In this paper, we propose a distribution-free test for detecting changepoint in the mean direction of angular data. The uncertainty in angular measurements is quantified through the \textit{square of an angle}, derived from the intrinsic geometry of the torus. It is established that, under the null hypothesis, the test statistic distributionally converges to the Kolmogorov distribution, while under the alternative hypothesis, both the consistency of the test and the asymptotic properties of the changepoint estimator are established. Through extensive simulations, we compare the empirical performance of the proposed method with two existing approaches for angular data and further benchmark it against a test based on the circular arc length distance. Finally, we demonstrate the practical utility of our approach by analyzing the timestamps of extreme events in Bitcoin, Ethereum, and Gold price datasets, where the continuous, high-frequency nature of the data is modeled in the circular framework.
Abstract: The evolution of monetary systems has transformed human civilization from simple barter exchanges to sophisticated digital financial ecosystems powered by blockchain technology. This review examines how barter systems evolved into con-temporary virtual currencies across history and assesses how cryptocurrencies fit into the circular economy. The study explores the shortcomings of conventional monetary systems and looks at how decentralized, transparent, and effective forms of economic transaction have been made possible by digital currencies like Bitcoin. Additionally, the study examines how blockchain technology might be used to support waste reduction, sustainability, resource efficiency, and transparent supply chain management. The study also assesses the difficulties posed by virtual currencies, such as market volatility, cybersecurity threats, regulatory ambiguity, and environmental issues pertaining to cryptocurrency mining. The review identifies significant research gaps and future prospects for incorporating virtual currencies into sustainable economic systems by synthesizing the body of existing work. The results indicate that through openness, decentralization, and technological innovation, blockchain-enabled financial systems have a great deal of potential to promote circular economy goals. Keywords: Virtual Currency, Cryptocurrency, Bitcoin, Blockchain, Circular Economy, Sustainable Finance, Digital Economy, Decentralization, Green Finance, FinTech, Supply Chain Management
Bitcoin is a digital asset with a high level of volatility, making it important to analyze using volatility models. This study aims to analyze the volatility of Bitcoin returns using the ARCH-GARCH model during the period January 2020 to April 2026. The data used are daily closing prices of Bitcoin (BTC-USD) obtained from Yahoo Finance and processed using RStudio. The analytical methods employed include descriptive statistical analysis, stationarity testing, ARIMA modeling, ARCH effect testing, and volatility modeling using ARCH-GARCH. The results show that Bitcoin price data are non-stationary, while Bitcoin return data become stationary after return transformation. Based on model selection using the AIC criterion, the best ARIMA model obtained is ARIMA(1,0,1). Residual testing indicates the presence of ARCH effects, therefore GARCH modeling is applied. From the comparison of several GARCH models, GARCH(1,1) is selected as the best model with an AIC value of -4.161214. The analysis also indicates that Bitcoin return volatility is persistent, with a value of α₁ + β₁ equal to 0.978169. In addition, forecasting results show that Bitcoin volatility is expected to remain high in future periods, indicating that Bitcoin is a digital asset with a high level of investment risk.
Imagine the hunt to predict Bitcoin&s;s wildly swinging price as a high-stakes competition among four clever computer programs, because investors really need to know where it&s;s headed to make smart plans. Our study pitted these programs—the classic ARIMA, the modern Facebook Prophet, the powerful XGBoost, and the deep-learning LSTM network—against each other to see which could best guess future Bitcoin prices. Using two main report cards, the MAE and RMSE scores, we found that Prophet and ARIMA were neck-and-neck, but the XGBoost model completely missed the mark, proving highly inaccurate with very high error scores. However, the true champion turned out to be the LSTM neural network, which blew the others out of the water by delivering the lowest error scores on both test and training data, essentially making it the most reliable tool for anyone looking to build a winning strategy in the tricky world of crypto trading.
George Thomas Sofras, Ourania Theodosiadou, Theodora Tsikrika, Stefanos Vrochidis · 5 authors
The increasing use of cryptocurrencies, especially Bitcoin (BTC), has created new challenges for financial investigation. Although blockchain transactions are publicly accessible, the pseudo-anonymous nature of cryptocurrency networks can facilitate illicit financial activity. This work explores anomaly detection in the Bitcoin network using a semi-supervised Long Short-Term Memory Autoencoder (LSTM-AE). The focus is on the analysis of wallet activity over time in order to capture temporal behavioral patterns that may be related to illicit activities. Experiments are conducted on the Elliptic++ dataset. The model is trained exclusively on licit behaviour and the results indicate that the proposed formulation is able to retrieve a large proportion of illicit wallets despite the highly imbalanced setting.
Cryptocurrencies have emerged as a prominent asset class characterized by rapid price fluctuations, growing institutional participation, and continuing debate over whether their price movements are random or predictable. This study examines the randomness and weak-form market efficiency of the top ten cryptocurrencies by market capitalization—Bitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, Solana, TRON, Dogecoin, and Hype liquid—using daily closing price data from April 2016 to March 2026 (subject to data availability for each coin). Daily log returns were tested using Descriptive Statistics, the Jarque–Bera test of normality, the Wald–Wolfowitz Run Test, and the Autocorrelation Test. The results show that daily returns for all selected cryptocurrencies are non-normally distributed, exhibiting excess kurtosis and skewness. The Run Test results indicate that seven of the ten cryptocurrencies—Bitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, and Dogecoin—do not follow a random walk, while Solana, TRON, and Hype liquid exhibit randomness consistent with weak-form efficiency. However, the Autocorrelation Test reveals strong positive serial correlation across all ten cryptocurrencies, indicating that the market falls short of weak-form efficiency. The study concludes that the cryptocurrency market provides mixed and largely inefficient evidence with respect to the Random Walk Hypothesis, implying that historical price information may retain some predictive value for investors.
When we consider the PoW (proof-of-work) in the Bitcoin blockchain, how is the work calculated? How does this work convert to energy quantities? This paper demonstrates that in the Bitcoin blockchain, "Proof-of-Work" (PoW) is not a complex calculus equation, but rather a probabilistic brute-force search. Miners repeatedly run block header data through a cryptographic hash function, tweaking variables until they output a number that meets a strict network threshold. In the Bitcoin blockchain, Proof-of-Work (PoW) is a probabilistic brute-force search where miners repeatedly run block headers through a double SHA-256 hash function to find an output below a global target threshold. The mathematical "work" is quantified by the network Difficulty (D), requiring roughly D × 2³² expected hashes per block. To convert this cryptographic effort into physical energy, the global network hashrate is first derived by dividing total block hashes by Bitcoin’s 10-minute target block time (600 seconds). This computational rate is then bridged to the physical world using hardware efficiency—measured in Joules per Terahash (J/TH)—multiplied by operational time. Because modern semiconductor ASICs operate roughly seven orders of magnitude above the absolute thermodynamic limits outlined by Landauer's principle, nearly all electricity consumed by this cryptographic pipeline directly converts into waste heat. The calculation of this work, how it translates mathematically to network metrics, and how those metrics convert into physical energy quantities is the discussion of this paper.<b>Part 1: How the "Work" is Calculated</b><b>1. The Hashing Puzzle (Double SHA-256)</b>A miner constructs a block header containing transaction data, a timestamp, the hash of the previous block, and a changing variable called a nonce. They pass this header through the SHA-256 algorithm twice:<br>H(x) = SHA-256(SHA-256(Block Header))The resulting output is a 256-bit unsigned integer, typically represented as a 64-character hexadecimal string.<b>2. The Target (</b><b>T</b><b>)</b>The network enforces a global threshold called the Target (T). For a block to be accepted, the hash output interpreted as a massive 256-bit integer must satisfy:<br>Hash Output ≤ T<br>Because the output of a cryptographic hash function is completely random and uniformly distributed, miners cannot predict the output. Finding a valid hash is essentially a Bernoulli trial (like rolling a die with an astronomical number of sides).<b>3. Mathematical Definition of Difficulty (D)</b>Because the Target T is a massive 256-bit number that changes every 2,016 blocks, Bitcoin uses a human-readable metric called Difficulty (D), scaled relative to a baseline "genesis" target (T<sub>max</sub>).<br>T<sub>max</sub> = 0x00000000FFFF0000000000000000000000000000000000000000000000000000The difficulty formula is D = T<sub>max</sub>/TAs the network gains more miners, T drops (becomes smaller), making hashes harder to find, which increases D.<br>The expected number of hashes E[hashes] required to find a valid block at a given difficulty is proportional to D:E[hashes] = D × 2³² × T/T<sub>max</sub> (scaled to baseline expectations)<br>More simply, the total expected hashes per block is roughly:Expected Hashes ≈ D × 4.295 × 10⁹<b>Part 2: From Computational Work to Energy Quantities</b>Energy consumption is a byproduct of hardware efficiency operating over a span of time to execute these hash attempts. There is no direct algorithmic conversion from a hash to Joules in the protocol code; instead, the conversion bridges cryptographic operations and thermodynamic hardware efficiency.<b>Step 1: Calculate Total Network Hashrate (H</b><sub><strong>net</strong></sub><b>)</b>The global hashrate represents the total number of hashes computed per second across all active machines globally. It is derived directly from the current difficulty (D) and Bitcoin's target block time (t = 600 seconds or 10 minutes):<br>Hashes per block = D × 2³²<br>Network Hashrate (H<sub>net</sub>) = D × 2³²/600 [hashes/second or H/s]<b>Step 2: Factor in Hardware Efficiency (EF)</b>ASIC (Application-Specific Integrated Circuit) miners dominate Bitcoin mining. Their electrical efficiency is measured in Joules per Terahash (J/TH) or Watts per Gigashash. Let the aggregate hardware efficiency of the network be denoted as EF (expressed in Joules per Hash, J/H):EF = Total Power Consumption (Watts)/Hashrate (H/s)<b>Step 3: Energy Derivation Formula</b>To calculate the total energy consumed by the entire Bitcoin network over a specific timeframe (e.g., 1 second, 1 day, or 1 year), we multiply the network hashrate by the hardware efficiency and time (t):<br>Energy (E) = H<sub>net</sub> × EF × Δ tSubstituting H<sub>net</sub> into the equation:<br>E = (D · 2³²/600) × EF × Δ t<br>For example, assume a network difficulty (D) of roughly 80 × 10¹² (80 trillion). Also, assume an average fleet hardware efficiency (EF) of 25 Joules per Terahash (25 × 10⁻¹² J/H). Calculate energy consumed over 1 day (Δ t = 86,400 seconds):Hashes/sec = 80 × 10¹² × 4,294,967,296/600 ≈ 5.72 × 10²⁰ H/sPower (Watts) = (5.72 × 10²⁰ H/s) × (2.5 × 10⁻¹¹ J/H) ≈ 14,300,000,000 W = 14.3 GWEnergy over 1 day = 14.3 GW × 24 hours ≈ 343.2 GWhThe summary of the conversion pipeline may be expressed as<br>Target (T) ⟶ Difficulty (D) ⟶ Network Hashrate (H<sub>net</sub>) ⟶× Hardware Efficiency (J/H)⟶ Power (Watts) ⟶× Time⟶ Energy (Joules/kWh)<b>Part 3: Thermodynamic Limits and Efficiency Bounds (Landauer's Principle)</b>To fully connect cryptographic work to physical energy, we can look at the theoretical minimum energy required by the laws of physics to perform computation.<b>1. Landauer's Principle</b>Landauer's principle establishes the minimum possible amount of energy required to erase or irreversibly manipulate a bit of information at a given temperature (T<sub>temp</sub>):<br>E<sub>min</sub> = k<sub><em>B</em></sub> T<sub>temp</sub> ln(2)k<sub><em>B</em></sub> is the Boltzmann constant (1.380649 × 10⁻²³ J/K).T<sub>temp</sub> is the absolute temperature of the environment (e.g., 300 K).For a single bit modification at room temperature, this absolute thermodynamic floor is roughly 2.8 × 10⁻²¹ Joules per bit.<b>2. Comparing SHA-256 to the Thermodynamic Limit</b>A single SHA-256 calculation involves processing a 512-bit message block through 64 rounds of complex logical operations (bitwise additions, rotations, and shifts), manipulating hundreds of thousands of bits cumulatively.Theoretical minimum energy per hash: Factoring in the sheer number of bit operations inside SHA-256, even a reversibly ideal computer would require thousands of bit manipulations, putting a strict physical floor on a single hash well above Landauer's limit (roughly on the order of 10⁻¹⁹ to 10⁻¹⁸ Joules per hash under optimal theoretical conditions).Actual ASIC efficiency: Modern state-of-the-art ASIC miners (like the Bitmain Antminer S21 series) operate around 15 to 20 J/TH (1.5 × 10⁻¹¹ Joules per hash).Comparing real-world hardware (10⁻¹¹ J/H) to absolute physical limits (10⁻¹⁸ J/H) reveals that current silicon-based semiconductor technology is roughly 7 orders of magnitude away from theoretical thermodynamic efficiency—meaning nearly all energy put into Bitcoin mining converts directly into waste heat.<b>Part 4: Complete Comprehensive Master Equation</b>Combining all components into a single macro-equation, the total daily electrical energy (E<sub>day</sub>) consumed by the global Bitcoin network can be calculated directly from the network's current Difficulty (D) and the average hardware efficiency fleet-wide (EF<sub>avg</sub> in J/TH):E<sub>day</sub> = (D · 2³²/600) × (EF<sub>avg</sub> × 10⁻¹²) × 86,400<br>Where:<br>D · 2³² / 600 yields the Network Hashrate (hashes/sec).EF<sub>avg</sub> × 10⁻¹² scales Joules-per-Terahash down to Joules-per-Hash.86,400 converts seconds into one full day.This mathematical coupling ensures that as network security (Difficulty D) scales up over time to attract more capital and hashpower, energy consumption scales linearly with it, modulated only by the parallel improvement rate of semiconductor manufacturing efficiency (EF<sub>avg</sub>).To recap the end-to-end framework:The Work: Quantified by the difficulty D and scaled via 2³² to determine total expected hashes per block.The Hashrate: Derived by dividing total hashes per block by the target 10-minute block time (600 seconds).The Energy Conversion: Bridged physically using the hardware's efficiency metric (Joules per Terahash, or J/TH) multiplied over time.The Physical Bound: Bounded by thermodynamic limits like Landauer's principle, explaining why modern ASICs produce the massive amounts of waste heat characteristic of the Bitcoin network.
Active asset managers increasingly include cryptocurrencies in their alternative asset allocations, highlighting their speculative and volatile nature. The aim of this research is to examine trends in the returns and volatility of cryptocurrencies, whilst accounting for the depegging of stablecoins, driven by speculative trading during macroeconomic shocks and technological shifts. We build a sample of market capitalisation, using data from the daily closing prices of Bitcoin (BTC), Ethereum (ETH), Binance (BNB), and Ripple (XRP), two fiat-backed stablecoins (USDT and USDC), and a cryptocurrency-collateralised stablecoin (DAI). As a first step, a Granger-causality framework is applied to examine the influence of stablecoin depegging events on crypto returns during financial market stress. The results are strongly asymmetric: there is little evidence that depegs predict returns; whereas cryptocurrency returns robustly Granger-cause USDC depegging events, an effect that intensifies during periods of market stress. Stablecoin depegs appear to be a downstream symptom of cryptocurrency stress rather than a leading indicator of it. The analysis was extended by modelling volatility, using an EGARCH-X model to study whether depegs also affect crypto during periods of market stress and if larger deviations from the dollar peg are associated with higher cryptocurrency volatility, concentrated in the most liquid stablecoins (USDT and USDC), while the evidence for any change in this association during stress is limited. The findings carry implications for risk monitoring in digital-asset markets, where stablecoin behaviour reflects, rather than anticipates, cryptocurrency market conditions.
Since 2008, when the cryptocurrency was first introduced under the name Satoshi Nakamoto, more and more people are interested in the «new money» – Bitcoin. Bitcoin is the first cryptocurrency and although many other cryptocurrencies were created and will be created in the future, Bitcoin remains the most popular cryptocurrency to this day. Naturally, along with the rapid growth of information technologies and their applications, many new «computerized» currencies will emerge. Because anyone can buy and sell cryptocurrency (e.g. bitcoin) and, thus, cryptocurrency is a subject of trade, hence cryptocurrency and in particular bitcoin is a product. Naturally, questions arise about the determinants of cryptocurrency price changes. In particular: Are the changes in the prices of cryptocurrency (and in particular Bitcoin) related to the development trends of the global economy? Are changes in the prices of cryptocurrency (and in particular Bitcoin) related to indicators of the state of the global economy, such as the well-known indices DJII, Nasdaq, S&P 500 and others. Thus it is interesting to see whether it is possible to predict changes in the prices of cryptocurrencies (and in particular Bitcoin) using different methods of time series.
Because Bitcoin typically exhibits higher volatility than traditional assets, evaluating and managing its risk is essential. We estimate Bitcoin’s potential maximum drawdowns (MDDs) using Monte Carlo simulations based on a stochastic jump process and assess the likelihood of substantial declines in the coming years. Based on our results, the simulation results suggest that an MDD of at least 60% is highly probable within three to four years, while an MDD of at least 70% appears plausible within five years. Moreover, our sensitivity analysis indicates that the MDD of Bitcoin is most strongly influenced by jump intensity. These results offer critical insights for market participants seeking to analyze Bitcoin’s downside risk and formulate strategies to navigate potential market downturns.
This paper investigates whether prediction market settlements create incentives for temporary price pressure in Bitcoin spot markets. Using high-frequency data from February 2025 to January 2026 and actual contract-level data from Polymarket and Kalshi to identify economically relevant contract strikes, we document basis divergence between settlement oracle exchanges (Coinbase) and non-constituent exchanges (Binance) during expiry windows. Employing a difference-in-differences framework with month fixed effects, we find that a one standard deviation increase in strike proximity is associated with a 6.7 basis point constituent exchange price deviation during settlement windows. The estimate is precise under the baseline minute-level HAC specification, while exact paired-month permutation inference based on 12 settlement events yields p=0.0256; equal-weight event aggregation produces a larger negative estimate, indicating event heterogeneity. Monthly directional patterns are suggestive, though stricter event-level and above-versus-below-strike tests provide mixed evidence on directional asymmetry. Taken together, these findings provide reduced-form evidence consistent with settlement-related incentives and may raise broader settlement-design considerations for decentralized financial systems. However, the analysis does not directly observe trader intent or the underlying mechanism.
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Mathematics, Computing, and Information Processing
With the improvement of data technology advances and the sharp addition of web customers number since the 90s, numerous computerized monetary standards are presented. the most popular among them is Bitcoin. It was decided to investigate the possible relations between the most popular cryptocurrency Bitcoin price dynamics and global Nasdaq index dynamics using Mathematical and Statistical methods. The main question is: Are the Bitcoin prices somehow related with Nasdaq Composite Index? We use both, Quantitative and Qualitative data analysis methods to answer this question: Namely, the Regression model and Non-Parametric testing. According to Quantitative methods, it was found that there exists a correlation and the regression equation is not bed: it seems that it is possible to explain about 60% of changes in Bitcoin Prices by changes in the Nasdaq Index. According to Qualitative methods, it was found that these two variables are independent. In this case, the Qualitative conclusion is more likely to be right, and the correlation is most likely because of coincidence.
This study examines whether connectedness among green bond returns, Bitcoin returns, market uncertainty, and geopolitical risk differs systematically across market states. Using a Quantile Vector Autoregression (QVAR) framework, we estimate connectedness across lower-tail, median, and upper-tail market conditions. To assess statistical reliability, we report bootstrap confidence intervals and difference-based tests and benchmark the quantile estimates against a conventional mean-based VAR. The mean-based benchmark closely matches connectedness around the median quantile. By contrast, system-wide connectedness is significantly stronger in both tails than around the median, as confirmed by difference-bootstrap tests. The direct green bond – Bitcoin linkage is stronger in the lower tail than under normal market conditions, although its net direction is not robustly identified across quantiles. Directional spillovers suggest a more prominent transmitting role for market uncertainty around the median and for geopolitical risk in the upper tail, although these differences should be interpreted cautiously. Overall, the findings indicate that conventional mean-based analysis adequately characterizes connectedness under normal market conditions but cannot capture the pronounced intensification of connectedness observed in the tails.
A decentralized system faces a fundamental governance tension: its governancerules are themselves amendable, which means that the meta‑rules stipulating howrules are modified are also at risk of being revised. Starting from the paradox ofself‑amendment uncovered by legal philosopher Peter Suber, this paper argues thatthis logical dilemma is not a purely philosophical speculation but a structural difficulty that repeatedly arises in the practice of blockchain constitutionalism. Underthe tenet that“code is law,”code‑based rules bear the meta‑governance functionsthat in a constitutional structure ought to be carried by constitutional provisions,yet code logically cannot set an insurmountable boundary for its own amendmentauthority. In response, this paper proposes a layered meta‑constraint security architecture: meta‑constraints are divided into an unmodifiable layer of logical constants, a layer of cognitive virtues formulated through community constitutionalprocedures, and a layer of value homeostasis adjusted through public deliberationand evolution; the trustworthiness of meta‑constraints is anchored in the logicalphysical isolation provided by trusted hardware roots. Through the institutionalization of procedures for identifying and attributing meta‑constraints, this paperdemonstrates how fork‑exit‑based social verification, cognition‑testing through independent auditing, and physical anchoring through multi‑key witness mechanismstogether constitute a mutually independent multi‑layered defense system. By examining the 21‑million‑coin supply cap of Bitcoin, the Ethereum EIP governanceprocess, and the constitutional crisis of The DAO incident as case studies, thispaper reveals the partial instantiation patterns of the three‑tier meta‑constraintarchitecture in existing systems and their failure boundaries. The paper concludesthat the long‑term security of a decentralized system ultimately depends not on theByzantine‑fault‑tolerance strength of its consensus algorithm, but on the completeness of its meta‑constraint architecture–that is, the existence of a set of boundariesthat are hierarchically protected in procedure, isolated and verified in hardware,and socially anchored in consensus, such that the combined cost of breaching themis raised to a level that no actor can afford within the expected life cycle of thesystem.
Byzantine Fault Tolerance (BFT) consensus is a foundational achievement indistributed systems theory, providing dual guarantees of safety and liveness forasynchronous networks with malicious nodes. However, this theoretical frameworkimplicitly relies on a presupposition that has not been sufficiently examined: allhonest nodes are homogeneous in their cognition of the protocol’sobjectives. Whena decentralized system evolves from a closed task-oriented network into an opengovernance ecosystem, the functional differentiation of nodes in storage strategies,verification preferences, and governance commitments deprives this presuppositionof descriptive validity. This paper does not deny the security contributions of BFT,but argues that security alone is insufficient to constitute a complete consensus.The full logic of consensus requires a complementary dimension: the capacity toaccommodate functional differentiation. Integrating recent empirical classificationstudies of blockchain nodes, protocol architecture design experiences that acknowledge functional differentiation, and Ostrom’s polycentric governance theory, thispaper proposes“Cognitive Niche Equilibrium”(CNE) as an extension of the consensus concept. System stability does not require all nodes to be isomorphic inevery function; rather, it requires the simultaneous satisfaction of three stabilityconditions: feedback anchoring, cross-validation, and evolutionary stability. Using Bitcoin and Ethereum as comparative cases, this paper translates these threeconditions into a layered implementation architecture symbiotic with existing BFTprotocol stacks, and discusses the security engineering principles and trade-offsunder this framework.
Fernando Frachone Neves, André Luiz Oliveira, Flávia Vancim Frachone MASSA, Tainara Adriani Ribeiro de Jesus · 5 authors
A proliferação da tecnologia blockchain e da mineração de criptomoedas tem gerado interesse de especialistas em sustentabilidade, emergindo um novo campo de estudo, desenvolvendo o conceito de criptomoedas verdes e a sustentabilidade digital. Neste sentido, este estudo realizou uma análise bibliométrica com o objetivo de mapear as tendências, estruturas temáticas e avanços na literatura científica sobre sustentabilidade digital no ecossistema blockchain, com foco em criptomoedas verdes. Para isso, foram analisados 133 artigos científicos extraídos do Web of Science (WOS), utilizando-se o software RStudio. Os resultados revelaram um crescimento acelerado de publicações, com um pico em 2024, indicando um campo de pesquisa em rápida expansão. As contribuições em pesquisa demonstram uma polarização, destacando a China e a Índia como principais polos. Temas dominantes incluem "cryptocurrency", "bitcoin", "blockchain technology", "green bonds", "clean energy" e "renewable energy", enquanto o mapeamento temático identificou "energy consumption", "risk" e "green challenges adoption" como temas motores. Esta revisão bibliométrica confirma o crescente interesse em criptomoedas verdes, impulsionado pela necessidade de mitigar impactos ambientais e alinhar a inovação tecnológica aos Objetivos de Desenvolvimento Sustentável (ODS) da ONU. Conclui-se que o estudo oferece percepções importantes aos formuladores de políticas, investidores e desenvolvedores, visando promover um desenvolvimento digital mais equitativo e alinhado à sustentabilidade.
Traditional distributed systems theory has long encoded hard forks as a signof consensus rupture and governance failure. This paper proposes an alternativeanalytical framework: in the practice of decentralized governance, a hard fork isnot a system malfunction but a structural mechanism through which incommensurable cognitive architectures achieve legitimate evolution via the separation ofconceptual space when a dispute touches upon the fundamental commitments ofthe protocol. The paper first redefines a fork as a jump of the authority to modify rules across governance levels—a soft fork adjusts parameters within existingconstraints, while a hard fork alters the boundaries of the constraints themselves,constituting a “dimensionality lift” operation in governance space. Second, it distinguishes three normative types of forks—consensual, controversial, and cognitivelyincommensurable—and argues that only the third type reaches the governancelimits of soft forks. Using the 2015–2017 Bitcoin block size war and the 2016 TheDAOincident as core cases, the paper reveals the internal dynamics through whicha controversial fork evolves from a parameter dispute into framework incommensurability, and how an extreme semantic crisis forces a community to confrontthe tension between code rules and substantive justice. Based on this analysis, thepaper proposes three normative criteria for fork legitimacy—feedback anchoring integrity, cross-verification operability, and conceptual-space appropriateness—andargues that forks, as an “exit-separation” mechanism, possess a meta-governancefunction in decentralized governance analogous to the right of exit in traditionalpolitical theory.
Ken Anadu, Pablo Azar, Sean Baker, Marco Cipriani · 9 authors
Stablecoins are digital assets whose value is pegged to that of a fiat currency, typically the U.S. dollar at a peg of $1.00 per token. In a previous blog post, we described the rapid growth of stablecoins through early 2025, highlighted changes in stablecoins’ reserve-asset composition, and examined their reactions to Bitcoin price shocks. In this post, we document the growth of stablecoins since our last post. Then, we examine how shocks from outside the crypto industry can impact the composition of stablecoins’ reserve assets. For our case study, we use the 2023 failure of Silicon Valley Bank (SVB) and its impact on USD Coin (USDC, issued by Circle), the second-largest stablecoin by market capitalization.
This study examines the short-run effects of U.S. monetary policy shocks on cryptocurrency returns and asks whether digital assets respond to conventional macroeconomic transmission mechanisms. Focusing on the post-2020 period, it evaluates the magnitude, direction, and persistence of Federal Reserve rate shocks across Bitcoin, Ethereum, Solana, Ripple, and TRON. The analysis applies an SVAR-X framework to daily data for January 2020-December 2025. Cryptocurrency log returns are treated as endogenous variables, while the U.S. Dollar Index and VIX are included as exogenous controls; federal funds rate changes are modelled as strictly exogenous policy shocks. Impulse-response results show positive and significant contemporaneous responses for Bitcoin, Ethereum, Solana, and TRON, but no significant reaction for XRP. These effects dissipate within days, indicating modest, short-lived, and heterogeneous monetary-policy transmission rather than persistent effects on cryptocurrency return dynamics over time.
Nghiên cứu sử dụng phương pháp nghiên cứu sự kiện (Event Study) để phân tích phản ứng của thị trường Bitcoin trước các sự kiện chính sách liên quan đến tiền kỹ thuật số tại Việt Nam. Thông qua việc đo lường lợi suất bất thường (AR) và lợi suất bất thường tích lũy (CAR) trong các cửa sổ sự kiện khác nhau, nghiên cứu đánh giá mức độ nhạy cảm của thị trường trước các tín hiệu chính sách và bất định thể chế. Kết quả cho thấy thị trường Bitcoin phản ứng đáng kể trước các thay đổi trong môi trường quản lý. Các sự kiện mang tính hỗ trợ hoặc định hướng xây dựng khung pháp lý cho tài sản số thường tạo ra CAR dương, trong khi các sự kiện liên quan đến kiểm soát hoặc tín hiệu pháp lý chưa rõ ràng tạo ra CAR âm và làm gia tăng biến động của thị trường. Nghiên cứu cũng cho thấy phản ứng của thị trường không chỉ xuất hiện trong ngày sự kiện mà còn kéo dài trong các phiên giao dịch tiếp theo. Kết quả nghiên cứu góp phần bổ sung bằng chứng thực nghiệm về vai trò của bất định thể chế đối với thị trường tiền kỹ thuật số trong bối cảnh Việt Nam. Sự biến động giá mạnh do các cú sốc chính sách đã làm gia tăng độ không chắc chắn trong quá trình đo lường giá trị hợp lý (Fair Value) của tài sản số, từ đó gây ra nhiều thách thức cho công tác ghi nhận, trình bày, kiểm toán và quản trị rủi ro của doanh nghiệp.
This study proposes a hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoin’s hourly and daily closing prices. Hourly BTC/USD market data spanning June 2021 to November 2025 were combined with approximately 326,000 Bitcoin-related news headlines published over the same period. Sentiment scores in the range of [-1, +1] were generated for each headline using FinBERT, a transformer-based language model trained on financial texts, and were subsequently integrated with technical indicators such as trading volume, MACD, and RSI. The resulting combined feature set was modeled using an LSTM network to capture temporal dependencies. Empirical results demonstrate that sentiment-enhanced hybrid models consistently outperform models based solely on technical indicators across RMSE, MAE, MAPE, and R² metrics. The hourly hybrid model achieved the best performance, with an RMSE of 1,009 USD and an R² of 99.23%. Furthermore, a 30-day out-of-sample real-time evaluation yielded an RMSE of 941 USD. The consistency between in-sample and out-of-sample results indicates that the proposed framework maintains stable predictive performance over time.