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&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.
Purpose (1) How can high-frequency data be utilized more effectively to identify and extract various risks in the cryptocurrency market? (2) Do the risk characteristics of different cryptocurrencies exhibit consistency or variability across multiple dimensions? (3) Based on these risk characteristics, how can more precise risk prevention and hedging strategies be provided to investors? Design/methodology/approach (1) The threshold optimal detection (TOD) model can accurately separate jump and continuous behaviors by setting appropriate threshold values, effectively identifying extreme price fluctuations in the market. (2) The method for separating trend and cyclical behaviors can be achieved through filter design and application. This approach effectively distinguishes between long-term and short-term fluctuations in the cryptocurrency market, enabling a clearer analysis of market risks across different time scales. (3) We use linear regression to estimate the sensitivity of cryptocurrency returns to different risk factors, represented by the β coefficient. (4) The time-frequency domain characteristics of wavelet coherence analysis allow for simultaneous examination of risk frequencies in the cryptocurrency market, providing a more comprehensive understanding of market behavior. Findings First, by integrating high-frequency data with multidimensional risk decomposition techniques, this study systematically identifies and analyzes various risk features within the cryptocurrency market, enriching the existing literature on high-frequency volatility and risk identification. Second, this paper innovatively decomposes continuous risk into trend risk and cyclical risk, providing a more refined framework for managing market volatility risks. Finally, the paper proposes differentiated response strategies tailored to various risk characteristics, particularly in jump risk management, offering practical guidance on the use of derivatives such as options to provide actionable solutions for investors. Originality/value This paper proposes a multidimensional risk extraction and analysis method by examining high-frequency data of nine major cryptocurrencies from December 2020 to July 2024. It not only explores the characteristics of jump risk and continuous risk but also further decomposes continuous risk into trend risk and cyclical risk using filtering techniques, revealing the heterogeneous performance of different cryptocurrencies in both long-term and short-term volatility. This multidimensional risk analysis allows for a more comprehensive capture of various market fluctuation patterns, providing investors and risk managers with more effective response strategies.
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.
Purpose This study aims to explore dynamic correlations and volatility spillovers as well as hedging opportunities of clean and dirty cryptocurrencies with both developed and emerging regional stock markets during the COVID-19 pandemic and the Russia–Ukraine conflict. Design/methodology/approach This study applies, first, the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity model proposed by Engle (2002) to analyze the dynamic correlations between clean–dirty cryptocurrencies and regional stock markets. Second, it uses the VAR–MGARCH with the BEKK representation developed by Engle and Kroner (1995), to explore the volatility spillover effects between all variables. Third, it determines the optimal portfolio weights and the hedge ratios following Kroner and Ng (1998) and Kroner and Sultan (1993), respectively. Findings The findings reveal significant correlation and volatility spillovers between cryptocurrencies and regional stock markets, which are more prevalent during the COVID-19 pandemic than during the Russia–Ukraine conflict. In addition, in times of crisis, pairing clean cryptocurrencies with emerging market indices appears more attractive due to their relatively lower contagion effects and volatility spillovers as well as cheaper hedging cost. Originality/value To the best of the authors’ knowledge, this study is among the first to analyze the dynamic linkages of clean and dirty digital currencies with regional stock market indices during both COVID-19 pandemic and Russia–Ukraine conflict, contributing to not only enhanced understanding of these cross-market spillovers in time of crisis but also their hedging benefits. This understanding provides actionable insights into portfolio construction and risk management involving clean and dirty cryptocurrencies and regional stock market indices.
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.
Sami Mejri, Francisco Jareño, Nasir Khan, Arturo Leccadito
This study examines the impact of geopolitical risk (GPR) on black and green cryptocurrencies during crisis times, focusing on their potential as hedging instruments and safe havens. Using daily data on nine cryptocurrencies (Bitcoin, Ethereum, Binance, Litecoin, Ripple, EOS, IOTA, Stellar and Tezos) and the Geopolitical Risk Index from January 3rd, 2019, to January 20th, 2025, the research employs a Regime-Switching Global Vector Autoregressive (RSGVARX) model and a quantile-on-quantile (QQ) approach to capture heterogeneous responses across market states and quantiles. In addition, the Dynamic Conditional Correlation (DCC) GARCH copula and Dynamic Gerber Correlation (DGC) models assess the hedging effectiveness and optimal portfolio weights of various cryptocurrency pairs. The study uniquely combines the RSGVARX and QQ methods to provide a comprehensive understanding of the dynamic interactions between GPR and cryptocurrency returns and introduces robust portfolio optimisation analysis using advanced econometric models. The results show that the impact of GPR on black cryptocurrencies is generally negative and statistically insignificant in Regime 1, with mixed effects in Regime 2, while green cryptocurrencies show similar heterogeneous responses. Several cryptocurrencies show resilience to GPR shocks in certain scenarios, highlighting their potential as reliable assets in times of geopolitical instability. The portfolio optimisation analysis identifies Bitcoin paired with Ethereum, Binance and Litecoin as the most effective combination for hedging throughout the sample period and during the stressful Russia-Ukraine war and Israeli-Palestinian conflict. These results suggest that investors should consider market states and transition probabilities when developing portfolio strategies involving cryptocurrencies, providing valuable insights for managing risk and ensuring financial stability during geopolitical crises.
The aim of this study is to reveal the dynamics between climate policy uncertainty (CPU) and S&P Global Carbon Credit Index (CARBON), S&P Cryptocurrency DeFi Index (DeFi), and WilderHill New Energy Global Innovation Index (NEX) using data from December 2017 to March 2024 in the US. Fourier Bootstrap ARDL, Fourier Bootstrap quantile causality, and KRLS methods are used in the study. The findings reveal that there is a negative relationship between the CARBON and the CPU index in the long term. Although the DeFi does not have a statistically significant effect in the long term, it reveals that it has a negative effect on the CPU index in the short term. In contrast, the NEX has a positive relationship with the CPU index in both the short and long term. Moreover, there is a U-shaped non-linear relationship between the NEX and the CPU index, which weakens in moderate climate uncertainties and strengthens again in high uncertainty. Considering the causality results, there exists a causality from CARBON to CPU in the 2nd, 3rd, and 4th quantiles, and from CPU to CARBON in the 2nd and 3rd quantiles. Additionally, there is a causality from DeFi to CPU in the 8th quantile and from CPU to DeFi in the 1st quantile. Finally, there is a causal relationship from NEX to CPU in the 2nd, 3rd, 4th, and 5th quantiles and from CPU to NEX in the 9th quantile.
As blockchain technology drives the global expansion of the digital currency market, the widespread adoption of high-frequency trading and cross-market arbitrage strategies poses dual challenges to traditional regulatory measures in terms of timeliness and accuracy. This study constructs a hybrid neural network model that integrates supervised and unsupervised learning to explore multi-dimensional feature fusion paths between on-chain data from blockchain and secondary market price data. Based on dynamic game theory, an intelligent regulatory sandbox system is designed, incorporating on-chain address reputation scoring mechanisms and liquidity smart contract circuit breakers to achieve real-time warnings and responses to market manipulation behaviors. Furthermore,a distributed regulatory framework built on zero-knowledge proof technology is proposed, providing a feasible solution for establishing a penetrating regulatory system while ensuring transaction privacy.
Zaäfri A. Husodo, Md. Bokhtiar Hasan, Humaira Tahsin Rafia, Masagus M. Ridhwan · 6 authors
This study investigates the interconnected dynamics among diverse digital currencies, specifically focusing on risk-adjusted returns, tail risks, dynamic spillovers, and portfolio implications. Unlike prior research, which typically examines individual digital currency classes separately or in limited combinations, our study integrates six distinct classes of digital currencies, namely Islamic gold-backed cryptocurrencies, green cryptocurrencies, gold-backed stablecoins, non-fungible tokens (NFTs), decentralized finance (DeFi) assets, and conventional cryptocurrencies, enabling direct comparisons of risk-return dynamics and systemic interdependencies. Using Value at Risk (VaR), Conditional Value at Risk (CVaR), quantile-based Vector Autoregression (Quantile VAR), and network connectedness analysis, we provide nuanced insights into the behavior of these assets across various market conditions (bullish, bearish, and normal states). Our results demonstrate that conventional cryptocurrencies and DeFi assets consistently deliver positive risk-adjusted returns, whereas Islamic gold-backed cryptocurrencies exhibit notably higher downside risks and negative performance. Spillover analysis reveals pronounced connectedness, particularly in extreme market states, with conventional cryptocurrencies identified as primary transmitters of market shocks and gold-backed stablecoins and Islamic gold-backed cryptocurrencies as recipients. Our findings underscore significant diversification opportunities offered by pairs of assets exhibiting low connectedness, especially in normal market conditions. Furthermore, portfolio optimization analysis highlights the superior hedging effectiveness and lower hedging costs associated with gold-backed stablecoins and conventional cryptocurrency pairs. This comprehensive investigation delivers critical implications for investors, suggesting informed strategies for asset allocation and risk management. Policymakers can also utilize our insights to design adaptive regulatory frameworks that address systemic risks arising from digital currency markets. ACKNOWLEDGMENT Gazi Salah Uddin gratefully acknowledges the Faculty of Economics and Business, Universitas Indonesia, for the academic appointment as Adjunct and Visiting Professor, and expresses sincere appreciation for the institutional support and research facilities extended during his residency, which significantly contributed to the completion of this work.