Blockchain Papers

Tian Lan, Michael Frömmel

No abstract is available for this record.

Staenly Staenly, Maria Yus Trinity Irsan

This study investigates the price dynamics of Bitcoin, a highly volatile and speculative digital asset. Using daily closing price data from January 2023 to January 2024, we apply the Bates model, which combines stochastic volatility with jump-diffusion processes, to better capture both continuous fluctuations and sudden, large price changes in the market. The model parameters are calibrated using historical data and evaluated through Monte Carlo simulation with 10,000 generated price paths over a 31-day forecast horizon. The results demonstrate a strong short-term predictive performance, with a Mean Absolute Percentage Error (MAPE) of 4.32%. This indicates that the Bates model can capture both volatility clustering and abrupt shifts, which are characteristic of Bitcoin. The findings suggest that this approach provides a valuable tool for risk management and investment decision-making in highly uncertain and dynamic markets.

Egil Diau

A central challenge in economics and artificial intelligence is explaining how financial behaviors-such as credit, insurance, and trade-emerge without formal institutions. We argue that these functions are not products of institutional design, but structured extensions of a single behavioral substrate: reciprocity. Far from being a derived strategy, reciprocity served as the foundational logic of early human societies-governing the circulation of goods, regulation of obligation, and maintenance of long-term cooperation well before markets, money, or formal rules. Trade, commonly regarded as the origin of financial systems, is reframed here as the canonical form of reciprocity: simultaneous, symmetric, and partner-contingent. Building on this logic, we reconstruct four core financial functions-credit, insurance, token exchange, and investment-as expressions of the same underlying principle under varying conditions. By grounding financial behavior in minimal, simulateable dynamics of reciprocal interaction, this framework shifts the focus from institutional engineering to behavioral computation-offering a new foundation for modeling decentralized financial behavior in both human and artificial agents.

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.

Thar Saadoon Shnaishel

General background: The increasing integration of digital technologies has transformed global financial systems, with cryptocurrencies, especially Bitcoin, emerging as prominent financial instruments. Specific background: Amid widespread adoption by institutions and individuals, Bitcoin has garnered attention for its potential to influence traditional financial markets, particularly during periods of global uncertainty such as the COVID-19 pandemic. Knowledge gap: While much has been discussed about the theoretical influence of cryptocurrencies, empirical evidence on their actual impact on global financial indices remains inconclusive. Aims: This study investigates the effect of Bitcoin trading volume and the COVID-19 pandemic on a composite index comprising advanced (S&P 500), emerging (KLSE), and developing (DZ) market indices from July 2018 to December 2022. Results: Using a fixed-effects panel data model, the findings reveal that past market performance significantly predicts current performance, while Bitcoin trading volume and the pandemic show no statistically significant impact. Novelty: The study uniquely combines market classifications and utilizes a composite index to empirically isolate the influence of Bitcoin across diverse economies. Implications: These results suggest that, despite Bitcoin's rising prominence, its direct influence on global financial markets may be limited in the short term, underscoring the need for continued investigation as regulatory frameworks and adoption rates evoHighlight : Minimal Impact: Bitcoin trading volume and the COVID-19 pandemic had no statistically significant effect on global financial market indices (2018–2022). Strong Market Correlation: Global financial indices showed strong interdependence, reflecting synchronized market behavior. Future Outlook: Despite current findings, evolving crypto regulations and technologies may alter their financial market influence. Keywords : Cryptocurrencies, Bitcoin, Trading Volume, COVID-19, Financial Indices

Mfaume Ismail Mahmoud, Arni Surwanti

Blockchain technology has emerged as a revolutionary force in modern finance, significantly impacting financial market efficiency by enhancing transparency, reducing transaction costs, and eliminating intermediaries. However, its overall effect on market efficiency remains a subject of academic debate. This study conducts a bibliometric and network analysis to systematically assess the evolution of blockchain research in financial markets, highlighting key publication trends, influential authors, leading institutions, and dominant research themes. Using Scopus as the primary database, a structured search strategy identified 3,054 high-quality articles published between 2005 and 2025, focusing on Business, Management, and Accounting (BUSI) and Economics, Econometrics, and Finance (ECON). VOSviewer was employed to map research collaborations, co-authorship structures, and keyword co-occurrences, providing a comprehensive understanding of the intellectual development in this field. Findings reveal a sharp increase in blockchain-related financial research, particularly post-2016, driven by the expansion of decentralized finance (DeFi) and institutional interest in digital assets. The study identifies Corbet, S., and Yarovaya, L., among the most influential authors, while leading institutions include Dublin City University and Lebanese American University. China, the United States, and India dominate research output, reflecting global interest in blockchain's financial implications. The analysis further uncovers key thematic clusters, including market efficiency, liquidity, and regulatory challenges, while also highlighting blockchain’s emerging applications in sustainable finance and artificial intelligence-driven investment strategies. Despite significant academic contributions, gaps persist, particularly in empirical assessments of blockchain’s long-term impact on market stability, regulatory alignment, and integration with traditional financial systems. Future research should focus on addressing these gaps by exploring cross-border regulatory frameworks, expanding studies beyond cryptocurrencies to tokenized assets, and investigating the role of artificial intelligence in blockchain-based financial solutions. By advancing these research directions, scholars and policymakers can develop a structured approach to blockchain adoption, ensuring its long-term sustainability and effectiveness in global financial markets.

Xiang Meng

Financial market efficiency is significantly influenced by the availability and quality of information, with information asymmetry posing a major barrier to optimal market functioning. This article reviews the role of data science in mitigating information asymmetry and enhancing market efficiency, comparing traditional approaches with modern data-driven methods (e.g., machine learning, NLP, and blockchain). It systematically evaluates traditional approaches used to measure and mitigate information asymmetry and highlights their limitations in accurately capturing complex market dynamics. Traditional approaches such as statistical testing, price behavior analysis, and asset pricing models provide fundamental insights but often fail to capture complex, non-linear market dynamics, such as adverse selection, moral hazard, and asset mispricing, due to their reliance on historical data and linear assumptions. In contrast, data science has revolutionized financial market analysis by combining machine learning, natural language processing (NLP), big data analytics, and blockchain technology to solve information imbalances. It enables real-time analysis of unstructured data, improves predictive modeling, and enhances transparency through sentiment analysis, algorithmic trading, and decentralized ledgers. It concludes that integrating data science with traditional finance theory significantly reduces information gaps, offering policymakers and investors tools to foster fairer, more efficient markets. This bridges theoretical finance with computational innovations, demonstrating how data science addresses longstanding limitations in measuring and improving market efficiency.

Stephanie Danielle Subramoney, Knowledge Chinhamu, Retius Chifurira

This paper investigates the volatility dynamics and underlying long memory features of four major cryptocurrencies-Bitcoin, Ethereum, Litecoin, and Ripple-which were selected due to their high liquidity, large trading volumes, and historical significance in the digital asset market. The long-range dependence exhibited in cryptocurrency markets is often overlooked. However, based on the strong evidence of persistent dependence in the return series, we adopt advanced volatility models that are capable of accommodating high volatility and heavy-tails, as well as the long memory properties of cryptocurrencies. Specifically, we employ long-memory extensions of the GAS (Long memory GAS) and GARCH (Fractionally Integrated Asymmetric Power ARCH) models, integrating heavy-tailed innovation distributions: the Generalized Hyperbolic Distribution (GHD) and Generalized Lambda Distribution (GLD). Standard GARCH and GAS models are included as benchmarks. The performance of the models are assessed using Value-at-Risk (VaR) estimation, backtesting (in-sample and out-of-sample) and volatility forecasting metrics. The results indicate that long memory models, particularly the FIAPARCH model, consistently outperforms the standard GAS and GARCH models in capturing tail risk and the volatility persistence. These findings emphasize the critical role of long memory in modeling the risk of cryptocurrencies, indicating that accounting for volatility persistence can significantly enhance the accuracy of risk estimates and strengthen risk management practices.

Ahmet Celikoglu

Whether financial assets movements exhibit correlation and memory has been an intriguing question for physicists. This study aims to investigate whether financial shocks exhibit non-Markovian behavior. In particular, it explores the presence of long-term memory and non-local fluctuations during financial crises. The non-Markovian behavior of volatility and return during the cryptocurrency crashes of 2017–2021 and 2021–2024 cycles are examined. The analysis shows that a scaling relation, which is valid for a singular Markovian process, breaks down in data sets spanning approximately 1 year and 3 years after the onset of the 2017 crash. A similar pattern was observed in the 2021 crash, although the analysis does not work for some data sets. In these time intervals, the crash process shows non-Markovian behavior with financial shocks demonstrating non-local fluctuations and evidence of long-term memory.

Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza

Abstract Predicting cryptocurrency prices is challenging due to market volatility and external influences like social media sentiment. This study integrates Twitter sentiment analysis with deep learning models (LSTM, GRU, Bi-LSTM, and Temporal Attention Model) to enhance Bitcoin price forecasting. Sentiment features were extracted using VADER and RoBERTa, with findings showing that RoBERTa-based models significantly outperform VADER. Bi-LSTM (RoBERTa) achieved the lowest MAPE of 2.01%, demonstrating the effectiveness of deep contextual embeddings. SHAP analysis identified Sentiment Momentum, RoBERTa Compound Score, and VADER Negativity Score as key predictors of price movements. These results highlight the value of sentiment-driven forecasting and provide insights for traders, investors, and researchers.

Luca Galati, Salvatore Perdichizzi

We analyze Trump’s memecoin launch, showing heterogeneous volatility spillovers driven by sentiment and fundamentals. Political signals amplified speculative dynamics, underscoring how politics increasingly shapes cryptocurrency markets and investor behavior.

David Umoru, Malachy Ashywel Ugbaka, Anake Fidelis Atseye, Samuel Manyo Takon · 18 authors

The financial market is a decentralized market made up of global network of businesses, forex, stock investment, and digital markets. The paper evaluated the patterns and interrelationships of volatilities in return amongst foreign exchange, stock, and bitcoin markets returns in oil importing nations. The Markov-Switching and quantile regression estimation methods were executed. Results indicate stock markets of Kenya and Uganda had the most frequent depreciating returns. Bitcoin returns were negatively and significantly influenced by changes in currency values, whereas change in bitcoin trading value causes a higher change in exchange rate returns. A percentage increase in stock market returns stimulates exchange rate returns to rise also but at a higher rate. Returns on exchange rates and Bitcoin markets are significant predictors of stock market returns. Exchange rate volatility dynamics occur in the opposite direction as those in stock markets and in the floor of Bitcoin market. Volatility was significantly observed when currency devalued confirming the erratic behaviors of investors to dwindling local currency values compared to the U.S. dollar. Financial markets authorities can use the research findings to support their choice to regulate the financial markets and shield investors from information asymmetry that could result from cross-market volatility interrelationships.

Essa Al-Mansouri

This paper investigates Bitcoin’s resilience against the U.S. dollar—widely recognized as the global reserve currency—by applying a multi-method wavelet analysis framework to daily price data of Bitcoin, the USD strength index (DXY), the euro, and other assets ranging from August 2015 to June 2024. Quantitative measures—particularly the Frobenius norm of wavelet coherence and an exponential decay phase-weighting scheme—reveal that Bitcoin’s out-of-phase relationship with the dollar is lower and more sporadic than that of mainstream assets, indicating it is not tightly governed by dollar fluctuations. Even after controlling for the euro’s dominant influence in the DXY, BTC continues to show weaker coupling than mainstream assets—reinforcing the idea that it may serve as a partial hedge against dollar-driven volatility. These results support the hypothesis that Bitcoin may serve as a resilient store of value and hedge against dollar-driven market volatility, placing Bitcoin within the broader debate on global monetary frameworks. As global monetary conditions evolve, the resilience of Bitcoin (BTC) relative to the world’s leading reserve currency—the U.S. dollar—has significant implications for both investors and policymakers.

Jaroslaw Klepacki

PURPOSE: This study aims to investigate psychological and behavioral mechanisms and their impact on the cryptocurrency market. The analysis is carried out through the prism of studying the FOMO phenomenon.

Kevin Rink

Abstract We use transaction-level data from the Bitcoin exchange Mt.Gox, including over 1.4 million transactions from more than 45,000 traders, to investigate the role of technical chart patterns in the early Bitcoin market from April 2011 to September 2013. Employing a pattern recognition algorithm, we identify hourly trading signals for five major chart patterns. Buy signals of these patterns are associated with an average increase in abnormal trading volume of more than 53%. Trades executed during buy signal periods yield significantly higher average returns than those made during non-signal periods. Traders who use chart patterns more frequently are more likely to generate right-skewed return distributions, engage in more active trading, and achieve higher average roundtrip returns. Our research suggests that chart pattern trading was a crucial tool for Mt.Gox clients, highlighting the importance of technical heuristics in shaping the dynamics in a less efficient and unregulated market environment. By leveraging a comprehensive transaction dataset from a major cryptocurrency exchange, we provide unique insights into the actual trading behavior of the first Bitcoin adopters. This sets our work apart from previous studies that mainly rely on backtesting technical strategies using publicly available price data.

Tetsuya Takaishi

This study examines the impact of the coronavirus disease 2019 (COVID-19) pandemic on market efficiency by analyzing three time series -- price returns, absolute returns, and volatility increments -- in stock (Deutscher Aktienindex, Nikkei 225, Shanghai Stock Exchange (SSE), and Volatility Index) and cryptocurrency (Bitcoin and Ethereum) markets. The effect is found to vary by asset class and market. In the stock market, while the pandemic did not influence the Hurst exponent of volatility increments, it affected that of returns and absolute returns (except in the SSE, where returns remained unaffected). In the cryptocurrency market, the pandemic did not alter the Hurst exponent for any time series but influenced the strength of multifractality in returns and absolute returns. Some Hurst exponent time series exhibited a gradual decline over time, complicating the assessment of pandemic-related effects. Consequently, segmented analyses by pandemic periods may erroneously suggest an impact, warranting caution in period-based studies.

Chiara Oldani, Giovanni S. F. Bruno, Marcello Signorelli

This paper investigates the existence of bubbles in the daily prices of the most popular cryptocurrencies, Bitcoin (BTC), Ether (ETH), and Ripple (XRP), employing the recursive methods of Phillips et al. (2015) and Phillips et al. (2011) for testing and date-stamping episodes of exuberant behaviour over a period spanning seven years (2018–2024), including the COVID-19 pandemic crisis (2020–2021). The critical values of the tests are computed through the composite wild bootstrap technique by Phillips and Shi (2020) to make them robust to time-varying unconditional heteroscedasticity and the multiplicity issue in recursive tests. Results indicate that the prices of the most popular cryptocurrencies traded on decentralized ledgers, BTC and ETH, exhibited multiple episodes of exuberant behaviour, unambiguously for BTC and depending on the tests for ETH. Bubbles detected in the prices of BTC were due to the halving of the crypto, to market exuberance and to the pandemic crisis; bubbles detected on ETH prices were due to the launch of NFTs on the Ethereum blockchain, and to the change in investors’ expectations (from exuberant to pessimistic); the change in the stance of monetary policy burst the bubbles of BTC and ETH prices in 2024. No test supports the exuberance of XRP that is traded on a centralized ledger; weekly data confirm the absence of multiple bubbles. By looking at the presence of bubbles in these different digital ecosystems, we also consider how the technological differences can impact, possibly asymmetrically, bubbles' formation.

Muhammad Muzammil, Abisheka Pitumpe, Xigao Li, Amir Rahmati · 5 authors

Governments and regulatory bodies have recognized investment scams as a prevalent form of cryptocurrency fraud. These scams typically use professional-looking websites to lure unsuspecting victims with promises of unrealistically high returns. In this paper, we introduce Crimson, a distributed system designed to continuously detect cryptocurrency investment scam websites as they are created in the wild. During the first 8 months of 2024, Crimson processed approximately 6 billion domain names and classified 43,572 unique cryptocurrency investment scam websites in real-time. Beyond detection, we provide insights into the design and infrastructure of these websites that can help users recognize scam patterns and assist hosting providers in detecting and blocking such sites. Furthermore, we investigate the inclusion of our detected scam websites in block-lists used by popular web browsers and applications, finding that the vast majority of these websites were absent. On the financial side, by analyzing the transactions incoming to scammer wallets on 6.7% of the sites detected by Crimson, we observe an estimated lower bound of 2.04M USD in losses due to cryptocurrency investment scams.

Micaela Suriano, Leonidas Facundo Caram, César F. Caiafa, Hernán Merlino · 5 authors

This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity–entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.

B. Ashok, V. Basil Hans

This paper explores temporal coordination mechanisms in market economies through the lens of Austrian Capital Theory, emphasizing how interest rates facilitate the alignment of complex intertemporal production plans across dispersed market participants. The study addresses the challenge of coordinating heterogeneous capital goods over time, a critical issue in dynamic economic systems where production spans multiple stages and horizons. Through a rigorous theoretical analysis and an extensive literature review, the research investigates the role of market processes in achieving this coordination, with a particular focus on how monetary policy influences these mechanisms. The analysis reveals that interest rates act as vital signals, aggregating dispersed knowledge and guiding entrepreneurial decisions to align production structures with consumers’ time-preferences. However, monetary interventions, such as interest rate manipulations, are shown to distort these signals systematically, contributing to malinvestment—where resources are misallocated to unsustainable projects—and overconsumption during business cycles. Empirical evidence from the 2002–2009 period, including the U.S. Federal Reserve’s monetary expansion, illustrates these effects, highlighting how negative real interest rates (2003–2005) falsified economic calculations, inflating household net worth by $21.7 trillion while reducing savings rates to below 1% by 2005, only to collapse by $13 trillion in 2008. This research synthesizes Austrian insights with emerging technological developments, particularly Web 3.0 technologies and decentralized systems like smart contracts and decentralized finance (DeFi), which may enhance market coordination by reducing reliance on central intermediaries and improving knowledge transmission. The originality lies in bridging classical economic theory with modern technological paradigms, offering a framework to assess how decentralized innovations can preserve Austrian principles of entrepreneurial discovery and spontaneous order. This theoretical analysis contributes to understanding the interplay between monetary policy, technology, and market dynamics, providing a foundation for future empirical studies on decentralized economic coordination.

Florentin Şerban

Traditional portfolio optimization techniques predominantly rely on the classical mean–variance framework introduced by Markowitz, which focuses on balancing expected returns against risk, typically measured by variance. However, in volatile and structur-ally unstable markets such as cryptocurrencies, this approach often fails to capture the full spectrum of uncertainty and diversification potential. This paper introduces an al-ternative methodology grounded in entropy, a fundamental concept in information theory that quantifies uncertainty and disorder. By incorporating entropy into the portfolio optimization process, we offer a more generalizable, distribution-free approach that enhances diversification and resilience.We develop and analyze three distinct en-tropy-based models: the maximum Shannon entropy model, the second-order entropy (Tsallis) model, and the maximum weighted Shannon entropy model. These formula-tions extend the traditional mean–variance approach by integrating nonlinear uncer-tainty measures, enabling a richer representation of investor preferences and asset in-terdependencies. Analytical solutions to the proposed models are derived using the method of Lagrange multipliers, ensuring mathematical rigor and interpretability.The proposed models are empirically validated using a portfolio composed of four leading cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—with market data from January to March 2025. The case studies demonstrate how entropy-based optimization leads to well-diversified portfolios, robust under market turbulence and heavy-tailed return distributions. Notably, the models facilitate dynamic adjustments in asset allocation in response to shifts in return–risk characteristics and entropy levels. This study contributes to the ongoing generalization of portfolio theory by positioning entropy as both a diversification enhancer and a structural risk measure. It provides theoretical insight, practical tools for asset allocation in high-volatility environments, and paves the way for future research in entropy-driven financial optimization frameworks.

Diego Mazzitelli, Elia Fiorenza, Inès Belgacem, Carmelo Arena

Objective of this manuscript is both to tracing the evolution of money, and examining its transition from commodity money to fiat money, up to the emergence of cryptocurrencies. It highlights the inherent issues of the barter system, emphasizing the urgencies and necessities that favored the adoption of legal tender. Subsequently, the impact of the creation of the Euro on the European economy—both historically and geopolitically—will be analyzed, contextualizing the European Union's institutional process. In a response to the crisis, Bitcoin (the first decentralized cryptocurrency) will be introduced, along with an illustration of the supporting Blockchain technology will be provided. Finally, the proposal of American Senator Lummis, who suggests a massive purchase of Bitcoin to be used as a strategic reserve through the “Bitcoin Act” program, will be explored, prompting several reflections on the future of the petrodollar as a reserve instrument. Through these reflections, the reader could develop their own thoughts on the importance of evolving towards forms of money more suited to an increasingly digitized and decentralized economy. In conclusion, by proposing an analogy between the ancient monetary practices on Yap and cryptocurrencies, we aim to stimulate the reflection that innovation is not only desirable in this fast-paced world but essential.

Claudio Boido, Mauro Aliano

Cryptocurrencies have attracted significant attention due to their high risk, extreme volatility, regulatory controversies, and scandals. Investors and policymakers are drawn to them for their potential to enhance diversification and deliver high returns. This study examines the impact of incorporating cryptocurrencies into investment portfolios, focusing on their ability to improve risk-adjusted returns and diversification. A rolling asset allocation strategy employing the maximum Sharpe Ratio within a Markowitz framework was applied to weekly data from 2018 to April 2024. The analysis compares two unconstrained portfolios and two constrained portfolios, which impose a concentration limit on cryptocurrency investments. Results reveal that in 70% of the rolling periods examined, portfolios with cryptocurrency allocations outperformed non-cryptocurrency portfolios in terms of Sharpe Ratios. However, the heightened volatility of cryptocurrencies significantly increased portfolio risk, with annualized weekly standard deviations ranging from 18% to 25%, compared to 12% to 15% for portfolios without cryptocurrency exposure. These findings illustrate the dual nature of cryptocurrencies: they can act as both a source of instability and an opportunity for diversification. The study underscores the necessity of a cautious and strategic approach to incorporating cryptocurrencies into investment plans, given their inherent risks and unpredictable behavior.

Kamel Touhami, Ilyes Abidi, Mariem Nsaibi, Maissa Mejri

This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and Time-Varying Coefficients Vector Autoregression (TVC-VAR) models, this study provides empirical evidence that environmental variables significantly affect the volatility and returns of Bitcoin, Ethereum, and Tether. The results show that Bitcoin and Ethereum are highly sensitive to CO2 emissions and temperature fluctuations, while Tether demonstrates a more moderate response. Moreover, the impact of these environmental factors evolves over time, underscoring their dynamic nature in cryptocurrency valuation. These findings highlight the importance of incorporating environmental variables into forecasting models to enhance risk management and investment strategies. This study contributes to the literature by bridging the gap between environmental concerns and cryptocurrency market behavior, offering valuable insights for investors, regulators, and policymakers.