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 investors’ 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, investors’ 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 “gold” 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 $γ^-$ and $γ^+$. 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-Volatilität 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 Volatilität von Bitcoin modelliere und prognostiziere. Dafür verwende ich ein heterogenes autoregressives Modell der realisierten Volatilität (HARRV) basierend auf Hochfrequenz-Daten von Bitcoin-Preisen. Das Modell erweitere ich nicht nur durch Variablen, die für die Tweets von Musk stehen, sondern auch durch andere. Beispielsweise eine Variable, die zwischen Wochentagen und Wochenenden unterscheidet und eine Variable, die die Häufigkeit der Google-Suchen nach dem Wort Bitcoin widerspiegelt. In der Masterarbeit zeige ich, dass Tweets von Elon Musk, die Interaktionen über dem Durchschnitt aufweisen, einen starken signifikanten Effekt auf die realisierte Volatilität haben. Außerdem zeigt sich, dass das Hinzufügen der Tweets-Variablen zum HAR-RV-Modell dazu beiträgt, die Modellierung und Vorhersage der Volatilität 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.
The aim of this study is to analyze the price dynamics of blockchain-based carbon credit tokens, namely Base Carbon Tonne (BCT), Moss Carbon Credit (MCO2), and KlimaDAO (KLIMA) as well as mainstream crypto assets such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Solana (SOL) and the speculative asset Carboncoin (CARBON). In addition, the Fear & Greed Index, which represents investor sentiment, has been incorporated into the model in line with the role of sentiment-driven effects in price formation processes in cryptocurrency markets, as highlighted in the literature. The study utilized daily closing prices from the period October 21, 2021, to November 1, 2025; correlation analyses were performed on raw daily price series using the Pearson correlation method, which was chosen to examine the direction and strength of the linear relationship between variables. Prior to modeling, the dataset was cleaned, Min-Max normalization was applied, and it was split into a 70% training set and a 30% test set while preserving chronological integrity. While the assumption of stationarity in time series is important from the perspective of classical econometric approaches, this study focuses on deep learning-based methods within the scope of nonlinear modeling frameworks. The data used in the study were obtained from Yahoo Finance and the AI Key API. The findings indicate that there are strong internal linkages among carbon credit tokens. In particular, while a strong positive relationship was observed between BCT and MCO2, it was determined that these tokens exhibit a weak negative correlation with Bitcoin. This suggests that carbon credit tokens are only marginally linked to the broader crypto market but form a more cohesive structure within their own ecosystem. Additionally, it was observed that the CARBON asset exhibits relationships ranging from weak to moderate with major crypto assets. The Fear & Greed Index, meanwhile, showed moderate relationships with BTC, ETH, and SOL, and weaker relationships with carbon credit tokens. During the modeling process, LSTM, GRU, Transfer-LSTM, and Transfer-GRU architectures were used; the data was split into 70% training, 30% validation, and 30% test sets while maintaining chronological integrity; the models were evaluated using MSE, RMSE, MAE, MAPE, and R² metrics. The results show that the GRU architecture generally offers the highest prediction accuracy, while transfer learning models perform relatively better in predictions for the KLIMA and Fear & Greed (F&G) Index. Overall, the study demonstrates that deep learning and transfer learning approaches are effective in modeling price behavior in tokenized carbon credit markets. Here, it is assessed that transfer learning does not automatically provide an advantage in every scenario, but offers strategic contributions for specific asset groups. In conclusion, the study demonstrates that AI-based models can be used as a decision-support mechanism in the pricing of sustainable financial instruments in the digital economy.
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 — 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 — 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 — 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 — 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.
Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3,491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting.
The growing presence of institutional capital in crypto asset markets has reopened the debate on whether Bitcoin and similar digital assets can act as safe havens, the way gold or sovereign bonds have been built historically. This thesis tackles that question with a quantitative framework rather than the qualitative arguments that dominated the early literature. The dataset covers November 2019 to May 2026 (2,380 daily observations for Bitcoin, 2,381 for the full asset universe). I fit GARCH-t and EGARCH-t models to capture conditional variance, apply Extreme Value Theory to isolate the tail directly, and run Monte Carlo simulation to estimate capital requirements over 30-day horizon.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
This chapter traces the intellectual and technological lineage of Bitcoin and digital money. While Nakamoto’s white paper launched Bitcoin, its roots extend through decades of economic theory, cryptographic innovation, and activist movements. We examine how the Austrian and Chicago Schools of Economics provided a framework for stateless and non-inflationary money, and how Cypherpunk ideals shaped the push for privacy and decentralization. The chapter reviews early experiments with digital currencies such as DigiCash, b-money, and e-gold, highlighting the technical shortcomings, regulatory battles, and user adoption barriers that prevented their success but furnished essential building blocks for Bitcoin. We then contrast the classical financial attributes of money—medium of exchange, unit of account, and store of value—with additional digital requirements such as offline spendability, identity-less spendability, and fungibility. Finally, we show how Bitcoin resolved the long-standing double-spending problem without a central authority through Proof of Work, situating it as both a culmination of earlier efforts and the starting point for a new era of cryptocurrencies.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
In this chapter, you will learn about the privacy limitations of public blockchains such as Bitcoin and Ethereum, and how these limitations have led to the development of privacy-focused cryptocurrencies. You will study the motivations for privacy coins and the risks posed by government-issued digital currencies. The chapter introduces and compares three major privacy coins: Zcash, Dash, and Monero. For each, you will explore their underlying technologies, including zero-knowledge proofs (zk-SNARKs), CoinJoin-style mixing, and ring signatures with RingCT and stealth addresses. You will also learn about consensus protocols, supply models, and the trade-offs each project makes between privacy, usability, and scalability. Finally, you will analyze the comparative strengths and weaknesses of these systems and understand the broader implications of privacy on blockchains.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
This chapter introduces blockchain network structures in both UTXO- and account-based systems. It begins with Bitcoin’s transaction and address graphs, showing how Satoshi Nakamoto’s design defines network topology and enables modeling through transaction graphs, address graphs, and chainlets. Privacy coins like Monero and Zcash extend this framework with ring signatures and zero-knowledge proofs that obscure data but still permit partial inference. Ethereum shifts focus to account-based networks, covering coin and token transactions, contract interactions, and trace analysis. Ripple concludes the chapter with credit networks built on trust lines and path-based settlements, showing how global credit flows can be modeled graphically.
Digital assets, a broad term encompassing crypto-currencies, tokens and digital representations of value, have transformed the financial landscape over the past decade. Ghana has transitioned from an unregulated crypto-currency environment to a structured, licensed digital assets space following the passage of the Virtual Asset Service Providers (VASP) Act 2025 Act 1154. Unlike traditional assets, digital assets exist exclusively in electronic form and are secured through cryptographic techniques, most notably blockchain technology. Bitcoin, Ethereum, and other crypto-currencies serve as prominent examples, alongside digital tokens used in decentralized finance (DeFi), security tokens, and stablecoins. They may serve a variety of functions, including use as a medium of exchange, for investment, or as a means of accessing goods, services, or applications within specific ecosystems. These assets include crypto-currencies, tokens, stablecoins, and other blockchain-based instruments. Global digital assets represent any item of value securely stored and managed via distributed ledger or blockchain technology. Encompassing cryptocurrencies, stablecoins, tokenized securities, and non-fungible tokens (NFTs), the sector has rapidly expanded into mainstream finance, revolutionizing global payments, portfolio diversification, and record-keeping. This article discusses the challenges and opportunities of digital currencies and the way forward. This research shows that digital currencies have advantages like making transactions faster, cheaper, and more accessible and also reveals a lot of disadvantages like creating major risks concerning compliance with regulations, cybersecurity, and potential impacts on monetary policy. The review emphasizes the necessity for robust regulatory frameworks for digital assets. It supports both innovation and stability for the digital currencies. It suggests that policymakers and financial institutions should adapt to changes and face the challenges by integrating digital currencies with existing systems. Overall, this review highlights the potential of digital currencies to transform finance. It also stresses the importance of focusing on the challenges they pose to ensure they can coexist successfully with traditional financial systems. As digital currencies evolve, the Ghanaian traditional financial sector faces pressure to adapt, with CBDCs, in particular, being explored as a secure, regulated alternative to volatile crypto-assets. nThe findings revealed that the central bank must adopt robust regulatory and licensing frameworks must align with Virtual Assets Service Providers (VASP) (Act 2025 Act 1154) by enforcing strict licensing for exchanges and custodians while adhering to AML/CFT (Anti-Money Laundering) directives. Also, the Bank of Ghana and the Securities and Exchange Commission must develop a comprehensive public education programme on the digital assets in the financial ecosystem. Given the novelty of the trend of criminality in the digital asset space, the establishment of specialized cybercrime courts to be presided over by judges, proficient in digital law and cybercrime would be of immense benefit. The mandate of such courts could be to expedite trials and ensure thorough adjudication of complex cyber cases. This would have the combined effect of empowering the Ghana Police Service and Cyber-Security Authority to fully invest time, money, and human resources towards the investigation of cybercrime, as well as serve as a deterrent for criminal elements, ultimately protecting our citizens and providing justice for those seeking redress.
Gouher Ahmed, Hamza Naim, Aqila Rafiuddin, Mohammed Nizamuddin · 5 authors
This study deals with the performance analysis and volatility estimation of conventional indices including Dow Jones, S&P 500, Brent Oil, Crude Oil and Gold and cryptocurrencies including Bitcoin and Ethereum for the period January 3, 2011 to November 26, 2021 for all of the indices except Ethereum for which the period chosen was from March 10, 2016 to November 26, 2021 due to late incorporation of the cryptocurrency. The stationarity, heteroscedasticity, and serial correlation of the data were considered. Time series regression using the GARCH model is applied for performance analysis and volatility estimation. GARCH (1, 1) estimates show the high performance of cryptocurrencies over the conventional indices, except Gold, which was insignificant, with Ethereum followed by Bitcoin being the most volatile among the different indices. However, Gold remains inert in response to the different indices. However, although the cryptocurrencies add to the country’s revenue, thus minimizing the deficits, there should still be proactive policies and practices to prevent the exploitation of stakeholders, especially for the sake of minority ones.
본 연구는 비트코인과 동북아시아 주식시장(한국, 중국, 일본) 간 동태적 상호의존성을 분석한다. 이를 위해 VAR 모형의 충격반응함수와 Diebold and Yilmaz(2009)가 제안한 전이효과 모형을 이용하여 금융시장 간 파급효과를 측정했다. 또한 코로나19 팬데믹의 영향을 분석하기 위해 분석 기간을 코로나19 이전, 코로나19 기간, 코로나19 이후로 구분했다.<br/> 주요분석 결과는 다음과 같다. 첫째, 코로나19 이전 기간에는 비트코인과 주식시장 간 연관관계가 제한적인 것으로 나타났다. 둘째, 코로나19 기간에는 금융시장 불확실성 확대와 글로벌 유동성 증가로 인해 비트코인이 주식시장에 미치는 전이효과가 크게 확대됐다. 셋째, 코로나19 이후 기간에는 연관관계가 코로나19 기간보다는 완화됐으나 코로나19 이전 기간보다는 높은 수준을 유지했다. 이는 점진적으로 비트코인이 주식시장과 통합되고 있음을 시사한다. 한편, 중국 주식시장의 경우 가상화폐 규제로 인해 비트코인의 파급효과가 상대적으로 제한적으로 나타났다.
Diky Paramitha, Etik Ipda Riyani, Nadhira Hardiana, Kan Wen Huey
Bitcoin has a tendency of price volatility that is much higher than other cryptocurrency assets, this makes a very significant difference from other financial assets that can go beyond conventional market logic thus creating a major obstacle in risk management. This study aims to dissect the extreme anomalies of bitcoin trading volume against the volatility of Bitcoin returns. Using a quantitative time series approach, the study analyzed monthly data on bitcoin price and trading volume using Bitcoin prices in the period February 2015 to December 2025. We assess volatility using the GARCH-X model to introduce trading volume as an exogenous variable. The basic GARCH shows significant volatility persistence, indicating a clustering of high volatility in Bitcoin's returns. This finding results that trading volume is not just a static transaction number but reflects a very crucial information proxy. Every movement of trading activity generates new signals in which aggressive price react. Trading volume is also highly correlated with the volatility of returns, although the volatility of the model indicates the need for careful interpretation. Bitcoin's volatility is not solely due to historical volatility dynamics, but also the impetus from trading activity, highlighting the need to consider accurate volatility modeling in the digital asset market. This research adds value by embedding trading volumes into the GARCH model to evaluate its contribution in explaining Bitcoin's volatility through empirical insights for investment decisions and risk management in the cryptocurrency market
Blockchain technology, originally devised to support the peer-to-peer transfer of Bitcoin, has evolved into a multipurpose digital infrastructure with far-reaching implications for business and finance. This paper undertakes a conceptual and exploratory examination of how blockchain is reshaping financial services, corporate governance, and commercial transactions. Drawing upon secondary literature, industry reports, and case illustrations, the study investigates blockchain applications across banking, cross-border remittances, supply chain finance, trade finance, capital markets, insurance, and decentralized finance (DeFi). It also discusses the enabling features of blockchain — decentralization, immutability, transparency, and smart contracts — that differentiate it from conventional centralized systems. The paper highlights the strategic benefits accruing to firms that adopt blockchain, including reduced transaction costs, faster settlement, enhanced traceability, and improved trust among counterparties, while also identifying barriers such as regulatory ambiguity, scalability constraints, energy consumption, and limited interoperability. The discussion synthesizes findings from extant studies to present an integrated view of blockchain’s transformative potential and its practical limitations. The paper concludes that while blockchain is unlikely to replace traditional financial infrastructure entirely in the near term, its selective and hybrid adoption is poised to redefine business processes, financial intermediation, and value exchange across industries.