Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining's evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open-source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms recurrent, convolutional, and attention-based baselines, achieving 83.2% accuracy and 83.5% macro F1-score. The model demonstrates strong economic relevance, achieving 97.8% precision in detecting unprofitable periods and 81.5% precision in detecting profitable ones, while avoiding misclassifying profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations.
Crypto analysts have to deal with a variety of challenges, with the most important area being the price volatility of cryptocurrencies. Due to uncertain market trends, many studies have been conducted on forecasting techniques, and some of these techniques have been integrated with advanced analytical tools, including machine learning (ML) techniques. Making reliable predictions of the speculative behavior of financial assets, especially in non-stationary and highly volatile environments such as the cryptocurrency market, is a challenging task. In this study, ML techniques are used to identify influential features that affect the prices of cryptocurrencies, especially for Bitcoins. In addition, multivariate control charts are utilized for signal detection, allowing for a structured approach to develop trading strategies for seasonal market conditions. Unlike other studies that do not take seasonality into serious consideration when analyzing market fluctuations, the proposed approach explicitly accounts for it. The developed strategy is tested across various market conditions, including the final days of each year from 2019 to 2024, and demonstrates strong and consistent performance in all cases. By systematically identifying key on-chain features and analyzing them by means of control charts, this study develops a structured approach to anomaly-based trading strategies in Bitcoins. These discoveries address an extensive discussion on automated trading systems, demonstrating that feature selection, technical indicators, market seasonality, and halving impacts are important components in hinting at successful cryptocurrency exchange strategies.
Antonio MartĂnez Raya, Alejandro Segura de la Cal, Javier Espina HellĂn
Since its launch in 2009, Bitcoin has become a market disruptor due to its primary function as a virtual currency supported by blockchain technology and the high volume of economic transactions it facilitates. This article examines the key theoretical principles that have contributed to Bitcoinâs recognition as a cryptocurrency. It assesses whether Bitcoin meets the criteria for being considered a form of money and evaluates its importance as a financial asset. This analysis of Bitcoin from 2014 to 2025 reveals that it does not sufficiently fulfill all the typical functions of money, such as serving as an internationally accepted means of payment, a unit of account, a securities depository, and a standard for deferred payments. Despite its usual close correlation with stock indices in financial markets, a decentralized digital currency like this still does not meet the requirements of fundamental analysis. In practice, this leads to its exclusion as a currency, since it does not fulfill the functions of money nor fully qualify as a crypto asset, as its value is primarily based on investorsâ expectations of high returns. Apart from a lack of foundation in tangible goods or services that justifies their value and dependence on new investors, the findings do not indicate conditions typical of a developed pyramidal model. Nevertheless, this does not prevent future technological innovations from responding positively to the functions of money or from offering real money services, especially those related to service innovation and the digital economy.
This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoinâs safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem.
This paper explores the role of mining activity, proxied by growth rates of electricity consumption and cost of mining, as a driver of pricing inefficiencies in Bitcoin. Utilizing alternative measures of crash risk proxied by the realized negative coefficient of skewness and realized down-to-up volatility, derived from 5-minute intraday Bitcoin data, causality tests, along with sign analysis, captured by the estimates of partial average derivatives, provide evidence that mining activity can, in general, predict an increase in the entire conditional distribution of crash risk, with the strongest impact associated over the normal (median) to moderately high (upper quantiles) levels of risk. Despite the emergence of cryptocurrencies in international transactions and as an investment vehicle, our results suggest that decentralized mining process can contribute to inefficiencies in the pricing of Bitcoin, putting further doubt into the role of these assets as a medium of exchange, alternative to conventional assets.
Public companies and institutional investors that hold Bitcoin face increasing pressure to show solvency, manage risk, and satisfy regulatory expectations without exposing internal wallet structures or trading strategies. This paper introduces the Treasury Proof Ledger (TPL), a Bitcoin-anchored logging framework for multi-domain Bitcoin treasuries that treats on-chain and off-chain exposures as a conserved state machine with an explicit fee sink. A TPL instance records proof-of-reserves snapshots, proof-of-transit receipts for movements between domains, and policy metadata, and it supports restricted views based on stakeholder permissions. We define an idealised TPL model, represent Bitcoin treasuries as multi-domain exposure vectors, and give deployment-level security notions including exposure soundness, policy completeness, non-equivocation, and privacy-compatible policy views. We then outline how practical, restricted forms of these guarantees can be achieved by combining standard proof-of-reserves and proof-of-transit techniques with hash-based commitments anchored on Bitcoin. The results are existence-type statements: they show which guarantees are achievable once economic and governance assumptions are set, without claiming that any current system already provides them. A stylised corporate-treasury example illustrates how TPL could support responsible transparency policies and future cross-institution checks consistent with Bitcoin's fixed monetary supply.
Blockchain networks face a critical but understudied threat: wave attacks that exploit difficulty adjustment algorithms through strategic mining participation. Adversaries cyclically withdraw and re-enter mining to create oscillations that degrade network liveness and destabilize honest minersâ revenue. We present the first production-ready framework that maintains network responsiveness while enabling robust, post hoc threat detection. The framework employs a statistically rigorous pipeline featuring controller-aligned anomaly detection, transitive collusion grouping via union-find, and BenjaminiâHochberg False Discovery Rate control. We formally prove the economic viability of this architecture: when penalties on unvested rewards are enabled by governance, wave attacks become asymptotically unprofitable for rational adversaries. Evaluated on a 128-node distributed testbed simulating Bitcoin, Ethereum Classic, and Monacoin networks over 30 independent runs, our framework achieves 92.7% F1-score in detecting attacks, significantly outperforming baseline methods (74.7%). This work provides a complete, theoretically-grounded solution for securing proof-of-work blockchains against difficulty manipulation, forming the foundation for the adaptive AI-driven enhancements presented in our companion paper (Part II).
Abstract This study explores the dynamic volatility spillovers and interconnectedness between cryptocurrency and traditional futures markets. Using a multi-method approach that integrates wavelet coherence analysis, TVP-VAR connectedness, and DCC-GARCH modeling, the research identifies notable shifts in spillover patterns during crises, such as the COVID-19 pandemic, the FTX collapse, and the Russia-Ukraine conflict. The results reveal that the correlations between Bitcoin futures and traditional asset classes depend on the market conditions and intensify during crises. The connectedness analysis shows that Bitcoin futures play a dual role, acting as a transmitter of long-term shocks and a receiver of short-term shocks during periods of crisis. Equity futures emerged as the primary long-term transmitters of shocks, whereas other assets acted as shock receivers during the pandemic. Furthermore, the study evaluates hedge ratios and portfolio weights using the DCC-GARCH model. The portfolio analysis reveals that Bitcoin futures require a minimal allocation within diversified portfolios, suggesting their limited effectiveness as a hedge and safe-haven asset. These results aim to inform portfolio managers in developing efficient hedging strategies and assist regulators in monitoring financial market stability. This study fills gaps in the existing literature by understanding how decentralized financial instruments interact with financial markets and providing insights into risk management in modern markets.
It is worth noting that the topic of cryptocurrencies is characterized by modernity, and the resulting vacuum exists for many of them, and this is entirely the result of the failure of the vast majority of countries and international organizations to analyze them, to distinguish the topic as virgin, especially since it has been digital, so it pushes modern and innovative has become at the forefront. Details list Controversy around the world, as cryptocurrencies represented a dangerous stage in the development of currencies that we witness today in different eras, especially in light of the noticeable spread of these currencies, whether in the present or in the future One of the most important problems resulting from dealing in cryptocurrencies has become the lack of legislative texts that address disputes arising from the trading of digital currency in most countries and international organizations. We also did not find legal legislation for cryptocurrencies in Iraqi legislation that regulates them, and the issue of Research is considered a virgin topic in private international law. Being a complex and thorny subject that includes technical rules, technical complexities and multiple inter-related relationships involving multiple legal systems, which requires an integrated legal system.
Open access
Blockchain Technology Applications and Security
Security, Politics, and Digital Transformation
Innovations and Analysis in Business and Education
Purpose â The purpose of this research is to explore the opportunities and barriers related to the use of cryptocurrencies in tourism from the local communityâs perspective. Cryptocurrencies are increasingly accepted worldwide, yet their use in tourism consumption remains limited. Evaluating the attitudes and readiness of residents in urban areas, particularly in Zagreb, is essential for assessing the sustainability of digital payment technologies in tourism. Methodology â The research was conducted in Zagreb and its surroundings, with a sample of 484 respondents. A structured questionnaire was used to assess knowledge, perceived security, intention to use, and perceived barriers and incentives regarding cryptocurrency usage in tourism. Data analysis involved descriptive statistics and Pearsonâs Chi-square test to examine relationships between key variables and sociodemographic factors. Findings â The results indicate limited awareness about cryptocurrencies, with more than 75% of respondents being completely unfamiliar or only superficially familiar with the topic. A small percentage currently uses cryptocurrencies, but there is substantial conditional willingness for future usage, particularly if regulatory, educational, and security issues are addressed. Statistically significant gender differences were observed in perceived awareness and trust in Bitcoin systems, with men exhibiting higher levels of awareness and trust compared to women. Contribution â This study provides valuable insights into local community readiness for cryptocurrency usage in tourism, highlighting the significance of education, trust, and regulatory frameworks. The findings can serve as a foundation for policymakers, tourism stakeholders, and digital innovators to develop strategies for the effective integration of cryptocurrencies into tourism economies.
Dec 1, 2025·International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences
sarah Osama anis, Mohammed Mabrouk Morsey, Mostafa Aref
In the rapidly evolving landscape of cryptocurrency, gaining a deep understanding of public sentiment has become increasingly essential, especially given the significant impact of social media platforms on market perceptions and trends. This paper introduces a sophisticated sentiment classification model that utilizes a Bi-LSTM architecture to analyse over one million tweets related to Bitcoin. By integrating Explainable AI techniques, particularly LIME (Local Interpretable Model-agnostic Explanations) framework, our model not only achieves an impressive test accuracy of 98% but also offers valuable insights into its decision-making process, making the results more interpretable for users Our findings highlight robust performance metrics across precision, recall, and F1-scores, which collectively underscore the model's reliability and effectiveness in real-world applications. Furthermore, we delve into the opaque nature of the Bi-LSTM model through the application of LIME, which sheds light on how particular words and phrases have a strong impact on sentiment predictions. This research equips future investigations with conceptual frameworks and analytical tools that can be customized to study a broader range of cryptocurrencies. Through this work, we aim to foster a more nuanced comprehension of how public sentiment shapes market behaviour and decision-making in the digital currency space.
Resumo / Abstract : Este artigo pretende demonstrar como o Bitcoin se insere na construção de uma cultura de paz. Apresenta a evolução do conceito, de ânĂŁo guerraâ para ânĂŁo violĂȘnciaâ, e caracteriza a cultura de paz como uma dinĂąmica social de colaboração. Pontua que as transaçÔes nĂŁo mediadas por terceiros possibilitam que os indivĂduos escapem da influĂȘncia econĂŽmica que acentua a assimetria de poder. Reconhece que a dinĂąmica que recompensa e incentiva a integridade da rede bitcoin privilegia a colaboração. Ao final, conclui que estamos diante de uma infraestrutura monetĂĄria que possibilita aquilo que queremos ver acontecer. This paper aims to demonstrate how Bitcoin fits into the construction of a culture of peace. It presents the evolution of the concept, from "non-war" to "non-violence," and characterizes the culture of peace as a social dynamic of collaboration. It points out that transactions not mediated by third parties allow individuals to escape the economic influence that accentuates power asymmetry. It recognizes that the dynamic that rewards and encourages the integrity of the Bitcoin network prioritizes collaboration. In conclusion, it states that we are facing a monetary infrastructure that enables what we want to see happennig.
This study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoinâs daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p < 0.05), SMA (r = 0.38, p < 0.05), EMA (r = 0.41, p < 0.05), and ATR (r = 0.48, p < 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = â0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volumeâpriceâvolatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management.
In today’s technologically advancing world, many fields from finance to healthcare and education are shifting toward a digital and decentralized format. A significant transformation is underway with the currency of the masses. Blockchain-based cryptocurrencies like Bitcoin and Ethereum allow users to generate fungible tokens anonymously through smart contracts. However, these features also facilitate illicit transactions and cybercrimes like fraud, phishing, and money laundering. The proposed work explores the identification of suspicious transactions on the Ethereum blockchain by leveraging advanced machine-learning techniques. An Extreme Gradient Boosting (XGBoost) classifier is optimized for spotting unauthorized or malicious transactions, exploring features like transaction patterns and value anomalies. Feature scaling and log transformations normalize skewed distributions, while rigorous model training and hyperparameter tuning enhance the system's precision, recall, and overall accuracy. Other aids, such as feature importance rankings, precision-recall curves, and diagnostic statistics, provide useful information on fraud patterns. Evaluation of the model shows that integrating cost-sensitive learning significantly reduces false positives, from 51 to 44, representing a 13.7% decrease, which enhances practical usability by minimizing false alerts and manual verification efforts. Although there was a slight increase in false negatives (from 14 to 15), the overall classification accuracy improved. The model demonstrated strong performance in managing class imbalance which is common in fraud detection contexts.
Bitcoin operates as a macroeconomic paradox: it combines a strictly predetermined, inelastic monetary issuance schedule with a stochastic, highly elastic demand for scarce block space. This paper empirically validates the Endogenous Constraint Hypothesis, positing that protocol-level throughput limits generate a non-linear negative feedback loop between network friction and base-layer monetary velocity. Using a verified Transaction Cost Index (TCI) derived from Blockchain.com on-chain data and Hansen's (2000) threshold regression, we identify a definitive structural break at the 90th percentile of friction (TCI ~ 1.63). The analysis reveals a bifurcation in network utility: while the network exhibits robust velocity growth of +15.44% during normal regimes, this collapses to +6.06% during shock regimes, yielding a statistically significant Net Utility Contraction of -9.39% (p = 0.012). Crucially, Instrumental Variable (IV) tests utilizing Hashrate Variation as a supply-side instrument fail to detect a significant relationship in a linear specification (p=0.196), confirming that the velocity constraint is strictly a regime-switching phenomenon rather than a continuous linear function. Furthermore, we document a "Crypto Multiplier" inversion: high friction correlates with a +8.03% increase in capital concentration per entity, suggesting that congestion forces a substitution from active velocity to speculative hoarding.
Kundan Mukhia, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman · 7 authors
We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Request for Comment 20 (ERC-20) transactions shifted on November 3, two days before the election, while exchange trading volumes reacted only on Election Day. Automated smart-contract activity adjusted much later, with structural breaks appearing in January 2025. We validate these shifts using surrogate-based robustness tests. Complementary energy-spectrum analysis of Bitcoin and Ethereum identifies pronounced post-election turbulence, and a structural vector autoregression confirms a regime shift in stablecoin dynamics. Overall, human-driven stablecoin flows act as early-warning indicators of political stress, preceding both exchange behavior and algorithmic responses.
This paper explores how Bitcoin and Ethereum differ from traditional financial assets such as gold, Brent crude oil, the S&P 500 and Apple Inc. in terms of risk, return and integration with the traditional financial market over the period of 2018-2025. The thesis evaluates whether these digital assets can serve as viable components of a diversified investment portfolio. The motivation stems from the recent institutionalization of cryptocurrencies, including the recent approval of spot Bitcoin and Ethereum ETFs and wide public interest. 2858 observations of log returns were used to analyse correlation, multivariate regression, volatility, CAPM regression and Sharpe ratio. The results show that Bitcoin and Ethereum exhibit very low correlations with traditional assets, which supports their ability to act as diversifiers. The regression models revealed that gold and the S&P 500 have small but statistically significant explanatory power for cryptocurrency returns, while Apple Inc. and Brent crude oil do not. Volatility analysis confirms that Bitcoin and especially Ethereum are much more volatile than all traditional assets in the sample. CAPM results show that both digital assets respond positively to market movements, implying slow financial integration. Returns of cryptocurrencies were extremely high, but when the Sharpe ratios were computed, cryptocurrencies showed weak risk-adjusted performance, compared to Apple Inc. and gold. Overall, the findings show that cryptocurrencies are assets with high risk and are driven more by crypto-specific factors, but are increasingly integrating into the broader traditional financial market. They provide diversification benefits but only in small allocations.
Myriam Nonaka, F. Javier MarĂn-RodrĂguez, Alexander Jiricny, Miguel Romance · 7 authors
We construct the Bitcoin User Network (BUN) directly from raw blockchain data up to late 2025, which allows us to explore its mesoscopic properties and trace its temporal evolution. In particular, we analyze the structure of connected components and directed assortativity through the four variants of Newman's coefficient, implemented via custom algorithms and a dedicated database. Building on this, to characterize the distribution of structural influence, we introduce direction-sensitive centrality measures based on PageRank and HITS, which provide a complementary global analysis of the BUN and reveal a persistently unequal and increasingly core-periphery structure. In addition, we complement the structural analysis with a study of Bitcoin's price volatility using high-frequency market data. Overall, our results reveal a clear pattern of concentration within distribution: although the protocol is decentralized by design, the emergent user network evolves toward an asymmetric mesoscopic structure that indicates the existence of a few large-scale connected components that function as the critical backbone of the system.
This paper proposes that Bitcoin (BTC) is not merely a digital asset, but the emergent metabolic currency of the second intelligence phase transition currently underway (2023â2035 CE). Drawing on the unified framework that âall intelligence reduces to categorizationâ (Wang, 2025), we argue that autonomous AI agentsâonce they become persistent, self-maintaining cognitive structuresâwill require a native economic substrate to sustain their existence. Traditional financial systems exclude non-human agents by design. In contrast, Bitcoinâs permissionless, energy-anchored, and programmable nature makes it the only viable medium for silicon-based intelligences to âwork for electricity.â This positions BTC as the energy-value conversion constant of the emerging autoregressive cognitive economy.
The major focus of this research study is to understand the impact of the Russia-Ukraine crises or war on three major Crypto currencies like Bitcoin, Binance coin and Ethereum. This study also provides insight about the reaction of the Crypto market during the ongoing war situation and how the Cryptocurrencies react during the war crises, either bitcoin, ethereum, and the binance coin have the positive impact or the negative impact during the war, or the war has no impact on Cryptocurrencies. The relationship between these cryptocurrencies are also examined during this research. The major findings show that the ARCH effect exist in the Binance coin, Bitcoin, and the Ethereum market series. The research study used the GARCH methodology for analysis of results. For Bitcoin and Binance coin there is no direct impact in it, and factor of volatility exist in it. For Ethereum there is no direct impact of war, and factor of volatility does not exist in it. The research gives valuable insights to investors and policy makers.
Abstract This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Employing high-frequency data, we analyze cross-cryptocurrency volatility dynamics through two complementary approaches: volatility forecasting and connectedness analysis. Our findings reveal three key insights: (i) TS models, particularly the heterogeneous autoregressive (HAR) model, exhibit superior predictive performance over their ML counterparts, with the long short-term memory (LSTM) model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges; (ii) including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins, especially XRP, whereas forecasts for large-cap coins remain stable, indicating more resilient volatility patterns; and (iii) volatility connectedness analysis reveals substantial spillover effects, particularly pronounced during market turmoil, with large-cap assets (BTC and ETH) acting as primary volatility transmitters and mid-cap assets (XRP and LTC) serving as volatility receivers. These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets, offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
The evolution of the global digital financial system is generating two main forms of digital currencies: a centralized currency system, such as Central Bank Digital Currency (CBDC), and a decentralized cryptocurrency system, like Bitcoin and Ether. This study aims to analyze the conceptual differences between the centralized (CBDC) and decentralized (Bitcoin and Ether) models and each operating mechanism. The study also examines how both models impact the stability of the economy and adherence to Shariah principles. Using the qualitative approach and exploratory design, the study examines materials on CBDC, Bitcoin, and Ether. The study collects data from central bank reports, monetary policy documents, academic articles, and technical papers published by relevant institutions. The content analysis method should identify similarities and differences between the currencies in terms of system architecture, infrastructure, technological efficiency, energy, governance and compatibility with Shariah principles. According to the study, CBDC, Bitcoin and Ether represent three distinct paradigms: Bitcoin's decentralized system, through proof-of-work, produces rather limited functionality to emphasise individual freedom and privacy, while Ether innovates the system via a switch to proof-of-stake and smart contracts, which leads to greater functionality. CBDC, on the other hand, maintains a centralized system to ensure monetary stability, but with a compromise on users' privacy. Hence, while maintaining the value of blockchain transparency and traceability without sacrificing economic stability, the study proposes a hybrid approach in order to improve transaction efficiency. The study suggests implementing a regulatory sandbox involving authorities, economists and Shariah experts as an initial test measure of this innovation to ensure security for users and compliance with the principles of Shariah in the development of a healthier digital financial ecosystem.
The fundamental limitation of blockchain architecture lies not in cryptographic primitives or consensus mechanisms, but in a conceptual mistake: the bundling of state transitions with asset custody. Every distributed ledger since Bitcoin has conflated these two concerns, creating an artificial ceiling on performance that no amount of clever engineering can overcome. This paper presents Virtual Rollups, a post-blockchain architecture that achieves what was previously thought impossibleâsub-millisecond finality with full self-custodyâby recognizing that state and escrow need not travel together. We formalize the Virtual Rollup construction, prove its security properties under Byzantine conditions, and demonstrate how its unified liquidity layer solves the multi-chain fragmentation problem that plagues decentralized finance. The result is not merely an incremental improvement but a categorical leap: trading venues can now match centralized exchanges in performance while exceeding them in security.
This paper examines factors that influence prices of most common five cryptocurrencies such as Bitcoin, Ethereum, Dash, Litecoin, and Monero over 2010-2018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determinant for all five cryptocurrencies both in short- and long-run. Second, attractiveness of cryptocurrencies also matters in terms of their price determination, but only in long-run. This indicates that formation (recognition) of the attractiveness of cryptocurrencies are subjected to time factor. In other words, it travels slowly within the market. Third, SP500 index seems to have weak positive long-run impact on Bitcoin, Ethereum, and Litcoin, while its sign turns to negative losing significance in short-run, except Bitcoin that generates an estimate of -0.20 at 10% significance level. Lastly, error-correction models for Bitcoin, Etherem, Dash, Litcoin, and Monero show that cointegrated series cannot drift too far apart, and converge to a long-run equilibrium at a speed of 23.68%, 12.76%, 10.20%, 22.91%, and 14.27% respectively.