Blockchain Papers

Harley Pacheco de Sousa

No abstract is available for this record.

Ali Köse, Mustafa Okur

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

Ahmet Furkan Sak

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.

Ayei E. Ibor, Denis U. Ashishie, John Adinya Odey, Bassey Ele · 5 authors

ABSTRACT Elliptic curve cryptography ( ECC ) underpins the security of most blockchain systems, yet its practical implementations face numerous vulnerabilities. In this systematic literature review ( SLR ), we catalogue and analyze attacks on ECC in the context of blockchain security, including side‐channel attacks, nonce/ PRNG failures, cryptanalysis, and implementation flaws, and we survey proposed countermeasures. We follow rigorous SLR methodology with defined inclusion/exclusion criteria, search strategies across databases such as IEEE Xplore, ACM , Scopus, Web of Science, and clear data synthesis, ensuring replicability. Emphasizing empirical case studies and real‐world exploits, we discuss instances where ECC weaknesses led to blockchain breaches including biased elliptic curve digital signature algorithm nonces exposing Bitcoin/Ethereum private keys, smartphone power analysis revealing wallet keys, and Trezor hardware‐wallet key extraction via single‐trace side‐channel analysis ( SCA ). We tabulate known attack vectors versus affected systems, and similarly compare countermeasure techniques such as hybrid classical/quantum schemes, threshold signatures, and zero‐knowledge proofs, along with implementation trade‐offs. We evaluate advances such as Curve25519/ EdDSA and ARM SVE2 to mitigate side‐channel leakage. Our findings highlight that practical security of blockchain cryptosystems depends on correct ECC implementation and emerging cryptographic upgrades, not merely on the mathematical hardness of the elliptic curve discrete logarithm problem.

Constantine Doumanidis, Anya Kalogerakos, Maria Apostolaki

BGP hijacking enables impersonation attacks in which adversaries divert traffic at the prefix level and serve malicious content to unsuspecting clients. Detecting such attacks has traditionally been the responsibility of network operators, leaving end hosts exposed for hours. We argue that end hosts can detect prefix-level impersonation independently, exploiting a fundamental asymmetry: a BGP hijack diverts traffic for an entire IP prefix, but impersonating every co-hosted service within that prefix is prohibitively difficult at scale, especially if each service is authenticated by a different Certificate Authority. We propose HOWLR, a tool that operationalizes this insight by using co-hosted, TLS-authenticated services as witnesses: if a client can no longer authenticate them, it has evidence of an ongoing attack. This work evaluates the feasibility of this method by quantifying the existence and diversity of witnesses in the wild. We show that HOWLR can protect 89% of Tor relay prefixes, and 75% of Bitcoin pool gateway prefixes.

László Papp

This paper argues that universal, cross-domain trust scores — from credit ratings and ESG scores to AI-generated trust metrics — face structural limits that better data or better models do not remove. The claim is not that scoring is never useful, but that compressing trust into a single comparable number, used for high-stakes allocation across contexts, recurrently fails. Trust is treated here not as a scalar quantity but as a contextual, relational, and time-dependent state. The paper identifies five recurring failure modes (context collapse, Goodhart's Law, epistemic centralization, irreversibility, and metric substitution for truth), illustrated through documented institutional failures (Enron, Wirecard, Volkswagen Dieselgate, the 2008 subprime crisis, and ESG rating practice). An informal impossibility argument — analogous in form to Arrow's theorem, not a formal mathematical proof — suggests that no single universal trust score can jointly satisfy context-independence, temporal stability, observer-neutrality, and manipulation-resistance. The paper then discusses proof-based verification as a complementary paradigm: for a bounded class of objective, checkable claims, the need for trust is reduced through local verification rather than measurement. Examples include Bitcoin proof-of-work, zero-knowledge proofs, and blockchain-based supply chain traceability. The limits of this approach are discussed explicitly, including the oracle problem and the irreducibly judgmental claims that proof cannot settle. This is version 2.0, a substantial revision repositioning the work from a position paper toward a conceptual analysis: the central thesis is qualified, an explicit scope-and-limitations section is added, the impossibility argument is reframed as informal, and the limits of proof-based verification are addressed directly.

วริสา ดาโท้, ปิยะชาติ เวียงนาค

สกุลเงินดิจิทัล เช่น Bitcoin (BTC), Ethereum (ETH) และ Dogecoin (DOGE) กำลังได้รับความนิยมเพิ่มขึ้น ทำให้การคาดการณ์ราคามีความสำคัญ งานวิจัยนี้ใช้โครงข่ายประสาทเทียมแบบ LSTM เพื่อพยากรณ์ราคาสกุลเงินดิจิทัล โดยเก็บข้อมูลราคาย้อนหลังจาก Yahoo Finance ตั้งแต่วันที่ 7 มกราคม พ.ศ 2563 ถึงวันที่ 5 มกราคม พ.ศ 2568 ทำการพรีโพรเซสข้อมูลด้วย Min-Max Scaling และใช้ข้อมูลย้อนหลัง 60 วันพยากรณ์ราคาวันถัดไป จากนั้นสร้างโมเดล LSTM โดยทำการทดลองปรับค่าพารามิเตอร์ 3 ค่า ได้แก่ LSTM Units, Dropout Rate และ Dense Layer และเปรียบเทียบประสิทธิภาพโดยใช้ RMSE ผลการทดลองพบว่า สำหรับ BTC โมเดลที่มี LSTM Units = 60, Dropout Rate = 0.1, Dense Layer = 2 ให้ค่า RMSE ต่ำสุดที่ 1967.35 สำหรับ DOGE โมเดลที่มี LSTM Units = 70, Dropout Rate = 0.1, Dense Layer = 1 ให้ค่า RMSE ต่ำสุดที่ 0.01242 และสำหรับ ETH โมเดลที่มี LSTM Units = 70, Dropout Rate = 0.1, Dense Layer = 1 ให้ค่า RMSE ต่ำสุดที่ 116.54 ผลลัพธ์แสดงให้เห็นว่า LSTM สามารถใช้ในการพยากรณ์ แนวโน้มราคาสกุลเงินดิจิทัลได้อย่างมีประสิทธิภาพ แต่ความแม่นยำอาจได้รับผลกระทบจากความผันผวนของตลาด

Batuhan Karabiber, Tayfun Tuncay Tosun

This study empirically aims to analyze the impact of primary monetary policy stance and transmission mechanisms of the European Central Bank (ECB)—such as the total assets of the ECB, long-term interest rate based on the government bond yields, and the EURUSD exchange rate—on major volatile cryptocurrencies like Bitcoin and Ethereum, as well as the leading stablecoin Tether. To this end, the study employs the linear Autoregressive Distributed Lag (ARDL) and the Bootstrap ARDL (BA-ARDL) procedures, robust approaches with limited data in time series analysis. The dataset consists of monthly data over the period from January 2019 to December 2025. We summarize the novel and robust primary empirical results of our study as follows: First, (i) it is revealed that the ECB’s balance sheet expansion has encouraged Bitcoin and Ethereum, yet has also, to a limited extent, suppressed Tether. Secondly, (ii) while the ECB’s long-term interest rate negatively impacts the prices of Bitcoin, Ethereum, and Tether, the negative impact on Tether is relatively weaker. Finally, (iii) the EURUSD exchange rate positively affects Ethereum, while its effect on Bitcoin is not statistically significant. On the other hand, at a 10% significance level, EURUSD has a weak negative effect on Tether. In conclusion, the empirical evidence demonstrates that the primary monetary policy stance and transmission mechanisms of the ECB influence the leading digital assets in distinct ways. Taking our findings into account is crucial for designing the digital euro in terms of financial stability and regulatory framework. Finally, we offer sound policy implications for the ECB based on empirical findings.

Victoria Portnaya

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.

Omer Bafail, Adnan Miski

The rapidly expanding landscape of Web3 and the metaverse profoundly accentuates the escalating challenge of rigorously assessing and strategically selecting foundational Layer-1 digital blockchain platforms. Decision-makers frequently contend with the imperative of rational choice amidst a complex confluence of often conflicting technological attributes. This study directly addresses this critical exigency by utilizing robust benchmarking and validation for the comparative ranking of 10 prominent blockchain platforms. By applying a suite of five established multi-criteria decision-making (MCDM) methods, namely TOPSIS, ARAS, RAPS, RAMS, and RATMI, a comprehensive evaluation is undertaken, scrutinizing performance across three pivotal criteria categories: performance/scalability, security, and economic/activity. The weights for the entire criteria set were determined using the objective entropy method. Using the entropy approach to determine weights based on randomness, the criteria weights were determined as follows: Speed 12.9%, Market Cap 7.2%, Hash Rate 43.7%, Time to Finality 12.1%, Total Transactions 10.8%, and Number of Nodes 13.3%. The empirical analysis consistently identifies Bitcoin as the top-ranking platform, securing first position across all five MCDM methodologies. This finding validates its unparalleled robustness and security based on the defined criteria. Hyperliquid and Sui also emerged as exemplary performers, consistently exhibiting strong aggregate scores and securing second and third positions, respectively. Conversely, other blockchains, such as the BNB Chain and Tron, demonstrated significant ranking volatility across the different evaluation methods. This study provides a validated, data-driven benchmarking tool, offering stakeholders a transparent framework for strategic decision-making. This application contributes to the conceptual accuracy of evaluating sustainable digital infrastructure.

Elliot Jones, William Knottenbelt

Advances in Artificial Intelligence (AI) have led AI for Theorem Proving to become a promising means of formally verifying computer systems. Whilst formal verification is traditionally reserved for safety-critical systems due to the required amount of expertise and effort, AI can help to automate a large amount of this workload and make it far more accessible. Blockchain-based systems are becoming increasingly popular and are frequently targeted by malicious actors, often resulting in huge financial losses, highlighting the need to better verify these systems and mitigate vulnerabilities. Arguably the most important component of these systems is the consensus protocol, which allows nodes to agree on decisions in a potentially adversarial environment. In this paper, we improve upon IsabeLLM, the automated theorem proving tool in Isabelle. Namely, we implement a Retrieval-Augmented Generation framework, Error tracing and counterexample generation for improved context supplied to the Large Language Model. Compatibility with the latest version of Isabelle and Sledgehammer is also implemented for improved efficiency. We compare the performance of the two versions of IsabeLLM in their ability to complete the verification of Bitcoin's Proof of Work consensus.

Klaus M. Frahm, Leonardo Ermann, Dima L. Shepelyansky

According to the recent Wealth Thermalization Hypothesis (WTH) the wealth inequality in the world is described by the Rayleigh-Jeans (RJ) thermal distribution of interacting agents in a society with social stratification. In this concept, the wealth layers of society are associated with energy levels from a nonlinear dynamical system conserving two integrals of motion being total energy and probability norm. This leads to RJ condensation and the formation of a huge poverty phase of low wealth and a tiny oligarchic phase that captures a main part of total society wealth. This RJ phenomenon has similarities with self cleaning in multimode optical fibers and constraint driven condensation in various physical systems. We analyze real Lorenz and Pareto curves for wealth of households in countries and the world, Gross Domestic Product of countries, market capitalization of companies at stock exchange of Hong Kong, Shanghai, London, bitcoin transactions, world trade between countries and show that the WTH theory gives a good description of these curves. On the basis of this comparison we argue that the RJ thermal distribution provides a universal description of wealth inequality in the world.

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.

Kai Yang, Jialiang Liu, Yunrui Guan

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.

Rama Siva Sarwari Mallela, Manuele Leonelli

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.

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.

Leon Calvin II long, Benjamin Lawrence Eckenfels

System and Method for Reinforcement Learning‑Based Token Minting and Cross‑Chain Cryptographic Anchoring This archive contains the full non‑provisional patent submission for a unified digital‑asset lifecycle system integrating reinforcement‑learning‑based token minting, Merkle‑structured ledgering, and synchronized cross‑chain cryptographic anchoring. The invention establishes a deterministic, mathematically governed framework for creating, operating, and verifying digital asset states across heterogeneous blockchain networks including Bitcoin, Ethereum, and Solana. The system introduces a blueprint‑based binding mechanism, a formal kernel governed by a unified state equation, and a sovereign ledger enabling long‑term provenance and deterministic replay. A reversible 32‑byte commitment value is computed using a Sponge‑586 invariant and anchored to Bitcoin via Taproot tweaks and OP_RETURN payloads. Parallel anchoring events emit the authenticated Merkle Mountain Range (MMR) root on Ethereum and Solana, producing tamper‑evident, multi‑consensus proofs of state. A reinforcement‑learning engine dynamically adjusts minting rates based on real‑time market conditions, behavioral metrics, and system‑level variables. The system further supports gasless user interactions (EIP‑2771), zero‑knowledge compliance pathways, federated‑learning simulations, and deterministic state reconstruction through Kolmogorov integrity scoring and synthesis restoration. This archive includes the complete specification, mathematical formulations, alternative embodiments, and references to supporting research hosted on Zenodo. It documents the developmental lineage, reduction‑to‑practice demonstrations, and cross‑chain anchoring methodology associated with U.S. Patent Application No. 19/693,343.

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.

Jules Clément, Gomolemo Goodwill Motloba, Abdulrazak Abdulrahman Abubakar

This paper develops a mathematical framework for modeling Bitcoin price dynamics through a system of coupled stochastic differential equations (SDEs). We capture the complex nonlinear interactions between Bitcoin price and five key factors: investor sentiment, trading volume, mining hashrate, transaction fees, and transaction counts. The model incorporates jump processes to account for sudden price movements and regime-switching to capture state-dependent dynamics. We derive the resulting partial differential equations for derivative pricing and analyze the system's behavior through simulation. Our empirical findings suggest significant feedback mechanisms between network metrics and price dynamics, with hashrate exhibiting the strongest correlation with price movements. The framework provides a foundation for understanding the complex, non-linear, and fractal-like behavior observed in cryptocurrency markets while enabling the pricing of derivatives in this emerging asset class.

Panayotis G. Michaelides, Panos Xidonas, Aristeidis Samitas, Konstantinos Ν. Konstantakis · 5 authors

The purpose of this study is to examine the potential safe-have properties of the two most popular cryptocurrencies, i.e., Bitcoin and Ethereum, against equites, government bonds and gold. To do so, the paper makes use of a daily dataset ranging from 2018 to 2022 acknowledging both the COVID-19 and the potential halving effect in the cryptocurrency market. To robustly assess the research question, the paper employs a quantile GARCH model with non-parametric diagnostics, dynamic Local Projections and rolling window estimations for robustness. The findings of the paper suggest that both assets act as diversifiers against equities and against each other, whereas the halving effect is statistically insignificant and the COVID-19 effect is statistically significantly positive only for the returns of Ethereum. The results imply that cryptocurrencies could contribute to portfolio diversification under stress market conditions.

Samuel Taiwo Fatunmbi, Om Amit Gandhi, Luke Logan

Blockchain consensus mechanisms based on Proof-of-Work consume significant energy, with Bitcoin alone estimated at approximately 150 TWh per year. Proof-of-Space reduces this cost by replacing repeated computation with storage, but plot generation remains bottlenecked by CPU hashing throughput. Prior work on VaultX demonstrated a high-performance CPU-based Proof-of-Space plotter using multi-threaded Blake3 hashing, achieving plotting speeds 4 to 50x faster than Chia depending on hardware configuration. In this paper, we present VaultxGPU, a GPU-accelerated extension of the VaultX plotter that offloads the Blake3 hashing pipeline to the GPU using custom kernels. We implement the plotter in both CUDA for NVIDIA hardware and SYCL for AMD and Intel GPUs, keeping Table 1 entirely in GPU VRAM and fusing the sort and match stages into a single kernel to minimize data movement. We evaluate VaultxGPU across K-values 27 through 31 against CPU baselines. Our SYCL GPU implementation achieves a 59.2x speedup over a single-threaded CPU baseline, completing a K=31 plot in 45.4 seconds compared to 2688 seconds, and outperforms even the best 384-thread CPU configuration. These results confirm that GPU acceleration is the correct direction for scaling Proof-of-Space plotting beyond what CPU parallelism can achieve.

Thu Minh Thi VU, Linh Ha Nguyen

Purpose This study investigates the volatility spillover dynamics between carbon credit market represented by European Union Allowance (EUA) futures and major cryptocurrencies, Bitcoin (BTC) and Ethereum (ETH), during the 2020–2024 period. It aims to understand whether these assets, despite their difference in regulatory and structural features, exhibit interconnected volatility pattern and particularly under crisis or shock conditions. Design/methodology/approach The article employs a two-step econometric approach. First, the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model is used to estimate time-varying return correlations among EUA, BTC and ETH. And second, the Diebold–Yilmaz (2012) spillovers index based on forecast error variance decomposition is applied to quantify the sizes, directions and evolution of volatility spillovers across markets. Findings The results reveal significant but uneven and time-varying volatility spillovers between carbon and cryptocurrency markets. Spillover intensity becomes more prominent, especially during major crisis periods such as the COVID-19 pandemic, the Russia–Ukraine war and the FTX collapse. Spillovers are asymmetric and regime-dependent. ETH emerges as the main net volatility transmitter, while BTC exhibits a near-neutral and regime-dependent role, alternating between transmitting and receiving shocks. EUA futures remain largely insulated, with only limited outward volatility transmission even under extreme market conditions. These findings suggest the presence of conditional and crisis-driven spillover linkages between green and digital assets. Originality/value This is among the first studies to empirically examine the volatility transmissions between carbon credit and cryptocurrency market using advanced econometric tools. It contributes to the emerging green -digital finance literature by identifying dynamic and directional interdependency across these evolving asset types.

Iosif M. Gershteyn, Jacob A. Alber

Quantum computing poses a real, broad-based, but bounded and substantially mitigable threat to Bitcoin and Ethereum. We separate the two quantum algorithms that public discussion routinely conflates: Shor's algorithm breaks the elliptic-curve signatures (ECDSA over secp256k1, BLS over BLS12-381) that authorize spending, whereas Grover's algorithm does not meaningfully threaten proof-of-work mining, which is protected by a merely quadratic speedup, fault-tolerant per-operation costs, a square-root parallelization wall, and difficulty adjustment. Folding hardware scaling, the falling resource requirement, a fault-tolerance readiness lag, and expert surveys into a single Monte-Carlo forecast yields a wide, bimodal arrival distribution for a cryptographically relevant quantum computer: about a one-in-six chance by 2035, near 30% by 2040, and about 60% by 2050. Exposure is concentrated and mostly migratable: of Bitcoin's roughly six million quantum-exposed coins only about 2.3 million are irreducibly at risk, while 50 to 65% of Ether sits at key-revealed accounts that can adopt post-quantum signatures. A timely migration beats even an optimistic 2035 machine, so the binding constraint is governance, not technology. A survey of the top twenty cryptocurrencies finds none fully post-quantum. Reproducible models accompany every quantitative claim.