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» â 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 Bitcoinâs 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 Bitcoinâs 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.
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 NonÂ-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.
A decentralized system faces a fundamental governance tension: its governancerules are themselves amendable, which means that the metaârules stipulating howrules are modified are also at risk of being revised. Starting from the paradox ofselfâamendment 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 thatâcode is law,âcodeâbased rules bear the metaâgovernance 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 metaâconstraint security architecture: metaâconstraints 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 metaâconstraints is anchored in the logicalphysical isolation provided by trusted hardware roots. Through the institutionalization of procedures for identifying and attributing metaâconstraints, this paperdemonstrates how forkâexitâbased social verification, cognitionâtesting through independent auditing, and physical anchoring through multiâkey witness mechanismstogether constitute a mutually independent multiâlayered defense system. By examining the 21âmillionâcoin 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 threeâtier metaâconstraintarchitecture in existing systems and their failure boundaries. The paper concludesthat the longâterm security of a decentralized system ultimately depends not on theByzantineâfaultâtolerance strength of its consensus algorithm, but on the completeness of its metaâconstraint architectureâthat 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 protocolâsobjectives. 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 Ostromâs polycentric governance theory, thispaper proposesâCognitive Niche Equilibriumâ(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.
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 levelsâa soft fork adjusts parameters within existingconstraints, while a hard fork alters the boundaries of the constraints themselves,constituting a âdimensionality liftâ operation in governance space. Second, it distinguishes three normative types of forksâconsensual, controversial, and cognitivelyincommensurableâand argues that only the third type reaches the governancelimits of soft forks. Using the 2015â2017 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 legitimacyâfeedback anchoring integrity, cross-verification operability, and conceptual-space appropriatenessâandargues that forks, as an âexit-separationâ mechanism, possess a meta-governancefunction in decentralized governance analogous to the right of exit in traditionalpolitical theory.
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.
This study proposes a hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoinâs 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 RÂČ metrics. The hourly hybrid model achieved the best performance, with an RMSE of 1,009 USD and an RÂČ 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.
We examine the possible asymmetric relations between returns and changes in realized moments in the Bitcoin market by employing the quantile regression models (QRMs) which can account for investorsâ heterogeneity. First, our findings confirm the existence of asymmetric return-volatility relation in the BTC market. Second, regarding the relations between returns and realized skewness, the negative and positive returns show larger impacts in lower and upper quantiles, respectively. Third, the relation between return and kurtosis exhibits similar asymmetric pattern to that of return-volatility. The empirical findings can be supported by behavioral theories including representative bias and affect heuristics.
Blockchain is becoming an approachable data platform with several stakeholders having a shared history of transactions without being owned by an individual. The fundamental concepts of cryptographic hashing, peer-to-peer communication, and agreement protocols are sufficiently documented, and a lot of current research focuses on performance, security, privacy, and governance individually rather than as interacting dimensions. Recent blockchain research publications and deployments were structured based on a four-axis perspective that follows the dynamics of a system in terms of security, scalability, privacy, and governance. This lens was applied to consent mechanisms including proof-of-work, proof-of-stake, and practical Byzantine fault tolerance, and to techniques such as sharding and layer-2 that are designed to enhance throughput. Basic throughput calculations using reported block sizes, transaction sizes, and block intervals indicate that configuration limits are usually much larger than the transaction rates achieved in practice, implying that protocol overheads and network behaviour require a major share of the budget. A survey of attacks and defences indicates that increases in speed and programmability often expand the attack surface at the consensus and smart contract layers, which motivates the development of better analysis and monitoring tools. The results are applied to draw design insights for domains of finance, supply chains, healthcare, identity, and smart city platforms, and to highlight remaining problems in benchmarking and cross-chain coordination. A practical mapping of major blockchain platforms, namely Bitcoin, Ethereum, and Hyperledger Fabric, onto the four-axis framework was also provided to demonstrate its utility for platform comparison and selection.
Uncertainty plays a significant role in shaping investment decisions, both directly and indirectly. In an uncertain economic environment, investorsâ motivations for decision-making may vary. While some investors tend to seek safe-haven assets, others may engage in speculative behavior. Therefore, uncertainty can influence financial instruments through various mechanisms. One of these instruments is Bitcoin, which is often regarded as the âgoldâ of cryptocurrencies. Compared to traditional financial investment instruments, Bitcoin exhibits higher volatility and is among the primary assets that may be affected by uncertainty. However, an important question is whether this effect is temporary or permanent. The main objective of this study is to address this question by examining the causal nexus between Global Economic Policy Uncertainty (GEPU) and Bitcoin by employing a frequency-domain causality approach. In this context, the causal relationships between GEPU and BTC prices are examined for the entire period and for different sub-periods. Although the study's findings show no causal relationship between the variables over the entire period, the analyses for the short-, medium-, and long-run indicate a causal relationship from GEPU to BTC in the medium run. Accordingly, GEPU can be considered one of the factors affecting BTC price; however, its impact does not appear to be persistent.
This study investigates two questions relating to cryptocurrency market dynamics. First, whether a composite skew measure derived from MicroStrategy (MSTR) trading activity can predict future Bitcoin (BTC) and Ethereum (ETH) volatility. Second, whether Ethereum volatility exhibits reproducible structural properties consistent with established theories of volatility persistence and cascading shock dynamics. Using rolling out-of-sample testing, autocorrelation-adjusted significance testing, regime classification, shock-decay modelling, return-interval analysis, and earthquake-inspired cascade frameworks, the study finds no evidence that MSTR composite skew provides a useful forecasting signal. More broadly, no forecasting model tested outperforms naive benchmark models beyond horizons of approximately three to five days. However, several descriptive properties of Ethereum volatility appear robust, including volatility persistence, regime structure, extreme-event clustering, non-simple shock decay, and partially transferable aftershock dynamics. In particular, while Omori-style decay and the productivity law are supported, Bath's Law fails consistently, suggesting cryptocurrency volatility cascades may differ fundamentally from those observed in traditional financial markets. The findings contribute to the understanding of volatility organisation in digital asset markets while highlighting the difficulty of extracting persistent predictive signals from historical OHLCV
Every widely followed Bitcoin cycle indicator (Pi Cycle, MVRV, Mayer, Puell) called turns precisely for a decade, then degraded in one sequence: precise, then early, then silent. This is one structural phenomenon. Across the four halving epochs (2011-2026), the per-cycle maxima of five top-calling oscillators decline monotonically while minima end higher, so any threshold calibrated on past cycles must stop firing; short-horizon indicators decay toward zero and several invert sign; yet Bitcoin's time structure stays fixed, with mature-cycle tops 525/546/534 days after their halvings and bottoms 406/364/366 days after their tops. Turns are identified retrospectively by a fixed mechanical rule, not a real-time record. Timing-free nulls put the joint clustering at 5e-6 to 1e-3 across every variant. A harder empirical null (block-bootstrapped paths under the identical rule) never reproduces the top cluster under its deterministic construction (0 of 10,000); the bottom cluster is largely intrinsic to the drawdown process (31-43% of paths), so the evidence concentrates in top phase-alignment. In block height (the exact 210,000-block unit) the top null stays 0 of 10,000 and partial bottom structure emerges; shape and volatility overlays do not improve. A causal power law in time-since-genesis (exponent near 5.6) is the only signal whose sign is stable across mature epochs, replicates on a second source and Ethereum, and whose timing edge over buy-and-hold turns positive in the current cycle (one holdout, suggestive not decisive). We rest nothing on per-epoch significance: a rotation null shows HAC inference over-rejects here (size 0.33 at nominal 0.05; p=0.21). Macro drivers (M2, yield curve) show the same instability and lose a joint horse race. We pre-register falsifiable windows: a 2026 bottom (Oct 5-Nov 16) and a next top 525-546 days after the following halving.
This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, including Strategy (formerly MicroStrategy, MSTR), exhibiting a beta exceeding 1. We then classify firms into three groups reflecting their exposure to BTC, liquidity, and return co-movements. We use transfer entropy (TE) to capture the direction of information flow over time. Transfer entropy analysis consistently identifies BTC as the dominant information driver, with brief, announcement-driven feedback from stocks to BTC during major financial events. Our results highlight the critical need for dynamic hedging ratios that adapt to shifting information flows. These findings provide important insights for investors and managers regarding risk management and portfolio diversification in a period of growing integration of digital assets into corporate treasuries.
When is honest Bitcoin mining rational? This question is central to the incentive design of proof-of-work blockchains. Sapirshtein et al. computationally derived near-tight lower and upper bounds on the incentive-compatibility threshold using a Markov Decision Process. Kiayias et al.'s Blockchain Mining Games instead derived theoretical lower and upper bounds. However, this theoretical approach has two limitations: its model restricts miners to a narrow action space and assumes idealized tie behavior, and its lower and upper bounds are far from tight. We resolve both limitations. We develop a more realistic model with a broader miner action space and asymmetric tie-breaking parameters $Îł^-$ and $Îł^+$. We then propose an algorithm that computes lower and upper bounds on the incentive-compatibility threshold with a maximum error of $9.98006\times10^{-4}$.
We study the impact that two miners equipped with quantum computers purpose-built for quantum Bitcoin mining will have on the 51% attack threshold of the Bitcoin network, given that the miners are playing a competitive game against each other to be the first to mine a block. We extend an existing game-theoretic framework for Bitcoin mining and compute the resultant payoff matrices. From these payoff matrices, we determine optimal quantum mining strategies for two non-colluding and aggressive quantum miners with multiple opportunities at finding a valid block in an otherwise classical Bitcoin network. We show that these optimal quantum mining strategies have a negligible effect on the 51% attack threshold. The novelty of our work is the inclusion of the Aggressive Quantum Mining Strategy and the realistic approach of allowing the quantum miners to restart their search if their measurements do not yield a valid block when determining the optimal quantum mining strategies. Our result is important for evaluating quantum-mining threats on cryptocurrencies based on Proof-of-Work, e.g. Bitcoin
Diese Masterarbeit untersucht, ob Posts von Elon Musk auf Twitter (jetzt: X) die Bitcoin-VolatilitĂ€t beeinflussen können. Einige meinen, dass Musk in der Lage sei, den Bitcoin-Kurs mit einem einzigen Tweet zu beeinflussen. Deshalb untersuche ich diese Frage, indem ich die VolatilitĂ€t von Bitcoin modelliere und prognostiziere. DafĂŒr verwende ich ein heterogenes autoregressives Modell der realisierten VolatilitĂ€t (HARRV) basierend auf Hochfrequenz-Daten von Bitcoin-Preisen. Das Modell erweitere ich nicht nur durch Variablen, die fĂŒr die Tweets von Musk stehen, sondern auch durch andere. Beispielsweise eine Variable, die zwischen Wochentagen und Wochenenden unterscheidet und eine Variable, die die HĂ€ufigkeit der Google-Suchen nach dem Wort Bitcoin widerspiegelt. In der Masterarbeit zeige ich, dass Tweets von Elon Musk, die Interaktionen ĂŒber dem Durchschnitt aufweisen, einen starken signifikanten Effekt auf die realisierte VolatilitĂ€t haben. AuĂerdem zeigt sich, dass das HinzufĂŒgen der Tweets-Variablen zum HAR-RV-Modell dazu beitrĂ€gt, die Modellierung und Vorhersage der VolatilitĂ€t von Bitcoin zu verbessern.
In the Bitcoin system, transactions arrive continuously at miners' mempools and await inclusion in future blocks. Every non-coinbase transaction must spend one or more unspent outputs created by previous transactions, inducing dependency constraints among transactions in the mempool. At the same time, miners are economically incentivized to prioritize transactions with higher fee rates, measured as transaction fee per unit size. This paper formulates the mempool linearization problem: given a set of transactions with associated fees, sizes, and dependency relationships, compute a dependency-respecting transaction ordering that maximizes fee-rate efficiency while supporting efficient updates as the mempool evolves dynamically. The problem is characterized through a partition of transactions into disjoint dependency-respecting subsets ordered by decreasing aggregate fee rate, together with an equivalent LP formulation. Motivated by structural properties of basic feasible solutions in the simplex method, a new algorithm called spanning forest linearization (SFL) is developed. Operating directly on the transaction dependency graph, SFL iteratively merges and splits chunks of transactions to refine a global ordering, and is guaranteed to terminate at an optimal solution. Evaluation on both synthetic and real-world Bitcoin mempool data shows that SFL consistently computes optimal linearizations with substantially lower runtime than competing approaches, including a method based on the parametric preflow algorithm of Gallo, Grigoriadis, and Tarjan. These results indicate that SFL provides a practical and scalable framework for transaction prioritization by decentralized miners in large and rapidly evolving mempools. SFL has also been incorporated into the Bitcoin Core codebase for transaction cluster linearization.
Harlequin is a blockchain protocol in which the right to take part in consensus, governance and adjudication comes solely from reputation earned by verifiable acts â never from capital (proof of stake) or expended computation (proof of work). Reputation is a four-dimensional quantity ("the four suits"), computed deterministically from a public evidence record by a damped trust-propagation function, aggregated conservatively (a strong dimension cannot buy authority in a weak one), and subject to time decay so that standing must be continually re-earned. Block authorship and committee/jury membership are assigned by reputation-weighted cryptographic sortition; finality is provided by a Byzantine-safe gadget over signed votes; disputes are judged by sortitioned juries with interest-exclusion, and the only enforced consequence is reputational â the protocol applies no coercive force. We give the system model, the consensus and justice mechanisms, and a security analysis against a state-level adversary whose goal is capture, censorship or de-anonymization rather than direct theft. Two results are emphasized for their honesty. First, steady-state Sybil resistance is strong: a Sybil farm without earned evidence obtains about 0% of consensus power (17/17 adversarial tests). Second, the cold-start window is not unconditionally safe: a competent adversary present at genesis can capture the bootstrap; we show the security of that window is a race between honest onboarding and adversary mass â bounded, not eliminated, by non-operator personhood verification, an automatic ceiling-halt and the onboarding rate, with the residual risk declared. We report an implementation in Rust (dependency-free cores cross-validated against FRAME pallets) and a reproducible validation record spanning unit tests and multi-node hardware runs. v3 â post-launch revision. The network described here is no longer a design: the chain launched on 18 July 2026, with its genesis seed anchored to Bitcoin block 958536, and has been sealing blocks under the mechanisms this paper describes since. This revision corrects the emission schedule (per-era public ratios: 15/16 for HLQ, 3/4 for SOV, decoupled from the reputational decay constant), documents the launch facts and the first on-chain runtime upgrade executed through the paper's governance mechanism, and updates the evaluation with the live chain's validation record. Both English and Spanish editions are included; the English edition is the primary text.