This study substantiates blockchain analytics as a specialized expert tool for detecting the legalization of criminal proceeds under wartime conditions. The purpose is to systematize the methodological foundations of distributed ledger forensics and develop a conceptual model for its integration into Ukraine’s financial monitoring system. The implementation involves a comparative analysis of scholarly sources and a review of international regulatory standards in the field of anti-money laundering. Graph neural networks ensure an accuracy of 91 to 96 percent in detecting illicit transactions, and the dominant schemes for laundering wartime proceeds are sanctions arbitrage through stablecoins, fund mixing, and DeFi-based legalization through decentralized protocols. The immutability of records in the distributed ledger creates a unique evidentiary environment that enables retrospective analysis of transaction chains even after laundering operations have been completed. The findings confirm the necessity of fully implementing FATF Recommendation 15 and establishing specialized crypto-forensics units within the structure of domestic law enforcement agencies. The proposed four-level model, encompassing data collection, graph analysis, scheme identification, and evidence formation, defines a practical path toward standardizing crypto-forensics in domestic forensic expert practice and improving the effectiveness of financial investigations.
Open access
Business and Economic Development
Legal, Health, Environmental and COVID-19 Challenges
Miners of proof-of-work networks like Bitcoin tend to gravitate towards regions with cheap energy. We analyze risks associated with this geographical centralization by exploiting a local electricity supply shock. Compared to a control group consisting of an energy-efficient proof-of-stake cryptocurrency, the blockchain’s capacity for processing transactions decreases while transaction fees increase substantially. The increased settlement latency on the blockchain also reduces secondary market quality as seen in higher exchange rate volatility, lower liquidity, and larger price differences between exchanges. Overall, our results suggest that geographical centralization poses short-lived but potentially severe system-wide risks to proof-of-work networks.
We investigate the evolving structure of interactions in cryptocurrency markets using a network-based framework constructed from high-frequency price data spanning 2020-2025. Directed and weighted networks are constructed from statistically significant Granger causal relationships between cryptocurrency log-returns, enabling us to quantify the flow of influence across assets. We find that normalized returns exhibit heavy-tailed distributions, consistent with the presence of large intermittent fluctuations and in line with stylized facts of financial markets. The resulting networks display pronounced heterogeneity in link weights and nodal strengths, indicating that a small subset of cryptocurrencies contributes disproportionately to market dynamics. By ranking cryptocurrencies based on their nodal out-strength, we uncover a dynamically evolving hierarchy of influence. Ethereum consistently emerges as the most influential asset, while Bitcoin shows a gradual decline in its relative importance. The ranking structure exhibits substantial temporal variability, with multiple cryptocurrencies entering and exiting the top positions over time. Our findings reveal a highly competitive and non-stable organization of the cryptocurrency ecosystem.
Kanan Huseynov, Ali Shahzaib, István András Seres, János Tapolcai
From a user's perspective, perhaps the most significant difference between traditional banking services and widely used blockchain-based financial systems is that, in the latter, transactions and, either directly or indirectly, account balances and transaction histories are publicly observable. Therefore, a growing number of cryptographic solutions have been proposed to add a privacy layer to such systems. However, the privacy that users actually obtain does not depend solely on the security of the underlying cryptographic protocol: user behavior, transaction amount patterns, and timing decisions can substantially reduce anonymity. In this work, we study behavioral leakage in cryptocurrency mixers, focusing on Railgun on Ethereum. We aim to heuristically estimate the probability that a given deposit and withdrawal transaction belong to the same user. We consider five sources of leakage: characteristic timing patterns, address reuse, proximity in the transaction graph induced by prior public transactions, amount fingerprints that preserve distinctive digit patterns across transaction values, and knapsack type matches in which groups of transaction amounts add up in revealing ways. Our results show that even cryptographically strong privacy systems may suffer substantial anonymity loss due to user behavior and transaction patterns. Our five heuristics are able to uniquely link 17.65% of Railgun withdraw transactions to deposit transactions. We also applied a knapsack solver algorithm that was able to produce a 3.42 bit median anonymity loss for withdraw transactions. This work contributes to a better understanding of the practical privacy limits of mixers and anonymity pools, and points toward safer usage practices and design principles.
Cryptocurrencies have received long-term interest among investors because of the features of Bitcoin since its introduction in 2009. However, it is the same features that pose serious and diverse threats. These risks are very dangerous to the security of investors and the integrity of the market. Although their urgency is immense, there are very few systematic analyses that incorporate both regulatory and technological views. In this research, the mixed-method design is used, and an empirical investigation of high-profile security events is combined with the critical analysis of regulatory and technical literature in order to define, classify, and track the causes of the most widespread risks. The article explores the weaknesses and strengths of the existing laws and strategies that would curb identified risks that cryptocurrencies present. It also suggests practical and tangible solutions, which would make use of new technologies to minimize the damages and risks of cryptocurrencies to a greater extent. The analysis in this study proves that properly reducing risks should be performed in a two-faceted way; it should be done with the help of the regulation gaps in action and the utilization of new, protocol-infused technological limits. This study presents a moderate structure that is meant to achieve market security that does not suppress the dynamism and transparency of the cryptocurrency ecosystem. This study analyzes the problem of cryptocurrency security, financial regulation, blockchain technology, risk mitigation, and decentralized finance.
Anas Azenzoul, Nacer MAHOUAT, Ouissale El Gharbaoui, Jihane Tayazime · 6 authors
Tax systems worldwide face a compliance gap that OECD data places at USD 100–240 billion annually in corporate avoidance alone, before accounting for the shadow economy and crypto-asset transactions. FinTech mandatory e-invoicing, real-time transaction matching, and machine-learning audit selection is narrowing the informational conditions that enable evasion, while simultaneously introducing governance risks: opaque algorithmic audit targeting, contested blockchain forensic evidence, and the surveillance potential of programmable money. This article presents a PRISMA 2020 systematic literature review of 59 peer-reviewed articles (Scopus, Web of Science, and ScienceDirect), complemented by IRAMUTEQ lexicometric analysis and an extension of the Allingham Sandmo compliance model to incorporate algorithmic detection probabilities, bomb-crater belief dynamics, and Zero-Knowledge Proof verification. Four thematic clusters emerge: tax compliance behaviour and FinTech adoption (19.92%), digital transformation and corporate performance (35.34%), bibliometric and emerging-technology research (16.54%), and cryptocurrency markets and regulatory challenges (28.20%). Across them, FinTech reduces evasion where institutional and technical conditions allow but generates distributional, evidentiary, and constitutional risks that existing legal frameworks have yet to resolve. In response, we propose the Techno-Legal Due Process Framework (TLDPF) three pillars (Techno-Proportionality, Cryptographic Burden of Proof, and Algorithmic Constitutionalism) grounded in EU/OECD constitutional doctrine as a normative design proposal awaiting empirical validation.
In the context of developments in the field of financial technology, cryptocurrencies, emerging as a new asset class, have garnered significant attention in financial markets in recent years, attracting investors, researchers, and regulators, and leading to numerous publications. Bibliometric studies evaluate these publications based on criteria such as the number of publications, their quality, the countries of publication, authors, and journals. This study aims to perform a bibliometric analysis of the academic literature available in the Web of Science (WoS) database, focusing on the volatility of cryptocurrency prices. It analyzes the magnitude and development of academic interest in this field, along with key words, the most cited works, and research trends, in an effort to determine the density of studies, their impact areas, and the academic networks that have emerged in this field. Based on the general findings, it is observed that the number of studies has been on an increasing trend over the years, and that the publications are predominantly in the field of Business Economics. Moreover, it has been found that publications are mainly in finance journals. In terms of network maps, the findings suggest a moderate level of collaboration among authors, with the United Kingdom and the People's Republic of China occupying central positions in international collaboration. In terms of citations, authors such as Lucey, and Katsiampa, Paraskevi, have emerged as prominent figures in the fields of cryptocurrencies and volatility. Regarding key words, terms like 'cryptocurrency', 'cryptocurrencies', 'volatility', and 'bitcoin' are predominantly used in these studies." Keywords: cryptocurrencies, bitcoin, volatility, bibliometric analysis
This study compares the forecasting performance of four deep learning architectures—GRU, LSTM, RNN, and CNN—for one-step-ahead Bitcoin price prediction. A grid search determined the optimal configuration, which was applied uniformly across models to ensure fair evaluation. Using daily BTC closing prices from January 2018 to July 2025, it is found that the GRU model achieved the lowest forecasting errors (MSE, RMSE, MAE, MAPE) and the highest R², with LSTM performing closely behind. Visual analyses confirmed that GRU and LSTM maintained stronger alignment with actual prices during volatile periods. To assess economic value, model forecasts were integrated into a rule-based trading strategy under realistic market frictions, including a 0.10% transaction cost and a 0.10% trading threshold, with both short-selling-enabled and long-only variants tested. The GRU strategy with short-selling generated the highest terminal wealth (approximately 24% higher than the Buy-and-Hold benchmark) and superior risk-adjusted returns, measured by CAGR, Maximum Drawdown, and Sharpe Ratio. The findings demonstrate that careful hyperparameter optimization, coupled with an architecture capable of capturing complex temporal dependencies, can significantly improve both predictive accuracy and trading profitability in cryptocurrency markets. These results provide practical implications for designing AI-driven trading systems.
Collective dynamics in financial markets can emerge through synchronized movements of large groups of assets. Motivated by analogies with interacting many-body systems, we introduce a spin-lattice representation for analyzing collective states in cryptocurrency markets. In this framework, assets are encoded as binary spin variables according to the sign of their returns, while correlations between assets determine effective interaction strengths. A correlation-based breadth-first search (CBFS) procedure embeds 169 cryptocurrencies into a $13 \times 13$ lattice, enabling the construction of an Ising-like Hamiltonian describing the market configuration, which we call the \emph{Market Crystal}. Macroscopic observables such as magnetization and energy provide a statistical-mechanical characterization of collective market states. The resulting phase-space structure highlights regimes of strong alignment and fragmentation among assets, with an energy--magnetization pattern suggestive of predominantly ferromagnetic interactions. This framework offers a statistical-mechanical viewpoint for studying collective behavior in financial systems.
Antonio Pellicani, Gianvito Pio, Sašo Džeroski, Michelangelo Ceci
Abstract The cryptocurrency market represents a significant innovation in the financial ecosystem, built upon cryptographic principles to ensure secure and transparent transactions. Cryptocurrencies experienced a global adoption, driven by their decentralized nature that enables borderless transactions without third-party intermediaries. The price of cryptocurrencies is characterized by a significant volatility, that introduces both opportunities and challenges. In this context, the development of accurate methods for the forecasting of price variation, able to work in real-time on data streams, has become vital for various stakeholders. In this paper, we propose a novel approach, called LEMON, for the online prediction of the price variation of cryptocurrencies, that leverages possible temporal correlations among them. Our approach stems from the empirical evidence that cryptocurrencies tend to form groups characterized by similar trends, a behavior often attributed to shared market dynamics and common external factors. Through the analysis of temporal correlations, LEMON dynamically identifies these groups, that are then exploited to learn multiple multi-target tree-based models, specifically designed for processing continuous data streams. LEMON also introduces a novel adaptive non-parametric weighting scheme, that automatically adjusts the importance of each instance based on the observed data distribution in real-time, improving the forecasting of the price variation. Our experiments, performed on 16 datasets related to 16 cryptocurrencies, demonstrate that LEMON outperforms state-of-the-art approaches in two distinct prediction tasks: forecasting the closing price variation (regression) and predicting the market trend direction (classification), making it an effective tool to support stakeholders requiring accurate real-time predictions.
Objective: This study examines the legal and procedural challenges posed by decentralised finance (DeFi) technologies to the anti-money laundering framework in Iraq, The research problem lies in the clear regulatory gap resulting from the decentralised nature of these platforms, which relies on smart contract technology and blockchain to eliminate the need for traditional financial intermediaries; this decentralised nature hinders the ability of Iraq’s Anti-Money Laundering and Counter-Terrorist Financing Law No. 39 of 2015 to control cryptocurrency flows and establish criminal liability in this context,، Method: The study adopted a comparative analytical approach, analysing the text of Iraqi legislation and comparing it with the operating mechanisms of decentralised finance platforms, whilst also examining the extent to which it complies with the updated international standards issued by the Financial Action Task Force (FATF) In particular, with regard to Recommendation No. 15, Results: the study reached a number of important conclusions, the most notable of which is that the current legal definitions of funds and financial institutions in Iraq are outdated, thereby limiting the ability of regulatory bodies to track virtual assets, Novelty: The study also identified procedural shortcomings in the handling of encrypted digital evidence and recommended urgent legislative reforms, including the regulation and oversight of Virtual Asset Service Providers (VASPs) through the establishment of a dedicated institutional framework.
Penelitian ini bertujuan untuk mengevaluasi penggunaan cryptocurrency sebagai metode pembayaran zakat yang halal di Malaysia, dengan mengeksplorasi kesesuaiannya dengan prinsip-prinsip Islam sambil mempertimbangkan inovasi teknologi dan kepatuhan syariah untuk pembayaran zakat yang efisien dan transparan. Pendekatan kualitatif digunakan, melibatkan tinjauan pustaka dan analisis regulasi terkait fatwa Malaysia, peraturan keuangan, serta sumber akademis tentang keuangan Islam dan cryptocurrency. Cryptocurrency dapat berfungsi sebagai sarana halal untuk zakat jika memenuhi kriteria syariah seperti transparansi, kepemilikan aset yang sah, serta menghindari gharar dan riba. Regulasi dan fatwa di Malaysia menunjukkan penerimaan yang berkembang di bawah pengawasan ketat; teknologi blockchain meningkatkan akuntabilitas distribusi zakat, meskipun volatilitas nilai dan pemahaman publik tetap menjadi tantangan utama. Integrasi cryptocurrency dapat memodernisasi sistem zakat, meningkatkan kepercayaan dan transparansi sekaligus memastikan kepatuhan syariah. Kolaborasi antara regulator, ulama Islam, dan pengembang fintech sangat penting untuk membangun ekosistem zakat digital yang inklusif dan dapat diakses oleh komunitas Muslim Malaysia. Studi ini menawarkan perspektif inovatif dengan menggabungkan analisis regulasi, teknologi, dan fiqh mengenai cryptocurrency halal untuk zakat di Malaysia, mengisi kekosongan penelitian tentang solusi keuangan Islam digital di pasar negara berkembang.
The digital revolution has spawned new assets such as cryptocurrency, non-fungible tokens (NFTs), monetized accounts, and digital estates that are increasingly dominant in the Indonesian economy; however, these inheritance objects have not been explicitly addressed by classical fiqih mīrāth provisions or the Compilation of Islamic Law (KHI), creating a legal vacuum that threatens legal certainty and the protection of heirs' rights. This study aims to reconstruct inheritance fiqih regarding digital assets and cryptocurrency within the perspective of Indonesian Islamic Family Law to ensure proportional and equitable protection of heirs' rights. Employing a normative-empirical legal research method with a conceptual approach, maqāṣid asy-syari'ah, and juridical-empirical analysis of religious court decisions from 2020–2025 as well as in-depth interviews with judges and practitioners, this research analyzes the concept of māl in fiqih and judicial practice. The results indicate that digital assets fulfill the pillars of māl functionally (manfa'ah, taṣarruf, hifẓ); however, judicial practice remains trapped in three inconsistent patterns avoidance, proportional inclusion, and expert-assisted valuation which systematically threaten the rights of female and child heirs due to the absence of valuation guidelines and private key escrow mechanisms. This study formulates a new fiqih maxim based on ḥifẓ al-māl and ḥifẓ an-nasl and proposes a digital estate declaration to guarantee legal certainty and equitable distribution. This original contribution expands the frontier of contemporary ushul fiqih by introducing a digital māl taxonomy in Islamic inheritance and opens an interdisciplinary discourse on Islamic family law, fintech, and blockchain..
The advent of decentralized cryptocurrencies has reignited fundamental debates in monetary economics about the nature and future of money. Proponents of digital currencies argue that decentralized, algorithmically governed assets can supplant central banks in managing monetary conditions and stabilizing economic outcomes. This chapter critically examines this proposition by evaluating cryptocurrencies against the classical functions of money and the core instruments of monetary policy. Grounded in monetary theory – from Friedman’s monetarism and Mises’ Austrian framework to Modern Monetary Theory – and extended through a behavioral finance lens, the analysis reveals that widespread belief in cryptocurrency as a viable monetary policy alternative is driven not merely by technological innovation but by deeply embedded cognitive biases, including overconfidence, narrative-driven speculation, and institutional distrust. The chapter also treats money as an economic asset subject to market competition. Drawing on Austrian economic theory and classical competition principles, the analysis evaluates whether decentralized currencies can realistically compete with sovereign money in an open monetary market. By integrating monetary economics with strategic competition frameworks, the chapter explores whether cryptocurrencies can achieve monetary dominance through efficiency, cost advantages, or differentiated value propositions. Based on principles from strategic business theories such as differentiation and cost-leadership, the chapter treats money as a competitive good subject to market dynamics, ultimately concluding that while cryptocurrencies represent a significant financial innovation, they fundamentally lack the institutional architecture and behavioral predictability required to replace central bank monetary policy.
The digitization of financial markets has produced two classes of platforms that price, in principle, the same state - contingent payoffs: centralized crypto-option exchanges and blockchain-based prediction markets. This paper provides the first option-implied benchmark test of prediction-market pricing for cryptocurrency threshold contracts. For each hour in a matched sample, we compare the Polymarket Yes price with the discounted risk-neutral binary value implied by a listed Binance call option on the same underlying, strike, and maturity, and study the gap between them. In the main September 2023 Bitcoin contract, the mean pricing gap equals 5.6 percentage points across 214 hourly observations (t = 6.46, p < 10^{-9}). Pooling three Binance-compatible Bitcoin threshold markets yields a mean gap of 6.3 percentage points across 287 observations, robust to HAC and block-bootstrap inference. The gap is persistent - with an AR(1) half-life of roughly four hours - yet mean-reverting, consistent with slow information transmission between segmented venues rather than mechanical noise. Cross-sectional regressions reveal that the wedge is largest at low option-implied probabilities and long maturities, a pattern consistent with speculative demand for prediction-market contracts rather than measurement error. A delta-hedged arbitrage proxy remains profitable after conservative transaction costs, though with marginal statistical precision. A Deribit extension on the same three Bitcoin contracts produces a larger pooled gap of 11 percentage points, while a smaller Ethereum exercise yields mixed evidence. The results demonstrate that digital fragmentation of financial markets generates systematic, persistent pricing wedges even for economically identical payoffs.
Daniel Pereira Alves de Abreu, Octávio Valente Campos, Aureliano Angel Bressan
Objective: This study aims to evaluate the performance of different ARMA-GARCH model specifications in the risk management of major cryptocurrencies, investigating whether the inclusion of exogenous variables improves the calibration of risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). Methodology: To achieve this objective, 4,032 specifications of the ARMA-GARCH model applied to the ten main cryptocurrencies in trading were tested. The study incorporated the Fear and Greed Index and Bitcoin Trading Volume as exogenous variables in an ARMA-GARCH-X framework, comparing the performance of the different specifications against an ARMA(1,1)-GARCH(1,1) benchmark. Originality: Despite growing interest in crypto asset risk management, there are still gaps in the literature regarding the effectiveness of incorporating exogenous variables into forecasting models, as well as the increase in the quality of forecasts when using more complex models. Main results: The results indicate that the inclusion of external variables improves risk calibration in some assets, although the gains are marginal and heterogeneous. There is also no single optimal parameterization, requiring ARMA orders, GARCH specifications, and error distributions to be adjusted for each cryptocurrency. Theoretical/methodological contributions: From a methodological point of view, the study contributes by demonstrating the importance of specific calibration of ARMA-GARCH models for different cryptocurrencies in risk estimation. Furthermore, the results suggest that, although more complex models can improve tail risk estimation, the gains in predictive power over simpler models are limited. Keywords: Cryptocurrencies; Risk Management; ARMA-GARCH; Value-at-Risk; Expected Shortfall.
Alexandra Conda, Ștefan Găman, Raul Cristian Bag, Miruna Mazurencu-Marinescu-Pele · 6 authors
Abstract This study investigates the relationship between Facebook sentiment and Bitcoin market dynamics using AI-based emotion detection. We analyze 120,000 Facebook posts collected via CrowdTangle alongside Bitcoin financial data from the Blockchain Research Center, covering 2015–2023. Employing FinBERT for sentiment classification, we develop novel compound sentiment scores that integrate text-based sentiment with Facebook’s multi-reaction engagement system, then apply four analytical components: sentiment analysis, Dynamic Topic Modeling, sentiment-based trading strategies, and machine learning volume prediction. Results demonstrate that Facebook sentiment has substantial predictive power for Bitcoin trading volume. Sentiment-based trading strategies significantly outperform buy-and-hold, achieving superior cumulative returns and risk-adjusted performance. For volume prediction, Linear Regression and Bidirectional LSTM achieve comparable test performance, indicating that model complexity does not guarantee superior prediction. Topic modeling reveals that cryptocurrency investment and trading discussions dominate Bitcoin discourse on Facebook, with themes evolving over time in response to market conditions. This research contributes by being the first to apply post-level NLP sentiment analysis of Facebook data to cryptocurrency markets, extending beyond the Twitter and Reddit focus of prior research. The findings provide practical tools for traders and analysts navigating volatile digital asset markets while demonstrating that Facebook’s demographically diverse user base and rich reaction system offer unique advantages for sentiment quantification.
A current, urgent problem is whether the price behavior pattern of significant quantities of digital assets reflects a single direction trend line or multiple phases that exhibit different structures, adjusted inter-asset relationship differences, and changes in management systems, given the growing importance of digital assets in investment portfolios and collateral holdings, exchange-traded funds (ETFs), new forms of financial activities, and system risks over the period from 2020 through 2025. Because of this period’s post-pandemic recovery, speculative overextension, sharp decline, stabilization, and the re-entry of large-scale institutions into practice, these changes in prices are more clearly identified under such a context. Empirically, this study integrates descriptive statistics, rolling volatility analysis, augmented Dickey–Fuller’s unit-root test, segmented trend regression model with structural breaks, and vector autoregression (VAR) for return interactions. Based on these bases, both Bitcoin and Ethereum have demonstrated a relatively strong direction of continuous appreciation, together with quite considerable regime-specific instability. The log-price series is non-stationary, but the daily return series is stationary; so a level model is appropriate for medium-term trend analysis, and returns-based models can be applied more flexibly at shorter timespans. The segmented trend-regression analysis shows that close to peaks, such as those that occurred in 2021 for a long period, the 2022 correction, and the resumption of investment in 2024, are relatively distinct from the overall linear change pattern across all time periods. Both Bitcoin and Ethereum display pronounced contemporaneous co-movement, but they show no substantial lags via VAR or Granger causality tests conducted in the context of time-varying parameters. This study employs an integrated empirical research approach based on various perspectives to explore the long-term structural adjustment and near-instantaneous cross-market relationship dynamics, as well as regulatory mechanisms within a systemic context.
We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule. Trained on 6,034 fully-filled hourly Binance Options surfaces of BTC and ETH spanning May-October 2023 and parameterised on a common $6 \times 7$ tenor-delta grid, the model attains a hidden-cell surface-completion RMSE in the 0.94-1.56 vol-point range across both markets and mask rates 10-50%. The hybrid predictor attains 0.83 vol points at 50% masking against 7.00 for the smile re-fit alone, an eightfold reduction obtained at no additional inference cost. Under structurally-correlated hole patterns that emulate the withdrawal of an entire tenor of strikes, the smile re-fit incurs 9.6-13.1 vol points of error while the learned model remains at 1.5-1.9, isolating a regime in which the generative model is the only viable predictor. Joint training on BTC and ETH improves the in-distribution model on both markets by 9-27% relative to the better-performing single-symbol counterpart, indicating a substantially shared vol-surface manifold across the two largest cryptocurrencies over the observation window. The hybrid is calendar- and butterfly-arbitrage-free at the listed strikes, a property that the parametric smile re-fit alone fails at high mask rates. The per-snapshot reconstruction error of the trained model flags the late-October ETF-anticipation rally and the August $17$, $2023$ flash crash as elevated-error periods without supervision. All training and evaluation infrastructure is released to support reproducible follow-on work.
Cryptocurrency markets are prone to violent, synchronised drawdowns, challenging the claim that a basket of crypto-assets offers genuine internal diversification. Because standard covariance-based metrics fail to capture asymptotic tail dependence, they systematically understate systemic risk and overstate diversification benefits precisely when markets crash. This study maps the conditional dependence structure of the cryptocurrency market directly in the joint tails, isolating direct extremal linkages from those mediated by the rest of the system. We analyse the daily returns of the thirteen largest cryptocurrencies over a sequence of 89 overlapping windows spanning late 2021 to 2025. We apply dynamic Hüsler-Reiss graphical models of extremes, estimated separately for joint crashes and rallies, and benchmark them against a Gaussian graphical model of ordinary co-movement. The results reveal a near-complete and stable lower-tail graph, an upper tail that thins over time to re-form sectoral structures, and the dissolution of ordinary token categories into a single block anchored by a Bitcoin-Ethereum core. These findings imply that intra-crypto diversification fails on the downside, standard risk models underestimate market-wide crash probabilities by roughly eight-fold, and dynamic extremal graphs offer a superior tool for systemic risk monitoring.
Data availability is a fundamental bottleneck in modern blockchain networks. Most blockchain systems rely on a full-replication model, which requires downloading of a full block to verify its availability. This model does not scale with block size because every node must handle large volumes of data, leading to slower block propagation, duplicated data transfer, and longer consensus agreement. This issue is well-known in Ethereum, where layer-2 rollups publish data directly into the chain. To overcome, Ethereum adopts Data Availability Sampling (DAS) to let nodes keep only a small fragment of the data while still ensuring availability. Prior work on DAS has focused on cryptographic foundations. Meanwhile, the peer-to-peer network layer that provides Byzantine-tolerant and scalable mechanisms for discovery and routing of DAS fragments is underexplored. We propose CDA, a new design for DAS based on coded distributed arrays that leverages network coding to ensure both robustness and efficiency. Our evaluation study compares CDA to RDA, the latest DAS development of Ethereum, showing an improvement of several times better.
Dustin Weiss, Robert Gaudiosi, Z. Ivy Zhou, Robert I. Webb
This paper examines intraday Bitcoin spot returns and trading activity around the expiration of Deribit Bitcoin options. Using data from spot exchanges and Deribit perpetual futures, we document a statistically and economically significant return reversal around expiration. The effect concentrates on days with elevated at-the-money open interest and is strongest when cumulative gamma exposure is negative, which is consistent with positive feedback trading pressure induced by option market makers hedging net short exposure. Trading activity also rises around expiry in Deribit perpetual futures and in the spot exchanges used to determine the Deribit settlement price. These intraday price effects are economically meaningful, implying annual wealth transfers of approximately USD 50 million between option writers and holders. Overall, the findings highlight the role of daily option expirations in shaping short-horizon price formation in Bitcoin markets and have implications for regulated investment products that rely on spot-market reference prices.
A. B. Hajira Be A. B. Hajira Be, S.Bhuvaneshwari S.Bhuvaneshwari, Sankari.S Sankari.S
Cryptocurrency markets have gained significant global attention due to their decentralized nature and high financial value. Among various cryptocurrencies, Bitcoin is the most widely traded and exhibits highly volatile price behavior. Accurate analysis and prediction of Bitcoin price trends are challenging because the market is influenced by rapid trading activities, large data streams, and complex temporal patterns. This paper presents a streaming data collection and analysis system for Bitcoin using the Long Short-Term Memory (LSTM) deep learning algorithm. The proposed system continuously collects real-time Bitcoin market data from online cryptocurrency exchanges through streaming APIs. The collected data is then preprocessed and analyzed using an LSTM-based predictive model capable of learning long-term dependencies in time-series data. The LSTM network processes sequential historical price data to forecast future market trends and provide analytical insights into Bitcoin price movements. The system integrates data acquisition, preprocessing, deep learning-based prediction, and visualization modules to create an efficient cryptocurrency analysis framework. The proposed approach focuses on improving prediction accuracy by combining real-time streaming data with advanced neural network models. This system can assist researchers, financial analysts, and investors in understanding cryptocurrency market behavior and making informed trading decisions. The proposed design demonstrates the feasibility of integrating streaming data technologies with deep learning models for real-time financial market analysis. Keywords— Cryptocurrency, Bitcoin, Streaming Data, LSTM Algorithm, Deep Learning, Time-Series Prediction, Financial Data Analysis.