during times of extreme market stress when cross-asset correlations tend to rise. Moreover, the research highlights critical regulatory, technological, and environmental challenges associated with crypto adoption, including inconsistent global regulations, concerns over energy-intensive proof-of-work systems, and vulnerabilities in smart contracts. These factors underscore the need for robust governance frameworks and investor education to support sustainable growth in the digital asset market. In conclusion, the paper asserts that cryptocurrencies can serve as high-risk, high-reward components of a diversified portfolio, particularly for investors with higher risk tolerance and a long-term investment horizon. The future integration of cryptocurrencies into mainstream finance will depend largely on regulatory clarity, technological innovation, and the maturation of supporting infrastructure such as custody services, derivative markets, and institutional-grade investment vehicles
This study compares two approaches for measuring Conditional Value-atRisk (CoVaR), emphasizing the role of high-frequency intraday data in assessing systemic risk within financial systems. The first approach, AB CoVaR, estimates the risk of an asset Y conditional on another asset X being exactly at its Value-at-Risk (VaR) threshold. In contrast, the GE CoVaR refines this measure by capturing the risk of Y when X exceeds its VaR threshold, thereby accounting for more extreme scenarios and larger potential losses. To estimate these CoVaR measures, we employ high-frequency data sampled at five-minute intervals from major cryptocurrencies, including Bitcoin, Ethereum, Ripple, Solana, and Binance Coin. The results indicate that the GE CoVaR approach systematically yields higher risk estimates and exhibits superior predictive performance when applied to intraday data. Moreover, the analysis reveals strong interconnectedness among cryptocurrency returns. Bitcoin and Ethereum emerge as the primary sources of systemic risk, whereas Solana and Binance Coin are the most heavily affected assets. These findings underscore the granular risk dynamics captured through intraday analysis.
Understanding regime shifts in crypto asset markets is essential for anticipating systemic risk and enhancing real-time monitoring tools. This study investigates structural changes in five major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Aave (AAVE), and Bitcoin Cash (BCH)—over the 2023–2025 period. Using the Generalized Sup Augmented Dickey-Fuller (GSADF) test applied to daily high-frequency mid-price data, we assess the presence and timing of structural breaks in each asset. The results reveal that BTC and BCH experienced regime shifts that aligned with macroeconomic developments such as monetary policy announcements. In contrast, DeFi-related tokens (ETH, SOL, and AAVE) exhibited more fragmented and short-lived shifts, often driven by project-specific technical changes. Notably, ETH showed a structural break in April 2024, likely related to Layer-2 migration pressures and delays in protocol upgrades. In April 2025, both the crypto asset market and traditional financial markets experienced substantial turbulence following heightened trade policy actions by the United States, which fueled global economic uncertainty. Despite these disturbances, the S&amp;amp;P 500 index did not exhibit persistent structural breaks, suggesting that traditional equity markets are more resilient to transient macroeconomic shocks. This contrast underscores Bitcoin’s emerging role as a macro-sensitive digital asset and highlights the structural volatility within decentralized finance ecosystems. Although the GSADF test is computationally intensive (O(T4)), we discuss future research directions involving GPU acceleration and surrogate modeling. Additionally, we propose the integration of LPPLS-based frameworks to support real-time detection of financial exuberance and contribute to more robust risk management strategies in volatile crypto-financial systems.
Abstract Despite its importance, there has been little research on the relationship between Bitcoin’s risk and returns. Therefore, it is necessary to investigate the risk–return trade-off of Bitcoin. In the existing limited literature, a negative risk–return relationship in Bitcoin for high-frequency intraday time-series data has been reported. In this paper, we use lower time–frequency data and suitable models for the data frequency to examine the risk–return trade-off of Bitcoin. Specifically, this paper examines the time-series volatility risk–return trade-off of Bitcoin using standard Markov switching (MS) and MS–GARCH models with weekly Bitcoin data from 2010 to 2024. Consequently, the study reveals several new findings. Firstly, the volatility risk–return trade-off relationship is identified for Bitcoin’s log returns. Secondly, the risk–return trade-off is also found for Bitcoin’s simple returns. Thirdly, the risk–return trade-off is uncovered for Bitcoin’s risk premiums as well. Fourthly, the study shows that the risk–return trade-off relationships for Bitcoin’s log returns, simple returns, and risk premiums hold true for all business days from Monday to Friday, indicating the robustness of the results. Furthermore, the study presents significant interpretations, implications, and discussion. We emphasize that we have discovered positive weekly risk–return relationships for Bitcoin using Markov switching models for the first time. This demonstrates the novelty of our work.
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA.
This study examines the temporary impact of major global news on bitcoin absolute price changes from 2018 to 2023, focusing on information related to the COVID-19 pandemic, inflation, and the Russia-Ukraine conflict. Using Bloomberg news and high-frequency data, the analysis is conducted in two stages. First, hourly price data and only highly significant news are analysed over the entire period. Second, second-by-second data from the CME Bitcoin Real Time Index (BRTI) is employed for key dates, incorporating broader news categories. The results show that bitcoin investors need approximately 45 minutes to process each news item on COVID-19 and war as information continuously flows into the market. This constant information processing enables investors to anticipate highly significant news on these topics up to two hours before its publication. Conversely, inflation-related news exhibits concentrated effects around scheduled release times. The findings highlight the necessity of selecting appropriate time frequencies for the analysis to avoid misinterpretation. Overall, the study highlights the significant impact that relevant global news has on bitcoin price volatility, suggesting that bitcoin markets are becoming increasingly integrated with traditional financial markets.
This study analyses the price discovery between bitcoin exchange-traded funds (ETFs) and their underlying asset (bitcoin spot) after the introduction of bitcoin ETFs on US exchanges. Using 5-min data, starting from the launch of bitcoin ETFs on 11 January 2024 and nine months later, until 11 October 2024, we calculate three price discovery measures, namely Information Share (IS), Component Share (CS) and Information Leadership Share (ILS). Our ILS results suggest that bitcoin ETFs, especially the most actively traded ETFs such as IBIT, FBTC and GBTC, dominate price discovery over bitcoin spot about 85 per cent of the time during the sample period. These findings indicate an increasing investor preference for the more accessible and liquid ETFs, supported by the US SEC approval, and underline the growing appeal of bitcoin ETFs for investors seeking efficient bitcoin exposure through brokerage accounts. The study contributes to the literature on price discovery in the cryptocurrency market and provides insights for academics, investors, regulators and policymakers.
The energy sector underwent a significant transformation with increasing demand for efficiency, transparency, and sustainability. The traditional or conventional system often faces several challenges, such as inefficient energy trading, a lack of transparency in renewable energy generation verification, and complex regulatory guidelines that affect its widespread adoption. Thus, blockchain technology has emerged as a potential solution to overcome these challenges, as it is known for its transparent, secure, and decentralized nature. However, despite the promising application of blockchain, its integration into the energy supply chain (ESC) is underexplored. The purpose of this research is to analyze the potential applications of blockchain technology in ESC in order to enhance efficiency, transparency, and sustainability in energy systems. The aim is to investigate the integration of blockchain with emerging technologies (such as IoTs, smart contracts, and P2P energy trading) in order to optimize energy production, distribution, and consumption. Furthermore, by comparing different blockchain platforms (like Ethereum, Solana, Hedera, and Hyperledger Fabric), this study discusses the security and scalability challenges of using blockchain in energy systems. It also examines the practical use cases of blockchain for the tokenization of RECs, dynamic energy pricing, and P2P energy trading by providing the Energy Web Foundation and Power Ledger as real-world examples. The article concludes that blockchain technology has the potential to transform ESC by enabling decentralized energy trading, which subsequently enhances transparency in energy transactions and the verification of renewable energy generation. It also identifies smart contracts and tokenization of energy assets as key parameters for dynamic pricing models and efficient trading mechanisms. However, regulatory and scalability challenges remain significant obstacles to its widespread adoption. Finally, this study provides the basis for future advancement in the adoption of blockchain technology in ESC, which offers a valuable resource for industry professionals, regulating authorities, and researchers.