Blockchain Papers

Wei Yin, Fan Wu, Peng Zhou, Berna Kirkulak-Uludag

No abstract is available for this record.

Lingshan Du, Ji Shen

ABSTRACT We propose a novel option pricing model that explicitly incorporates volatility‐of‐volatility (VOV) dynamics and its associated risk premium. Our framework integrates realized variance and realized quarticity to capture latent VOV dynamics, addressing key challenges in cryptocurrency option pricing. Using Fourier inversion methods, we derive a closed‐form pricing formula for European‐style options. Empirical analysis with high‐frequency cryptocurrency option data shows that our model improves pricing accuracy, reducing implied volatility errors by 8.55% compared to benchmark models. The model outperforms benchmarks across all moneyness levels, remains robust for both short‐ and long‐maturity contracts, and maintains accuracy under high volatility. This study contributes to the literature by introducing a tractable and empirically validated approach to cryptocurrency option pricing through explicit VOV modeling.

Satyaban Sahoo, Deepti Singh

This study employs novel quantile time-frequency connectedness approach to explore the dynamic connectedness among sustainable assets (sustainable, green bond, and clean energy index), traditional assets (traditional index and crude oil), and cryptocurrency. This method assesses the impact of uncertain events on asset relationships. Findings indicate median connectedness of 36.94% in the short run and 4.81% in the long run, with short-term dynamics dominating system transmission. The traditional index is the primary transmitter of short-run shocks, while the green bond index leads in long-run shocks. Diversification across asset classes is recommended for effective hedging and optimal returns during extreme market conditions.

Arvind Awasthi, Mariyam Shaukat

Purpose : The purpose of our study was to examine the property of long memory in the mean and volatility of daily returns on two major cryptocurrencies – Bitcoin and Ethereum – over the period ranging from January 1, 2017, to December 1, 2017. The growing body of research on this relatively new asset class, cryptocurrencies, motivated us to conduct this study. Methodology : We used autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) models to study long-memory in both mean returns and volatility of Bitcoin and Ethereum. The study also employed Bai–Perron and Quandt–Andrews tests to identify structural breaks in both cryptocurrencies, allowing for a more detailed examination of the property of long-memory in sub-samples. Findings : Our results confirmed the absence of long memory in mean returns on Bitcoin for the whole sample period as well as both sub-sample periods, implying that its market is efficient. Mean returns on Ethereum exhibited long memory over the complete sample period; however, the sub-sample analysis revealed a shift toward market efficiency, as long memory was not present in the second sub-sample. The results from FIGARCH analysis confirmed the prevalence of long-memory in the volatility of returns on both Bitcoin and Ethereum. Implications : The mean returns for both cryptocurrencies did not show persistence in the second sub-sample period, which was characterized by heightened economic uncertainty. This implies that external events did not have predictive power over cryptocurrencies. However, the presence of long memory in volatility implied that past volatility levels affected present volatility levels in both cryptocurrencies. Therefore, investors and policymakers could use volatility predictions to assess riskiness in their portfolios and the cryptocurrency market to formulate regulations, respectively. Originality : The paper contributed to the rather sparse body of literature pertaining to properties of financial time series with respect to cryptocurrency. An important contribution of this paper is to provide a comprehensive study, accommodating a structural break, over a period that includes periods of low and high economic uncertainty.

Shahar Somin, Yaniv Altshuler, Alex Pentland

Blockchain technology, once limited to niche technological communities, has seen widespread global adoption in recent years, with the potential to reshape financial and social systems. Launched in July 2015, the Ethereum blockchain introduced programmable Smart Contracts. This innovation enabled the creation of user-defined crypto-assets adhering to the ERC-20 standard, supporting a wide range of decentralized applications beyond simple value transfer. We present a large-scale, temporally annotated dataset of ERC-20 token transactions recorded on the Ethereum blockchain. Spanning from November 2015 to December 2024, the dataset encapsulates the trading activity of 216,336,529 users trading 1,138,136 unique tokens, offering a detailed view of crypto-market activity over time. Uniquely, it enables the analysis of a financial ecosystem from its inception, providing rare insights into its structural evolution, participant dynamics, and emergent behaviors. As the largest publicly available resource of its kind, it supports research in blockchain analytics, market dynamics and temporal network analysis. The full dataset and accompanying code are released for public use.

Long Guo, Li-Xin Zhong

No abstract is available for this record.

Alessia Galdeman, Lucio La Cava, Matteo Zignani, Andrea Tagarelli · 5 authors

The rapid growth of Non-Fungible Tokens (NFTs) and the extensive trading activities associated with such an intriguing domain led to the emergence of large-scale and interconnected transaction networks involving the most prominent NFT markets. Despite such interdependencies representing an inestimable source of information for the proper understanding of the NFT landscape, previous studies treated each market separately, overlooking relevant phenomena. In this study, we explore a multilayer network modeling approach to analyze transactions in multiple NFT markets. We reveal previously unnoticed macroscopic and mesoscopic traits by investigating indicators that discern whether markets are independent or linked: users trading NFTs are organized in cross-market communities where multi-market users act as bridges across marketplaces, adapting to the diverse nature of the markets they operate in. We also conduct an in-depth examination of such multi-market users, studying their specific activity patterns that leave a distinctive mark on the system: the majority of multi-market users well differentiate their earnings and expenses among the markets, while a fraction of them is directed toward a more polarized money allocation based on the typology of the markets. By offering a fresh perspective on this intricate financial system and emphasizing the importance of perceiving the NFT markets as a unique and interconnected world, our study paves the way for further contributions aimed at unraveling the complexity of cryptosystems and understanding the latent phenomena across NFT markets.

Mateus Gonzalez de Freitas Pinto

We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we evaluate the fit of two competing models of market microstructure: the Mixture of Distributions Hypothesis (MDH) and the Intraday Trading Invariance Hypothesis (ITIH). Using intraday data at different levels of aggregation, we investigate the relationship between return volatility per transaction and trade size. We find evidence favoring the MDH in the crypto futures market.

Silvia Edelweiss Crusco dos Santos, Hélder Sebastião, Nuno Silva

Using daily data from November 9, 2017 to December 31, 2022, this paper uses Granger causality in the mean and the distribution to investigate the transmission of information between return, volume, volatility, and illiquidity for Bitcoin and the nine most important altcoins in terms of market capitalization. Additionally, the forecastability of Bitcoin returns is examined using linear models with different predictor spaces estimated using LASSO and the performance of several trading strategies devised upon those forecasts is assessed. The causal relationships between returns, volumes and volatilities of Bitcoin and each altcoin are more evident in the left tail of the distribution, where Bitcoin acts mostly as a transmitter of information, and in the right tail for causality regarding illiquidity. In bullish markets, Bitcoin acts mostly as a receiver of information. The best Bitcoin trading strategy is based on the model which incorporates the information on all cryptocurrencies, exhibiting a cumulative return of 331% and an annualized Sharpe ratio of 94.59%, considering an enter/exit threshold of 0.25% and after 0.5% round-trip transaction costs. These results are statistically significant when compared with the buy-and-hold strategy, which renders a cumulative return of 121% and a Sharpe ratio of 64.74%. These results point out the importance of considering information from other cryptocurrencies to forecast and trade on Bitcoin.

Gilles Brice M’bakob

No abstract is available for this record.

V. V. Dikovitsky

Introduction This article presents a method for short-term cryptocurrency price forecasting utilizing news headlines. Methods The study analyzes the impact of news on asset prices within one hour of publication, employing machine learning-based classification with BERT and GPT models, as well as GloVe vector representations. Results The proposed cascade classifier model enhances prediction accuracy by initially assessing the strength of a news item and subsequently forecasting the direction of price movement. Experimental results demonstrate the effectiveness of the developed classification model. Discussion The model achieves an accuracy of 79% in predicting price movements, confirming the potential of leveraging news headlines to improve short-term forecasts in cryptocurrency markets.

Young-Sung Kim, Dong‐Jun Kim, Sun‐Yong Choi

No abstract is available for this record.

Yingying Huang, Weizhong Liang, Kun Duan, Andrew Urquhart · 5 authors

No abstract is available for this record.

Kezban Hitay, Adem Anbar

This study investigates Granger-causality relationships between crypto-assets (Bitcoin and Ethereum) and traditional financial assets (stock indices and exchange rates) in BRICS-T countries over the 2016–2024 period. The findings highlight significant interlinkages: bidirectional causality exists between Bitcoin and Russia's stock market, and between Ethereum and both Brazil's stock market and the USD/INR exchange rate. Unidirectional causality is observed from Bitcoin to the stock markets of Brazil, India, and China, while the USD/TRY exchange rate influences Bitcoin. Similarly, Ethereum affects the stock markets of Russia, India, and South Africa, while the USD/TRY exchange rate also Granger-causes Ethereum. These results indicate a growing synchronization between crypto-assets and conventional financial markets. The presence of both unidirectional and bidirectional causalities emphasizes the increasing integration of global financial systems and highlights the importance for investors to consider cross-market interactions when making decisions. Crypto-assets are no longer isolated but are embedded in broader financial dynamics.

Fangfang Zhu, Shaojun Fu, Xiangdong Liu

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets.

Jonathan Blackledge, Anton Blackledge

Cryptocurrencies like Bitcoin can be considered commodities under the Commodity Exchange Act (CEA) and the Commodity Futures Trading Commission (CFTC) has jurisdiction over cryptocurrencies considered to be commodities, particularly in the context of futures trading. This paper presents a method for long and short term trend prediction of certain cryptocurrencies which is predicated on an application of the Fractal Market Hypothesis. This is an area of market theory where the self-affine properties of a fractal stochastic field are used to model a financial time series. After an introduction to the underlying theory and mathematical modelling, a fundamental analysis of Bitcoin and Ethereum to U.S. Dollar exchange markets is conducted. This analysis is based on a consideration that a changes in polarity of the 'Beta-to-Volatility' and the 'Lyapunov-to-Volatility' ratios to indicate an impending change to the Bitcoin/Ethereum price trend signal. This is used to recommend a long, a short or a hold trading position for which algorithms are provided (coded in Matlab) and 'back-tested'. An optimisation of these algorithms is conducted, leading to a strategy for implementing an ideal range of the key parameters for 'driving' the algorithms developed. This is based on maximising the accuracy and profitability to assure a high level of confidence. The application of the trading strategy developed through this approach is demonstrated to provide useful information to aid cryptocurrency investments and quantify the likelihood that the market will become bull or bear dominant. Under stable conditions, Machine Learning (using the 'TuringBot') is shown to provide useful estimates of future price values and/or fluctuations over small event horizons in time. This minimises any \lq trading delay' caused by filtering the data and increases returns by providing optimal trade positions within a \lq micro-trend' that is too fast for detection otherwise. In certain cases, this increase can reach ~10%. The results presented confirm that Bitcoin and Ethereum exchanges are self-affine (fractal) stochastic fields with L\'evy distributions, displaying a Hurst Exponent of ~ 0.32, a Fractal Dimension of ~ 1.68 and Levy Index of ~1.22. They also confirm that the Fractal Market Hypothesis and its indices provide a suitable market model, that generates returns on investments that outperform all Buy and Hold strategies based on more standard market indices.

Efe Çağlar Çağlı, Thomas Dimpfl

No abstract is available for this record.

Nader Naifar

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets.

Jordan Ali, Gili Vidan

Axie Infinity is a blockchain-based video game offering players the chance to earn crypto tokens in exchange for their time spent playing the game. During the COVID-19 lockdowns, the game's popularity surged alongside the crypto market and stories of early adopters’ quick returns on investments circulated among online crypto and Web3 communities. As the game's rapidly growing userbase plateaued, the community experienced several growth-related crises, one of which saw the value of the game's tokens crash. But players were not passive victims of these developments. They responded by creating a “scholarship” program to secure the flow of new players to the platform and actively commented on their commitment to the “grind” of playing the game to recoup their investments. This article treats the trajectory of Axie Infinity as both an exemplar case study of broader dynamics in the crypto gaming landscape—a process we call the economization of play —and as a unique site in which players were not simply duped by the promise of the game, but were responding to crises proactively with risk mitigating and rationalizing strategies.

Bilgehan Teki̇n

Abstract This research examines the dynamics of the non-fungible tokens (NFT) market by utilizing key financial metrics such as Bitcoin prices, the Crypto Fear-Greed Index, and DeFi indicators. It analyzes NFT-USD values, the Crypto Fear-Greed Index, total value locked in DeFi, and Bitcoin interactions between February 2021–July 2023. Employing ordinary least squares regression, quantile regression, Johansen cointegration, and VECM Granger analysis, the study uncovers complex relationships shaping the NFT market. The findings reveal a positive correlation between Bitcoin prices and NFT values, a negative relationship between total value locked in DeFi and NFT values, and an inverse connection between the Crypto Fear-Greed Index and NFT values. Additionally, cointegration exists among the variables, and causality analysis indicates that Bitcoin influences total value locked, while shifts in the Crypto Fear-Greed Index reflect market sentiment changes. These insights contribute to a deeper understanding of behavioral finance by illustrating how psychological factors, such as investor sentiment and the bandwagon effect, interact with digital asset markets. From a practical perspective, the results emphasize the importance of recognizing these interdependencies for policymakers and market participants striving to foster innovation in the rapidly evolving NFT ecosystem. By aligning with the transformative potential of Blockchain and DeFi, this study provides strategic insights for optimizing resource allocation, enhancing market efficiency, and shaping regulatory frameworks within innovative financial landscapes.

Aleksey Kolokolov

This article proposes a new nonparametric test for detecting short-lived locally explosive trends (drift bursts) in pure-jump processes. The new test is designed specifically to detect intraday flash crashes and gradual jumps in cryptocurrency prices recorded at a high frequency. Empirical analysis shows that drift bursts in bitcoin price occur, on average, every second day. Their economic importance is highlighted by showing that hedge funds holding cryptocurrency in their portfolios are exposed to a risk factor associated with the intensity of bitcoin crashes. On average, hedge funds do not profit from intraday bitcoin crashes and do not hedge against the associated risk.

Rohit Asthana, Niti Nandini Chatnani

Cryptocurrency has gained increasing prominence in the recent past with its increased integration with established financial markets like stock markets, currency markets and so on. This study examines the integration of cryptocurrency and the Indian stock market. By employing time-varying parameter-vector autoregression (TVP-VAR), the study analyses the daily closing data of Sensex, Nifty 50, Bitcoin and Ethereum over a 41-month period from 1st April 2020 to 31st August 2023 to find out the total connectedness, pairwise connectedness and to identify the net receivers and transmitters. The findings reveal a moderate unidirectional transmission from the cryptocurrency market to the stock market, thereby confirming cryptocurrencies as net transmitters and stock market indices as net receivers. This study is of relevance to policymakers in devising an appropriate cryptocurrency regulatory framework and to investors in designing better portfolios.

Alisa Kalacheva, Pavel Kuznetsov, Igor Vodolazov, Yury Yanovich

The rise in cryptoasset valuations and the ease of creating new tokens have spurred an increase in illicit activities within the market. Decentralized exchanges (DEX) facilitate the trading of a vast array of tokens, including those with minimal liquidity, amplifying the risk of fraudulent schemes. Fraudulent practices take various forms, including counterfeit tokens, rug pulls, and pump-and-dump schemes, all lacking functional innovation and relying heavily on aggressive social media marketing. This study contributes to the identification and profiling of deceitful tokens on DEX platforms. Our approach involved compiling on new tokens with an active trading start and attracted competition to buy them in first blocks spanning multiple years from the Ethereum blockchain, tracking all associated purchase and sale transactions. Our analysis revealed that Uniswap V2 predominantly hosts the trading of new tokens, with an alarming discovery that over 98% of tokens minted daily exhibit fraudulent characteristics. Subsequently, a machine learning model was developed to predict the likelihood of a rug pull occurring shortly after trading commencement. Although the dataset labeling methodology and detection problem statement are exploratory, we demonstrate the economic significance of the proposed approach within trading pipeline. The findings highlight the importance of identifying fraudulent activities and emphasize the need for collaboration between decentralized exchanges and regulatory bodies to mitigate financial losses for investors.

Abdulkadri Toyin Alabi, Abdullahi Ishola

The environmental impact of cryptocurrencies has attracted increasing scrutiny, largely due to the high energy consumption of blockchain networks. However, empirical research on the causal relationship between cryptocurrency trading activity and carbon emissions remains scarce. This study addresses this gap by analysing the dynamic interplay between cryptocurrency trading and CO₂ emissions for Bitcoin, Ethereum, and Binance Coin, using monthly data from January 2015 to September 2024. Employing the Toda-Yamamoto augmented Granger causality approach, we apply logarithmic transformations to ensure data stationarity and address integration and endogeneity concerns. Our results reveal a bidirectional Granger causality between Bitcoin trading and CO₂ emissions, suggesting a feedback loop between market activity and environmental impact. For Ethereum, we find a similar albeit weaker bidirectional causality from trading to emissions, while no significant causal link is detected for Binance Coin, likely reflecting its more energy-efficient consensus mechanism. These findings highlight the disproportionate environmental burden of proof-of-work cryptocurrencies and underscore the need for targeted regulatory responses. We recommend the adoption of carbon-sensitive crypto policies, such as mandatory energy usage disclosures and incentives for transitioning to sustainable consensus mechanisms. This study advances the environmental finance literature by providing robust empirical evidence on the links between digital asset markets and carbon emissions.