Blockchain Papers

Erkan USTAOĞLU

No abstract is available for this record.

Ifran Khan, Huangbao Gui, Chin Man Chui, Mrs Faryal · 6 authors

This study investigates the dynamic volatility transmission between leading cryptocurrencies (Bitcoin, Ethereum, and Binance Coin) and major Chinese firms in the technology (Tencent and Alibaba), green energy (CATL, BYD, and LONGi), and traditional energy (PetroChina) sectors, including the CSI 300 index. Employing the frameworks of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) on daily data from July 2018 to May 2025, we demonstrate significant cross-market risk transmission. The total connectedness index averages 34.77%, soaring to over 50% during the COVID-19 crisis, underscoring heightened systemic vulnerability. Our key finding identifies the CSI 300 index and cryptocurrencies (BTC, ETH) as the primary net transmitters of volatility shocks, whereas Chinese tech and energy firms (Tencent, CATL, and PetroChina) act as the main net receivers. A critical insight from the frequency decomposition is the absolute dominance of short-term spillovers (1–4 days), which constitute 34.85% of total connectedness, vastly outweighing the minimal effects in the medium- (4–10 days: 0.78%) and long-term (beyond 10 days: 0.52%). Investor sentiment, speculation, and news shocks drive short-term volatility spillovers from cryptocurrencies to stocks, particularly evident in their strong correlation with Chinese tech and energy equities. We attribute these spillovers to shared investor bases, sectoral links like crypto mining's energy demand, and regulatory interdependencies. Our evidence confirms that cryptocurrency markets are now integral to global financial stress, transmitting significant volatility to real-economy sectors. This study offers critical insights for investors and policymakers managing risk in an increasingly interconnected financial landscape.

Seyed Mehdian, Ştefan Cristian Gherghina, Ovidiu Stoica

This paper examines the market reaction to the approval of spot Bitcoin and Ethereum exchange-traded funds (ETFs), focusing on the return dynamics of a functionally diverse types of leading cryptocurrencies, including coins (BTC, BCH, LTC, XRP), smart contract platforms (ETH, ADA, AVAX), and utility tokens (LINK, MATIC). Using high-frequency intraday data, we perform an event study to assess the abnormal returns around the ETF approval dates. This study makes a significant contribution to the literature on event studies by being the first to examine investors’ reactions to information arrival in a “primary market.” Both the market model and the capital asset pricing model (CAPM) are applied to evaluate the effects of ETF approval on individual asset returns. Our results reveal that spot Bitcoin ETF approval by the US Securities and Exchange Commission leads to significant positive abnormal returns, along with heightened market volatility. In contrast, spot Ethereum ETF approval has had more modest effects. Moreover, we observe considerable shifts in the volatility spillovers among Bitcoin, Ethereum, and other major cryptocurrencies after the ETF approval, reflecting a change in market sentiment and interconnectedness. This analysis enhances understanding of how institutional products, such as ETFs, shape cryptocurrency market behavior, offering valuable insights for regulatory frameworks and investor strategies.

Saad Alshammari, Rihab Bedoui Ben Salem, Joslin Girar, Khaled Guesmi · 5 authors

Purpose This paper aims to delve into the intricate dynamics of the Bitcoin market, combining established financial theories with innovative methodologies to assess market efficiency and identify anomalies. Design/methodology/approach The paper investigates the efficiency of the Bitcoin market through a diverse set of lenses, using statistical methods such as linear and rank correlations, mean absolute error, mean squared error and introducing a unique copula-based approach for modeling dependence structures. The authors explore the weak form of informational market efficiency, focusing on the period before and after 2014. Findings Notable findings from this study include evidence of partial inefficiency, the emergence of anomalies, and the presence of predictability, challenging the assumption of a pure martingale. Structured into sections reviewing relevant literature, outlining this empirical methodology, presenting robust empirical results and concluding with insights and implications, this paper contributes to a deeper understanding of Bitcoin’s market behavior. Originality/value Despite the extensive literature on market efficiency, the Bitcoin market remains relatively unexplored. This study addresses this gap, offering a nuanced analysis that goes beyond traditional measures. This work emphasizes the relevance of adopting innovative approaches to assess market efficiency in a rapidly evolving financial landscape.

Bayu Avrianto Raksakadarma, Raden Aswin Rahadi

This study investigates the safe-haven and diversification roles of gold and Bitcoin in financial markets from 2015 to 2025. With rising economic uncertainties, the need for reliable safe-haven assets has become critical. Gold has historically provided stability during crises, whereas Bitcoin's volatile nature raises questions about its reliability as a safe haven. Through empirical analysis, including GARCH models and quantile regression, the research evaluates the performance of both assets during market stress. Findings indicate that gold consistently outperforms Bitcoin in terms of downside protection, confirming its status as a traditional safe-haven asset. Conversely, Bitcoin offers potential diversification benefits, enhancing portfolio performance when combined with gold. The results support the notion that integrating both assets can lead to improved risk-adjusted returns, making a dual-asset investment strategy a practical approach for investors navigating market uncertainties.

Chaker Aloui, Hela Ben Hamida, Intissar Grissa

No abstract is available for this record.

Mohamed Amine Nabli, Ikrame Ben Slimane, Haykel Hamdi

Purpose This study explores the quantile-on-quantile connectedness between major European listed football clubs and Bitcoin, providing a deeper understanding of their interdependencies. The selection of these assets is motivated by their prominent roles in both financial and sports markets, especially during periods of market volatility. By employing advanced portfolio optimization strategies, the research examines how these strategies enhance resilience and effectively manage risk during periods of market volatility. Design/methodology/approach Utilizing the quantile-on-quantile connectedness framework by Gabauer and Stenfors (2024), a robustness test is conducted using Quantile Granger Causality analysis by Jeong et al. (2012). Optimal investment portfolios are constructed using three strategies: Minimum Variance Portfolio (MVP), Minimum Correlation Portfolio (MCP) and Minimum Connectedness Portfolio (MCoP). The research analyzes a decade of data (2014–2024) from major European listed football clubs. Findings Results demonstrate that inversely related quantiles exhibit stronger total connectedness than directly related ones, highlighting the importance of managing tail risks. Bitcoin displays characteristics of a safe-haven asset during market downturns, yet under specific conditions, it can act as a shock transmitter for clubs such as Juventus and Olympique Lyonnais. Portfolio analysis indicates that Bitcoin serves as a critical diversification tool, with its optimal allocation varying across different strategies. Research limitations/implications These findings provide important insights into the dynamic relationship between football clubs and Bitcoin, offering practical implications for investors and portfolio managers. This study’s focus on market volatility and tail risks highlights Bitcoin’s role in improving portfolio resilience, enabling more informed decision-making in investment strategies. Originality/value This study contributes to the existing literature by exploring the novel interplay between European football clubs and Bitcoin using quantile-based connectedness analysis. It underscores the strategic role of Bitcoin as a diversification tool, offering valuable insights into risk management and portfolio optimization in dynamic financial markets.

Lamia SEBAI, Jahmane Abderrahman, K. M. Rezaul Karim

This paper analyses the relationships between the volatilities of five major stock markets (S&P 500, CAC 40, DAX, FTSE 100, and Nikkei 225) and five cryptocurrencies (Bitcoin, Dash, Ethereum, Monero, and Ripple), (WTI), and gold. The GARCH model, which describes the volatility of financial assets and cryptocurrencies, was used. A significant and higher volatility spillover was observed across these market pairs. The conditional correlation between Bitcoin and other cryptocurrencies is time-varying, but the conditional correlations between crypto-currencies and gold and all assets are negative during the period (2017-2018) and positive. At the beginning of the COVID-19 crisis, the conditional correlation between cryptocurrencies, stock indices, and WTI increased, which confirms the impact of COVID-19 related contagion between them.Our findings show that cryptocurencies and gold are considered hedges for the international investors during the period 2017-2018.

Anshul Agrawal, Sanjeev Kadam, Mohd Afjal

Predicting Bitcoin prices has always been challenging due to its high volatility and lack of linearity, and this challenge becomes even more pronounced in the case of market disruptions. In this context, this paper investigates the appropriateness of four machine learning models in forecasting, namely XGBoost, Long Short-Term Memory, Bagging Ensemble, and Stacking Ensemble, during two recent Black Swan periods, such as the COVID-19 pandemic and the Russia–Ukraine war. For this purpose, a dataset of 1240 Bitcoin daily closing prices from February 23, 2020, to August 8, 2023, was considered for the prediction purpose. The next day, Bitcoin prices were forecasted, and the prediction accuracy was measured against root mean square error and mean absolute percentage error. The results revealed that the Bagging and Stacking Ensemble was the most precise model during both Black Swan events. In contrast, Long Short-Term Memory (LSTM) and XGBoost were the most and least accurate, respectively. The finding indicates the robustness of ensemble-based approaches to coping with financial uncertainty. The study is instrumental because it allows comparing multiple models during extreme conditions, which provides vital insights for traders, analysts, and policymakers striving to identify the most accurate sources. Finally, this study contributes to the limited body of AI-driven financial forecasting literature focusing on models during actual Black Swan events instead of hypothetical situations.

Dimitrios Koutmos, Samet Günay, James E. Payne

No abstract is available for this record.

Jesús Molina‐Muñoz, Andrés Mora‐Valencia, Javier Perote

No abstract is available for this record.

Burhan Erdoğan

This study analyzes the time-varying interactions among assets in the digital financial asset market. Within the scope of the study, 1,820 daily observations from the 2020–2025 period for Ethereum, Ripple, Binance Coin, Cardano, Stellar, IOTA, Stacks, and Chainlink are examined using the Generalized R² method proposed by Balli et al. (2023). This approach reveals both contemporaneous and lagged interconnectedness between assets, thereby enabling an understanding of how dynamic relationships evolve over time. The results indicate that market interconnectedness is not stable over time and that the transmission of shocks tends to intensify particularly during periods of uncertainty. The findings show that Ethereum maintained a central role throughout the analysis period, while Cardano, STX, LINK, and IOTA were more exposed to shocks. These results underscore the necessity of policy frameworks that address not only individual asset risks but also contagion risks to promote market stability. From an investor’s perspective, it is recommended that portfolio compositions consider both contemporaneous and lagged effects.

B. Cappiello

This chapter examines the intersection of blockchain technology and its environmental impact, focusing on the energy-intensive validation protocols underlying blockchain systems. It provides a brief overview of blockchain technology and non-fungible tokens (NFTs), highlighting their unique characteristics and growing popularity. The transition from proof of work (PoW) to proof of stake (PoS) is analyzed in terms of their differing environmental footprints. The chapter explores climate change legislation from the United Nations Framework Convention on Climate Change (UNFCCC) to the Paris Agreement and offers an overview of European climate policies. It then assesses emerging legislative trends in the European Union, the United States, and China concerning blockchain’s environmental impact. Finally, it evaluates the legitimacy of PoW and PoS mechanisms within the framework of international and European climate regulations, offering insights into aligning blockchain technology with global sustainability goals.

Seyedeh Fatemeh Rokni, Mohammad Yavari

ABSTRACT Non‐fungible tokens (NFTs) are digital assets that represent the ownership of unique items that can be bought or sold using cryptocurrency. This comprehensive analysis of NFT involved a literature search conducted in February 2025. A total of 963 publications were initially identified from the Scopus database. Following meticulous screening, analysis, and evaluation, 734 relevant and high‐quality documents were selected for further examination, employing rigorous discussions, voting, and critical appraisal. The literature review on NFTs highlighted several frequently co‐occurring keywords, including Blockchain, Smart Contracts, Commerce, Ethereum, Digital Assets, Metaverse, Decentralization, Digital Storage, Cryptocurrency, and Distributed Ledger. This study organizes NFT research into 10 distinct categories through a combination of review and text mining techniques. These categories include “pricing, marketing, and investment,” “application of NFTs,” “art,” “games and metaverse,” “benefits, drawbacks, and review papers,” “security”, “law and ownership,” “system for NFT and extending of NFT,” “Supply chain management,” and “AI.” For each category, the research questions and their corresponding answers are mapped. Additionally, the study developed a comprehensive framework to establish connections between research categories, providing valuable insights into expanding NFT adoption. Finally, this research investigates the challenges associated with the application of NFTs and explores potential future research directions.

I-Chan Chiu, Mao-Wei Hung, Kuang‐Chieh Yen

No abstract is available for this record.

Lê Thanh Hà

No abstract is available for this record.

Ahlem Lamine

This study provides an in-depth analysis of the dynamic connectedness between G7 stock market indices, traditional cryptocurrencies (Bitcoin, Ethereum), gold, digital gold (PAXG, XAUT), and companies specializing in artificial intelligence (AI). Covering the period from 2020 to 2024, the analysis focuses on four distinct periods: the COVID-19 pandemic, the Russia-Ukraine conflict, the banking crisis triggered by the collapse of Silicon Valley Bank in March 2023 and the speculative rise in the gold markets in 2024. The methodology employs a Quantile Vector Autoregressive (QVAR) connectivity approach, starting with the median quantile and systematically extending to various quantiles to capture the entire distribution of connectedness under different market conditions. Our results reveal significant fluctuations in the Total Connectivity Index (TCI) during the studied crises and demonstrate how the roles of key assets—Bitcoin, Ethereum, gold, PAXG, XAUT, and AI firms—shift between being net emitters and receivers of shocks. These shifts underscore the importance of asset selection in crafting effective hedging strategies. Specifically, we observe that G7 investors adopt varying diversification strategies depending on their domestic market conditions and the specific crisis period. The study highlights that assets for diversification and risk reduction vary by country and crisis. Traditional cryptocurrencies and AI companies in general emerge as promising diversification tools, especially in times of technological disruption and economic uncertainty. Several financial implications for investors and policymakers are proposed, providing insights for optimizing portfolio resilience in the face of global market volatility.

Sisira Colombage, A.A.K.K. Jayawardhana, Giles Oatley

This study examines links between global financial stress and cryptocurrency returns from 1 January 2017 to 31 January 2025, while explicitly accounting for commodity markets. We use an econometric toolkit: unit-root and cointegration testing, ARDL bounds, Toda–Yamamoto causality, and a two-state Markov Switching model to trace long-run equilibrium and transmission mechanisms across cryptocurrencies (BGCI), systemic stress (OFR-FSI), volatility measures (VIX, VVIX, VSTOXX, VVSTOXX, MOVE), major equities and bonds, and three commodities (gold, oil, copper). Results show robust long-run cointegration between BGCI and several financial variables, including S&P/ASX 200 and the Bloomberg Barclays Bond Index; models that include commodities continue to support these long-term links. Toda–Yamamoto tests reveal that stress and volatility indices unidirectionally transmit shocks to cryptocurrencies and commodities, while gold displays a bidirectional relationship with BGCI, indicating a conditional safe haven interaction. Markov Switching estimates show amplified co-movement among BGCI, gold and bonds in stress regimes, with the model predominantly remaining in a normal state. Overall, cryptocurrencies are embedded within the broader financial system; commodities, especially gold, are used to moderate the stress crypto transmission and offer conditional diversification value during turmoil.

Ahmad Al Izham Izadin, Rosylin Mohd Yusof

Purpose This study aims to examine the spillover effects between cryptocurrencies, stablecoins and Islamic stock indices, focusing on their interactions during crises. Islamic stocks have historically been more resilient during crises due to their adherence to Shariah principles. Time-varying parameter vector autoregression (TVP –VAR) method will allow the study to look at the spillover over time. Design/methodology/approach Using the TVP–VAR model, the study analyzes daily return data from January 1, 2018 to September 30, 2024. The data includes Bitcoin (BTC), Ethereum (ETH), Tether (USDT) and large-, mid- and small-cap Islamic stock indices. Findings The findings reveal that cryptocurrencies such as BTC and ETH exert stronger spillover effects on Islamic stock indices compared to stablecoins like USDT). However, Islamic stocks remain largely insulated from crypto spillovers, with any spillovers predominantly flowing from stocks to cryptocurrencies. Large-cap Islamic stocks are more susceptible to cryptocurrency spillovers, whereas mid- and small-cap stocks tend to transmit more volatility to crypto assets. Crises amplify these effects, with COVID-19 causing a sharp but short-lived impact, while the 2022 crypto crash led to more prolonged and intense spillovers, particularly affecting large-cap stocks. Notably, stablecoins exhibited no measurable spillover effects on Islamic stocks during the 2022 crypto crash, reinforcing their role as stabilizers in volatile markets. This reinforces the role of stablecoins and Islamic stocks as a relatively stable investment during crypto-related crises. Practical implications The findings suggest that Islamic stock investors face lower exposure to cryptocurrency crisis volatility. Furthermore, stablecoins could be a valuable addition to Islamic investment portfolios. Originality/value This study expands the limited understanding of how stablecoins influence Islamic stock markets. It adds further depth by examining spillover effects across different market capitalizations.

Florentin Şerban, Silvia Dedu

Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.

Pratik Biswas, Chandan Sharma

No abstract is available for this record.

Yuankui Wang, Mohd Fahmi Ghazali, Ruzanna Ab Razak, Mohd Azlan Shah Zaidi

This study applies Phase Space Reconstruction and Phase Space LSTM to analyze Bitcoin’s interactions with Gold, S&P 500, U.S. Bonds, EUR/USD, and Crude Oil, revealing hidden dependencies and chaotic structures in financial markets. Study implement a multi-method validation framework combining the Rosenstein algorithm for Lyapunov exponent estimation, 0 − 1 test for chaos and BDS test to provide robust evidence for deterministic chaos. Results indicate that most assets exhibit deterministic chaos, with price evolution highly sensitive to liquidity conditions and macroeconomic forces. Phase space analysis conducted in optimal four-dimensional embeddings uncovers stronger predictive linkages between Bitcoin and U.S. Bonds, reinforcing its growing dependence on global financial conditions. The application of PS-LSTM significantly enhances forecasting accuracy, demonstrated through rigorous validation including statistical significance testing and economic significance evaluation using risk-adjusted performance metrics. These findings suggest that cryptocurrencies are not isolated assets but deeply entangled with systemic financial fluctuations, necessitating a reassessment of market stability and risk propagation through the lens of statistical mechanics and econophysics. • PSR reveals hidden dependencies across Bitcoin, gold, stocks, bonds, exchange rate and commodities. • Phase space analysis reveals that Bitcoin-bond linkages indicate macroeconomic integration. • Phase Space LSTM (PS-LSTM) enhances forecasting accuracy, reducing overfitting and improving predictive stability across all assets. • PS-LSTM reduces overfitting and improves forecasting across all asset classes. • Chaos detection confirms the presence of nonlinear dynamics in cryptocurrency and commodity markets. • Higher-dimensional embeddings enhance the detection of causality between financial assets.

Tanya Wadhwani

Taxation of Non-Fungible Tokens (NFTs) is a topical challenge in the emerging global digital economy. NFTs, being individual blockchain assets symbolizing ownership of art, music, gaming collectibles, or virtual property, are difficult to fit into traditional categories of property, securities, or commodity law. India's Finance Act 2022 brought a framework for taxing Virtual Digital Assets (VDAs) including NFTs, by levying a flat 30 per cent tax on income arising on their transfer and a 1 per cent Tax Deducted at Source (TDS). This legislative move, making NFTs a part of the official economy, has at the same time created ambiguity around valuation, fairness, and the larger digital innovation implications. The present paper embarks on an in-depth analysis of NFT taxation in India by putting the Finance Act 2022 within the overall policy context of economic formalisation. It analyzes the complexities of valuation, wherein subjective determination of price and unstable market conditions restrict even-handed assessment; risks of double taxation, especially where royalties, resale profits, and GST overlap; complexities of cross-border enforcement in decentralized blockchain transactions; and compliance burden on investors, creators, and exchanges through compulsory TDS deductions. These bring out the inflexibility and shortfalls in the current framework. As a backdrop to understand India's strategy, the paper contrasts global practices. The United States has released Internal Revenue Service (IRS) guidance that indicates NFTs can be considered collectibles and taxed with a premium capital gains rate. The United Kingdom uses principles of capital gains tax to the transfer of NFTs, with the aim of equity with other assets. Singapore has exempted some NFT transactions from Goods and Services Tax (GST), which is a forward-thinking move, while the European Union is still considering a harmonized digital tax code under its proposed reforms of the digital economy. These cross-country views expose that India's flat-rate policy is an extreme outlier in its harshness, threatening to drive creators and investors to more innovation-friendly places. The paper also addresses wider policy issues, such as whether NFTs are subject to capital gains rules or a general rate of tax, whether authentic creators should be distinguished from speculative traders, and how international cooperation, especially under the OECD's digital tax efforts, could reduce cross-border revenue losses. Pursuant to these analyses, the report provides in-depth recommendations: the introduction of transparent and standardized valuation guidelines for NFTs; differentiated treatment for creators to foster innovation; relief from compliance burdens by amending the 1 per cent TDS; consideration of bilateral tax treaties to cover cross-border NFT transactions; and a phase-wise movement towards a capital gains tax model. By plugging a major research lacuna in Indian juristic literature, this article contends that though India's taxation of NFTs is an important starting point, the existing architecture is incomplete and unbending. What is needed is a remodeled approach one that scales the state's interests with the imperative to promote a dynamic digital economy.

Imran Khan, Sami Ur Rahman

No abstract is available for this record.