The rapid increase in cryptocurrency adoption among Generation Z in Indonesia has raised concerns regarding investment decision-making in highly volatile digital asset markets. This study examines the influence of herding behavior, social media exposure, and fear of missing out (FOMO) on cryptocurrency investment decisions, with financial literacy as a moderating variable. A quantitative approach was employed using survey data from 200 Generation Z cryptocurrency investors in Pontianak City. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that herding behavior and social media significantly influence investment decisions. Fear of missing out also affects investor decision-making. Financial literacy moderates the relationship between herding behavior and investment decisions as well as between social media and investment decisions, but does not moderate the relationship between FOMO and investment decisions. These findings indicate that cryptocurrency investment decisions among Generation Z are influenced by social interactions and emotional biases.
Diana Bonilla Guzmán, Sofía de las Nieves García Gámez, Rubén Mora-Ruano, Alvaro-Antonio Salas-Suárez
This study aims to identify the extent to which a country's level of governance implicitly determines and encourages the use of cryptocurrencies, and the main elements associated with the use of alternative currencies to traditional ones. The methodology used is a descriptive analysis of the variables, an econometric analysis through an ANOVA, and the application of a truncated regression model, which aims to bring the research closer to the possible correlation between governance indicators and the rate of adoption of cryptocurrencies. The study concludes that countries with low levels of governance are directly related to the greater adoption of cryptocurrencies. To the best of our knowledge, this study is the first to analyse the relationship between cryptocurrency adoption and institutional governance by comparing two regions with different levels of development. The research is limited by the existence of other factors that influence the analytical framework of cryptocurrency adoption, but the availability of data has allowed the present study to focus on governance aspects. Now, despite the fact that the governance indicators present a global analysis in terms of their measurement, the relevant aspects of each country are not specified. The adoption of cryptocurrencies in some countries may not be strongly related to governance aspects but rather to the friendly regulations that have been implemented.
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.
The increasing adoption of cryptocurrencies has created new opportunities for digital financial innovation while simultaneously exposing individuals and institutions to sophisticated forms of financial fraud. Conventional rule-based fraud detection systems have become inadequate in addressing the dynamic and complex nature of blockchain-enabled financial crimes, leading to growing interest in the application of artificial intelligence (AI). This study systematically reviews the literature on artificial intelligence techniques for cryptocurrency fraud detection, with particular emphasis on their relevance to the Kenyan digital financial ecosystem. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. Peer-reviewed studies published between 2020 and 2026 were identified from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. Following the screening and eligibility assessment, 19 studies were included in the final qualitative synthesis. The findings reveal that machine learning, deep learning, hybrid AI models, and blockchain analytics significantly enhance cryptocurrency fraud detection by improving anomaly detection, transaction monitoring, predictive accuracy, and anti-money laundering compliance. Compared with traditional rule-based approaches, AI techniques provide faster, more adaptive, and scalable solutions capable of detecting evolving fraud patterns in decentralized financial systems. However, the review also identifies challenges relating to limited high-quality datasets, algorithmic bias, lack of explainability, cybersecurity risks, privacy concerns, and inadequate regulatory frameworks, particularly within developing economies. Furthermore, the review highlights a scarcity of empirical research focusing on cryptocurrency fraud detection in Kenya and identifies opportunities for developing localized datasets, explainable AI models, and context-specific regulatory frameworks. The study concludes that artificial intelligence has considerable potential to strengthen cryptocurrency fraud detection and financial security in Kenya, provided that technological, ethical, and regulatory challenges are adequately addressed. The findings provide valuable insights for researchers, financial institutions, technology developers, and policymakers seeking to enhance AI-driven fraud prevention within the country's evolving digital financial ecosystem.
Cryptocurrency has emerged as one of the most significant developments to accompany the digitization of global finance, and its footprint in India has expanded rapidly despite an unsettled regulatory environment. This paper examines how Indian investors perceive the opportunities and risks associated with cryptocurrency and blockchain technology, and evaluates whether their level of awareness shapes that perception. A structured questionnaire survey was administered to 158 respondents drawn from different age groups, educational backgrounds, occupations, and income levels in Karnataka, and the resulting data were analyzed using percentage analysis, frequency distribution, and the Chi-square test of independence. The findings indicate that a large majority of respondents, particularly those aged 21-30, view cryptocurrency and blockchain as tools capable of improving transparency, financial inclusion, and entrepreneurship, while simultaneously expressing concern over price volatility, cybersecurity threats, and unclear taxation rules. The Chi-square test confirmed a statistically significant association between investor awareness and perception of cryptocurrency (calculated value 19.41 against a critical value of 9.488 at 4 degrees of freedom and the 5 percent level of significance), leading to rejection of the null hypothesis. The study concludes that a clear, balanced regulatory framework combined with investor-education initiatives would allow India to capture the innovation potential of digital assets while containing the risks associated with their adoption.
As the growth of the FinTech platforms continues, there is an increasing demand for intelligent, secure and traceable solutions that can provide real-time detection of fraudulent transactions and shield financial records from manipulation. In this research, an Artificial Intelligence-powered blockchain framework, combining machine learning for fraud detection and permissioned blockchain for validation, was proposed. It was found that ensemble models performed better than a linear baseline. The overall best balance of precision, specificity and F1 score was obtained with the Random Forest model, and the highest precision–recall was obtained with the Extra Trees model, with fraud recall slightly better. In addition, feature-importance analysis revealed a small number of transaction attributes, which were anonymised, that most significantly affected fraud classification. The chosen model was then connected to a prototype of a chained hash blockchain that preserved the hashes of transactions, the time, the predicted probability of fraud, the validation result, and the version of the model. Through hash inconsistency, the prototype was able to detect any transaction modifications which might have been made on purpose and successfully ensured ledger integrity. The results illustrate how both AI and blockchain technologies complement each other. AI is effective in detecting fraud accurately and on time, and blockchain enhances the traceability, auditability and tamper resistance of transactions.
Cryptocurrency users have increasingly become targets of phishing and scam attacks. To mitigate these threats, leading crypto wallets (e.g., MetaMask) have introduced transaction simulation, which previews a transaction's balance changes before on-chain execution. While effective against traditional fund-draining attacks, we show that this defense can itself be exploited by a new phishing technique, which we term transaction simulation phishing. This attack uses carefully crafted smart contracts whose execution depends on dynamic blockchain state, causing simulations to display benign or profitable outcomes while the actual on-chain execution redirects users' funds to attacker-controlled addresses. We present the first comprehensive study of transaction simulation phishing. We first develop a taxonomy of phishing contracts that can be utilized to facilitate this attack. Then, we propose SIMGUARD, a bytecode-level detection system that combines static and dynamic program analysis to identify phishing contracts. Applying SIMGUARD to Ethereum, Binance Smart Chain, Avalanche, and Polygon, we detect over 4,000 phishing contracts deployed between August 2024 and June 2025. Our analysis identifies more than 5,700 victims and approximately $3.48 million USD in losses, 91.5% of which occurred on Ethereum. Moreover, our clustering result reveals that the largest phishing contract cluster alone accounts for about 83% of the total losses. These results expose a critical weakness in current wallet defenses and highlight the urgent need for more robust transaction simulation mechanisms.
Yanzheng Jin, Pengyang Shao, Xiaohao Liu, Xi Ai · 6 authors
Financial trading relies on extracting reliable signals from heterogeneous market modalities such as price series, breaking news, and investor sentiment. Existing multimodal methods primarily combine heterogeneous modalities to exploit complementarity, treating each modality as equally valuable while overlooking whether different modalities provide mutually supportive evidence for the same trading signal. However, this task-conditioned and non-canceling support, termed multimodal corroboration, is particularly valuable, especially for financial trading. Because individual financial views are noisy and weakly informative, support that persists across heterogeneous views may provide a more stable task-relevant signal than evidence appearing in only one view. To exploit this property, we propose CoLAS (multimodal Corroboration of Latent Asset Signals), a framework that operationalizes multimodal corroboration as a trainable task-conditioned representation for trading prediction. The modality representations are organized into a per-instance matrix, where a softmax-based spectral objective strengthens its dominant shared component. Signed modality contributions then determine whether this component provides non-canceling support and construct the resulting corroborated signal. A coupled robustness-aware consistency objective further preserves the resulting corroborated signal when a modality is corrupted or missing. Extensive experiments on stock and cryptocurrency datasets demonstrate the effectiveness of our proposed CoLAS, yielding consistent improvements in both annualized return and Sharpe ratio over existing methods.
Giuseppe Cavaliere, Thomas Mikosch, Anders Rahbek, Frederik Vilandt
This paper develops bootstrap inference for autoregressive conditional duration (ACD) models observed over a fixed calendar span, so that the number of durations is random. We study recursive schemes that either fix the calendar span or the realized event count. For the fixed-count bootstrap, we establish consistency when the duration tail index satisfies $κ\geq1$. When $0<κ<1$, classical consistency fails because the estimator has a mixed-normal limit, but the bootstrap reproduces its conditional Gaussian component. Consequently, basic percentile intervals remain first-order valid and bootstrap $t$-statistics are asymptotically standard normal. Monte Carlo experiments show accurate finite-sample inference across finite- and infinite-mean regimes and robustness to non-exponential innovations. An application to cryptocurrency ETF transaction durations finds strong persistence and illustrates the practical difference between fixed-count and random-count inference.
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
The advent of blockchain technology has created a paradigm shift in the way digital data can be securely, transparently and decentralized managed, a paradigm that could potentially replace the longstanding centralized digital information systems. This is a full journal-grade review of blockchain as a tool to ensure trustless, immutability and decentralized data governance in a variety of important application fields. The study, which is based on a systematic review of 20 peer-reviewed publications from 2023 to 2025, explores the essential structural elements of blockchain systems: Distributed ledger structures, cryptographic hash functions, Merkle tree integrity verification, consensus mechanisms, and smart contracts, and how they all contribute to removing single points of failure and institutional trust dependencies. There is a comparative study of the various public, private and consortium blockchain types, as well as the evaluation of the various consensus algorithms, such as Proof of Work (PoW), Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The results show that data management systems based on blockchain technology always have superior data integrity, access auditability, censorship resistance, and user data sovereignty properties compared to centralized systems, and come with trade-offs in scalability, energy efficiency, and compliance with regulations. Evidence collected for the application has come from health care organizations' record management, supply chain traceability, decentralized identity systems, Internet of Things (IoT) data integrity, an energy company data management system, and cybersecurity threat intelligence, among other contexts. Key challenges and emerging technologies, such as quantum computing systems, post quantum cryptographic standards, layer-two rollups, sharding and zero-knowledge proofs, are explored in tandem with the blockchain trilemma, GDPR compliance issues and cross-chain interoperability. The study finds that blockchain-based data management is moving from the experimental stage to becoming a core component to the digital economy's infrastructure.
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.
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
Reshma D’Souza, S Sheela, H S Sameena, S Jyothi · 6 authors
Abstract: In this era where technology is used to create unidentical counterfeit products, Finding the original objects is a very tedious task for the users. These Counterfeit Products affect the health of the user in the case of medical and skin care products also. This project implements Blockchain, a new Technology which is used to overcome this problem. Blockchain technology is the distributed, and immutable technology that provides data consistency and security. Here a QR code is generated for each product that is linked to the database which in turn is mapped to the chain nodes. By scanning this QR code the user can detect the original products amongst the fake ones. It highlights the need of cryptocurrency in the broader vision of supply chain security, elaborating on how Blockchain network, particularly using Ethereum Framework, provides a decentralized and transparent ledger for tracking and validating products.
Cryptocurrency's convenience is a convenient truth — granted here in full, with receipts. A permissionless ledger settles across borders without account approval, banking hours, or correspondent chains; Nakamoto designed exactly that, on purpose. The correction is that the convenience and the danger are the same property: what makes the transfer fast and unstoppable is that it is final — no chargeback, no administrator, no undo. Institutions can price that trade. A person cannot, and the proposal is that the rational personal policy is a wall, not a judgment call. Offered as a proposal, not a result.
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.
Christian Cachin, David Lehnherr, Juan Villacis, François-Xavier Wicht
Sender untraceability hides the account spent by a cryptocurrency transfer among a set of candidates, its masking set. What a transfer does to that set separates two designs: classical schemes retain the whole set and append a nullifier marking the spent account, so the ledger grows with every transfer; constant-state schemes instead consume and replace the entire set. We ask how this choice affects synchronization. We formalize the two designs as the linear and constant untraceable asset transfer objects (LUAT and CUAT) and locate them in the consensus hierarchy. In LUAT, transfers from distinct accounts commute. Its consensus number is 2, compared with 1 for standard asset transfer, independently of the masking-set size and of the untraceability notion, and LUAT is starvation-free. Partitioning the accounts into fixed masking sets lets exhausted sets be garbage-collected without increasing that number. In CUAT, a transfer consumes and replaces every account of its masking set, so two transfers whose sets intersect cannot both take effect. We formalize this with the conflict graph on masking sets, whose edges join sets sharing an account. Under weak untraceability, which protects a transaction in isolation, the consensus number is unbounded already for one-round protocols. Under strong untraceability, which protects against an observer of the complete history, untraceability holds on a history exactly when any two accounts sharing a masking set occur in the same number of the masking sets in it. This uniform incidence bounds the conflict graph, and matching constructions attain it, so the consensus number is determined exactly and grows quadratically in the masking-set size. Finally, CUAT is not starvation-free. The two objects therefore pay for the same privacy differently: LUAT in storage, CUAT in synchronization and fairness.
Abstract We study comovement among major cryptocurrencies from a portfolio management perspective. To this end, we develop two new statistical tools. First, we propose a new measure called the portfolio-conditional correlation defined as the correlation conditional on the portfolio return being below or above a given threshold. Second, we develop a new multivariate model named the Common Autoregressive Jump Intensity Score-based (ComARJIS) model in which the time-varying intensity of a common jump in cryptocurrency returns is formulated under the Generalized Autoregressive Score (GAS) framework. Our main findings are as follows: First, we find an adverse downside correlation: the downside correlation is higher than the upside correlation. Second, the ComARJIS model successfully shows the correlation asymmetry of cryptocurrencies. Third, and most importantly, a market-timing strategy with the common jump intensity improves the Sharpe ratio. This result suggests that time diversification could be helpful for cryptocurrency investors even if asset diversification is impossible. Fourth, the meltdown risk represented by the common jump intensity is associated with the financial market stress in the U.S.
One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transacted through the world financial system each and every year. The problem with this approach is that the criminals seldom use only one bank. They thread their way across five, ten, and sometimes dozens of institutions, all seeing merely a harmless nugget. In isolation, looking at his or her own transaction logs, no single bank will easily know that there is a problem. This paper is about a system, called AMLNet, which tackles this blind spot. Unlike the traditional approach, which would allow banks to share their customers' data with each other,AMLNet trains a detection model on customers' data within each bank, and shares only what the detection model learned from the data, not the data itself. All collaborative training is documented in a blockchain ledger, making it transparent and tamper-proof. With a Zero-Knowledge Proof, each bank is able to prove cryptographically that it is acting honestly, but not disclose anything private. A graph of transaction data (accounts as nodes, transfers as edges) is used to extract structural features, which are compressed by PCA before being input to a Multi-Layer Perceptron (MLP) risk-scoring classifier of each account. Together they increase fraud recall by approximately 20% over any single institution operating alone, while maintaining a low false positive rate, and that the overall computation time is less than 10 minutes on an average laptop.
We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs. Numerical results come from scripted fixed-seed model runs and deterministic simulators; human-supervised AI agents supported the July 20 evidence-integrity revision through literature retrieval, separately tasked critique, artifact reconciliation, documentation, and source packaging, not trading decisions. The strongest later-period evidence, conditional on extensive predecessor search, is negative: an unchanged ten-pair mandatory-daily selector lost 6.72\% over 19 July cycles at an assumed 31-bps completed-cycle cost, with 3 wins and 16 losses. In short model-specific July evaluations, the validation-selected local-minimum policy returned -1.79\%, while the local-maximum sell-to-cash/re-entry policy underperformed continuous holding by 2.80\%; their gross mean advantages of 11.11 and 12.21 bps were below even the 21-bps stress. A Gurgul-inspired, OHLCV-only daily adaptation attained minimum/maximum ROC AUC of 0.874/0.896 but average precision of only 0.134/0.116 and lost 44.30\% over seven cycles, versus -41.20\% for buy-and-hold. A forensic audit also downgraded an earlier One4All "30-day holdout": its dates had influenced prior architecture work, its four-hour outcome horizon was not purged at split boundaries, it used same-close entry, and its raw result directories were absent. Across the tested, mostly exploratory protocols, event-ranking performance did not establish positive executable policy value. Every operational decision remains NO\_TRADE.
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.
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