Hamid Najafi Bouyaghchi, Ameneh Farahani, Ismail A Mageed
The cryptocurrency market is volatile, which makes it very difficult to accurately predict. The Long Short-Term Memory (LSTM) is an approach to Predict Price Cryptocurrency (PPC) that uses price time series data. However, in this method, the prediction accuracy is dependent on the tuning of meta-parameters. Therefore, to tune these meta-parameters, an improved version of the optimization algorithms is needed that provides the task of selecting the optimal values of these parameters for price predictions. Therefore, in this study, the LSTM is combined with the classic version of the Differential Evolution (DE) algorithm, and the real data against the prediction results of the model presented in this study showed the appropriate accuracy of this model. Then, the classic version of the DE algorithm was modified to reduce its errors compared to previous algorithms. In this regard, coding was done in MATLAB version 2023b software, and the improved version was compared in terms of error rate with the Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the new Bald Eagle Search (BES) algorithm, which showed an accuracy of 86.94% for the improved model in this study.
SARMF (Smart Contract Automated Remediation and Mitigation Framework) is a structured and reproducible security engineering pipeline designed for vulnerability detection, taxonomy alignment, automated remediation, and adversarial validation of Ethereum-compatible smart contracts. This operational protocol presents a structured workflow for smart contract vulnerability detection and automated mitigation within blockchain-based systems. The methodology integrates deterministic environment setup, multi-tool static analysis, vulnerability normalization using standardized taxonomies, rule-based automated patch generation, and dynamic adversarial validation. By combining static detection tools with controlled refactoring patterns and behavioral verification, the framework ensures reproducibility, traceability, and measurable performance impact assessment. The protocol concludes with comprehensive audit reporting and archival procedures to support transparency and independent verification. This workflow provides a systematic foundation for secure smart contract lifecycle management in decentralized applications. Unlike traditional audit checklists, this framework operationalizes vulnerability detection, taxonomy alignment, automated remediation generation, and validation feedback loops into a unified reproducible security engineering pipeline. Key Contributions of SARMF: Deterministic environment and compilation reproducibility model. Unified multi-tool vulnerability normalization aligned with SWC taxonomy. Rule-based automated mitigation generation preserving semantic integrity. Iterative validation loop combining static, adversarial, and fuzz testing. Structured audit archival enabling independent verification and traceability.
The sudden growth of cryptocurrencies has created a set of intricate regulatory and legal issues for the financial and governance system of India. The decentralized nature of digital currencies like Bitcoin and Ethereum challenges the conventional monetary system, giving rise to concerns about their legal status, protection of investors, taxation, and overall financial stability. This paper critically analyzes the regulatory environment in India, especially in the wake of the 2018 circular issued by the Reserve Bank of India and its subsequent strike-down in the case of Internet and Mobile Association of India v. Reserve Bank of India. It also discusses challenges with respect to money laundering under the Prevention of Money Laundering Act, 2002, taxation of virtual digital assets, and the lack of a comprehensive statutory regulatory framework for cryptocurrency exchanges. The paper contends that the current stance of India is one of regulatory ambivalence, vacillating between control and tolerance.
A.S. Kannan, E. Baraneetharan, R.Venkatasubramanian, S. Sasi · 6 authors
India's ambitious renewable energy targets of 500 GW by 2030 and net-zero emissions by 2070 necessitate transformative energy trading solutions capable of harnessing distributed renewable sources. This paper introduces a blockchain-enabled peer-to-peer (P2P) energy trading platform designed for India's diverse energy landscape, which includes rooftop solar, wind plants, and microgrids in both urban and rural areas. Built on the Ethereum foundation, the platform employs smart contracts to automate energy transactions between prosumers, reducing dependence on the conventional grid and advancing India's energy security goals. The system integrates machine learning algorithms trained on specific Indian usage patterns and weather conditions to forecast optimal trading times, accounting for seasonal changes, festivals, and industrial demand cycles. Key model assumptions include: (i) prosumers have bidirectional smart meters with IoT connectivity; (ii) weather data availability from Indian Meteorological Department stations; (iii) baseline electricity tariffs following state-level regulatory frameworks; and (iv) participants operate within Karnataka Electricity Regulatory Commission's P2P trading guidelines. Core parameters include LSTM networks with 50 hidden units, learning rate of 0.001, and 24-hour prediction horizons; Random Forest models with 100 estimators and maximum depth of 10; smart contract gas limits of$3,000,000$units; and dynamic pricing coefficients$\alpha=0.15$and$\beta=0.08$calibrated against Tamil Nadu industrial tariffs. Through automated transactions, the platform allows small-scale generators to sell surplus energy directly to local consumers, mitigating the$18-20 \%$distribution losses typical of the Indian grid. Pilot studies in Tamil Nadu and Maharashtra showcased significant results, including a 35-45% cost saving for participating industries and transparent carbon credit accounting, aligning with emerging ESG compliance needs. The platform contributes to the Digital India initiative by fostering a decentralized energy infrastructure that supports both economic development and environmental sustainability.
Ethereum transaction fees exhibit substantial shortterm volatility driven by network congestion, making it difficult for users and applications to determine optimal transaction timing. This work investigates the temporal structure of Ethereum base fees and develops a scalable data-collection and forecasting pipeline for short-horizon, congestion-aware fee estimation. We propose a harvester engine framework based on a parallel blockprocessing mechanism to capture short-term market volatility and develop a parallelized harvester for efficient fee-history collection using the eth_feeHistory JSON-RPC interface. This interface provides the high-resolution, block-level data required for intraday analysis, despite protocol constraints such as the$\mathbf{1 0 2 4}$-block retrieval limit per request. Our fee-history engine incorporates bounded concurrency, latency-aware pacing, and retry stabilization, reducing 30-day data-acquisition time from hours to minutes. We analyze intra-day fee behavior and show that Ethereum base fees exhibit a stable$\mathbf{2 4}$-hour diurnal cycle. We also propose a normalized shape with a rolling-level calibration framework that preserves a stable daily rhythm while continuously adapting to month-level fee changes. Empirical evaluation shows that the adaptive approach improves accuracy and robustness. These findings demonstrate that Ethereum gas fees contain a predictable structure that can be leveraged for practical, short-term forecasting when combined with adaptive calibration.
ABSTRACT The digitization of healthcare insurance claims faces persistent challenges including data breaches, fraudulent submissions, and inefficiencies in verification and settlement. This paper presents a Zero‐Knowledge Succinct Non‐Interactive Argument of Knowledge (Zk‐SNARK) enabled blockchain framework deployed on the Polygon Proof of Stake (PoS) network for secure and privacy‐preserving health insurance processing. The proposed architecture integrates Attribute‐Based Encryption (ABE) for data confidentiality and the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, ensuring end‐to‐end data integrity and access control. Experimental evaluation on the Polygon PoS testbed demonstrates a transaction cost of approximately $0.002, which is over 99% lower than Ethereum's 3–10 $ per transaction, while maintaining 100% resistance to data tampering, replay attacks, and transaction manipulation. Under the Polygon real network, the proposed framework supports a network‐level transaction capacity of up to 7000 transactions per second (TPS) under nominal operating conditions, with an approximately 9.3% reduction in effective capacity under stress scenarios, while maintaining 100% verification accuracy for all Zk‐SNARK proofs. The average on‐chain verification and settlement latency was measured at 4.7 s, confirming the system's suitability for real‐time healthcare claim settlement. These results validate that the proposed Zk‐SNARK enabled Polygon PoS framework offers a scalable, cost‐efficient, and cryptographically robust solution for healthcare insurance automation, outperforming existing blockchain implementations across security, efficiency, and economic performance metrics.
Everybody is these days plunging into decentralized applications, blockchain, and digital identity. And honestly, it's a rendering that the ancient method of authenticating yourself looks nice, outdated and risky. Whenever you give up your personal info, you are just wishing it does not find its way into a data leak or get misused. Precisely, this is the reason that we constructed a new path to manage identity checks that really care about your privacy. This is what we are doing: our framework is based on Ethereum Attestation Service (EAS) and Zero-Knowledge Proofs (ZKPs). To begin with, we generate offchain attestations based on the EIP-712 standard. Your signature remains verifiable; however, your confidential information doesn't go anywhere and through which we squeeze these attestations. SP1 using zero-knowledge virtual machine (zkVM) this step checks everything twice, the construction, the encryption, the logic, all of it. When it's all good then the system will spit a short, non-interactive Groth16 or Plonk (if)SNARK proofs known as zero-knowledge proofs (you're curious). This evidence makes things private and at the same time accurate. There is the interesting side of it: you can check it immediately in your local devices using a super lightweight browser or with a Node.js app WebAssembly verifier. It does not require any middlemen and there is no need for extra trust. You can send evidences, in case you wish to using Solidity contract on-chain. This allows the system to issue new fraudulent statements such as isOver18 = true without displaying your actual age or any other personal data. So what does this mean? You earn greater confidence, enhanced interoperability and connections through decentralize systems. Transparency is what you have with this of ZKP attestation and actual privacy. It's actually practical, as well, can be used with KYC, DeFi, age-requiring app checks, and secured access controls. Essentially, it is a huge leap higher on behalf of anonymity and trust in electronic self.
This research demonstrates the environmental impacts of Digital Currencies (DC), particularly focusing on Bitcoin's (BTC) energy-intensive Proof-of-Work (PoW) process as well as a fundamental expectation for sustainable alternatives, which can be termed as Green Coins (GC) which are expected to be able to maintain the benefits of BTC, while generating little to no negative impacts on the environment. Bitcoin is estimated to consume about 150 TWh annually, a measure comparable to that of a mid-sized country, while also generating 60 to 90 million metric tons of$\text{CO}_{2}$emissions and about 30,000 metric tons of electronic waste (e-waste) through deliberate accelerated hardware obsolescence. On the other hand, GC tends to use more efficient proofs such as Proof of Stake (PoS) and Proof of Space-time (PoST) and examples include Ethereum following its 'Merge' estimated a reduction of over 99 % of energy use and Dogecoin has and even lower environmental impact compared to BTC. Using data sets from the Cambridge Bitcoin Electricity Consumption Index (CBECI) and Digiconomist, this study quantifies Bitcoin's carbon footprint and tracks the trends from 2017 to 2025, through more extensively investigating its sustainability profile relative to its GC counterparts. Findings reveal Bitcoin continues to have high energy use and e-waste, peaking in 2021, while both Ethereum (ETH) and Dogecoin (DG) had significant gains in sustainability improvements. Addressing scaling, security, and regulatory issues, the paper highlights the potential of sustainable financing within the digital financial markets to drive Green technologies, which is increasingly important for aligning cryptocurrency financing with Environmental, Social, and Governance (ESG) parameters, providing a way to continue to innovate while decarbonizing digital financing.
In democratic systems, secure and transparent voting mechanisms are essential to maintain public trust and electoral integrity. Traditional paper-based and centralized electronic voting systems often face challenges such as limited transparency, risk of data manipulation, and dependence on centralized authorities. To address these issues, this project proposes a decentralized blockchain-based voting system designed to enhance security, transparency, and reliability. The system is developed on the Ethereum blockchain, where each vote is recorded as an immutable transaction to prevent tampering or duplication. Smart contracts written in Solidity automate essential election functions including voter registration, vote validation, and result computation. A web-based interface built using React.js and Web3.js enables secure interaction with the blockchain, while wallet-based authentication ensures that each authorized user can cast only one vote The system is implemented and tested in a controlled environment to evaluate performance, accuracy, and resistance to double voting.
Global illicit fund flows exceed an estimated $3.1 trillion annually, with stablecoins emerging as a preferred laundering medium due to their liquidity. While decentralized protocols increasingly adopt zero-knowledge proofs to obfuscate transaction graphs, centralized stablecoins remain critical transparent choke points for compliance. Leveraging this persistent visibility, this study analyzes an Ethereum dataset to establish an empirical baseline for behavioral AML detection. Our findings demonstrate that domain-informed tree ensemble models achieve higher Macro-F1 score, significantly outperforming graph neural networks, which struggle with the increasing fragmentation of transaction networks. The model's interpretability goes beyond binary detection, successfully dissecting distinct typologies: it differentiates the complex, high-velocity dispersion of cybercrime syndicates from the constrained, static footprints left by sanctioned entities. This methodological approach provides actionable insights that align with industry shifts toward deterministic verification, informing the auditability and compliance requirements under regulations such as the EU's MiCA and the U.S. GENIUS Act while minimizing unjustified asset freezes. By providing a high-precision behavioral classification of suspicious wallets, this approach contributes to raising the economic cost of financial misconduct while informing compliance practice under emerging stablecoin regulations.
Smart contracts, primarily written in Solidity, are Turing-complete programs on platforms like Ethereum, requiring gas fees for deployment and execution. Gas quantifies computational costs, and inefficient contracts result in unnecessary expenses for developers and users. Gas optimization at the source code level has been studied in various related works; however, existing methods for summarizing gas-inefficient patterns primarily rely on author-defined rules or heuristic approaches, and their evaluations lack a labeled dataset.In this paper, we conduct a comprehensive empirical study on the issue of gas optimization in smart contracts. We begin by gathering audit reports from Code4rena, a well-known smart contract audit platform. These reports include both expert evaluations, conducted by professionals known as Wardens, and automated analyses generated by the platform’s static analysis tool, 4naly3er. After filtering out false-positive gas optimization instances from the automated reports, we identify 2,095 instances of gas-inefficient patterns across 54 projects. We categorize these inefficiencies into 24 types using thematic analysis and find that static analysis tools often produce false positives and negatives. To address this, we propose a hybrid method combining static analysis and large language models (LLMs) to detect and repair gas inefficiencies. The static analysis tool identifies potential optimization opportunities, while the LLM refines these findings and suggests effective repairs. Our evaluation shows that our approach achieves a precision rate of 82.28% and a recall rate of 88.46%, and can save 919 units of gas per function on average during execution.
Najaf Iqbal, Muhammad Abubakr Naeem, Hang Luo, Walid Bakry
Using 5-minute data of 16 cryptocurrency tokens belonging to 5 different categories (AI, Gaming, Meme, Layer 1/2, and FAN tokens), we investigate the risk transmission in higher moments, i.e. realized volatility (RV), realized skewness (RS), and realized kurtosis (RK), employing the TVP-VAR framework and robustness tests. We also perform six sub-sample investigations on various geopolitical and other systemic events. Ethereum, Binance Coin, and Ripple are strongly related to other tokens. Sandbox, Decentraland, and Enjin Coin lead spillover transmission, while Numeraire, Measurable Data Token, and Cryptex Finance absorb most of the shocks. The connections are stronger regarding RV than RS and RK, showing potential for tail-risk reduction, which is heterogeneous regarding extreme events. AI tokens are the least connected during normal conditions as well as most of the extreme events, except the US presidential Election, which puts these tokens in the centre of the system. The Israel-Palestine war, the FTX collapse, and the SEC approval of the first Bitcoin ETF are among the most important events regarding enhancement in the higher-moment risk transmission. Token market investors/traders and regulators can draw essential insights from our findings.
Zibin Lin, Taotao Wang, Shengli Zhang, Long Shi · 6 authors
Open Web 3.0 platforms increasingly operate as \emph{service ecosystems} (e.g., DeFi, DAOs, and decentralized social applications) where \emph{admission control} and \emph{account provisioning} must be delivered as an always-on service under bursty demand. Service operators face a fundamental tension: enforcing Sybil resistance (one-person-one-account) while preserving user privacy, yet keeping on-chain verification cost and admission latency predictable at scale. Existing credential-based ZK admission approaches typically require per-request on-chain verification, making the provisioning cost grow with the number of concurrent joiners. We present \textbf{ZK-AMS}, a scalable admission and provisioning layer that bridges real-world \emph{Personhood Credentials} to anonymous on-chain service accounts. ZK-AMS combines (i) zero-knowledge credential validation, (ii) a \emph{permissionless} batch submitter model, and (iii) a decentralized, privacy-preserving folding pipeline that uses Nova-style recursive aggregation together with multi-key homomorphic encryption, enabling batch settlement with \emph{constant} on-chain verification per batch. We implement ZK-AMS end-to-end on an Ethereum testbed and evaluate admission throughput, end-to-end latency, and gas consumption. Results show stable verification cost across batch sizes and substantially improved admission efficiency over non-recursive baselines, providing a practical and cost-predictable admission service for large-scale Web 3.0 communities.
Fraud detection within the Ethereum network remains a major research challenge due to the strong statistical resemblance between legitimate and fraudulent transaction patterns, severe class imbalance, and the multiscale complexity of temporal-interaction dependencies. Proposing and evaluating a multi-branch attention-based system with automated architecture optimization, which can detect fraudulent Ethereum accounts with high accuracy, is the aim of this study. The experimental evaluation was performed on a dataset with 9,841 samples and 17 extracted features. The proposed system employed a hybrid multi-branch architecture combining CNN, Bi-LSTM, and LSTM with a Gated Fusion mechanism along with multiscale attention layers. The Artificial Bee Colony (ABC) algorithm was applied to automatically optimize sixteen key structural and learning parameters. The results indicate that the proposed system achieved an accuracy of 99.84 %, F1 score of 98.94 %, sensitivity of 98.76 percent, and precision of 99.12 percent. These results notably outperform eight algorithms, such as Random Forest, XGBoost, LGBM, and GADL. According to the confusion matrix analysis, there is a reduction in false negatives, confirming that the system produced only five such cases in the sample set. These findings show that the proposed system is an effective and efficient approach for detecting fraud in blockchain systems and enables deployment in exchanges, DeFi platforms, and regulatory institutions.
Zibin Lin, Taotao Wang, Shengli Zhang, Long Shi · 6 authors
Web 3.0 platforms need an onboarding mechanism that can admit real users at scale without forcing them to reveal identity documents or pay one on-chain verification cost per user. Existing approaches typically rely on KYC-style disclosure, per-request on-chain verification, or trusted batching, making onboarding cost and latency difficult to predict under bursty demand. We present \textbf{ZK-AMS}, a credibly anonymous admission infrastructure that maps Personhood Credentials to anonymous on-chain Soul Accounts. Rather than introducing a new primitive, ZK-AMS composes zero-knowledge credential validation, permissionless batch submission, recursive proof aggregation, and anonymous post-admission account provisioning into one end-to-end workflow. Its key design feature is a confidential batching pipeline in which admission instances of a common relation are folded off-chain under multi-key homomorphic encryption, allowing an untrusted batch submitter to coordinate aggregation without direct access to individual user witnesses during batching; the confidentiality scope is characterized explicitly in the security analysis. The resulting batch is settled on-chain with constant verification cost per batch rather than per admitted user. We implement ZK-AMS on an Ethereum testbed and evaluate admission throughput, end-to-end latency, gas consumption, and parameter trade-offs. Results show stable batch-verification gas across evaluated batch sizes, substantially lower amortized on-chain cost than the non-recursive baseline, and practical cost-latency trade-offs for high-concurrency onboarding in Web 3.0 platforms.
Zero-knowledge proof generation imposes stringent timing and reliability constraints on blockchain systems. For ZK-rollups, delayed proofs cause finality lag and economic loss; for Ethereum's emerging L1 zkEVM, proofs must complete within the 12-second slot window to enable stateless validation. The Ethereum Foundation's Ethproofs initiative coordinates multiple independent zkVMs across proving clusters to achieve real-time block proving, yet no principled orchestration framework addresses the joint challenges of (i) strict head-of-chain ordering, (ii) sub-slot latency bounds, (iii) fault-tolerant task reassignment, and (iv) prover-agnostic workflow composition. We present push0, a cloud-native proof orchestration system that decouples prover binaries from scheduling infrastructure. push0 employs an event-driven dispatcher--collector architecture over persistent priority queues, enforcing block-sequential proving while exploiting intra-block parallelism. We formalize requirements drawn from production ZK-rollup operations and the Ethereum real-time proving specification, then demonstrate via production Kubernetes cluster experiments that push0 achieves 5 ms median orchestration overhead with 99--100% scaling efficiency at 32 dispatchers for realistic workloads--overhead negligible (less than 0.1%) relative to typical proof computation times of 7+ seconds. Controlled Docker experiments validate these results, showing comparable performance (3--10 ms P50) when network variance is eliminated. Production deployment on the Zircuit zkrollup (14+ million mainnet blocks since March 2025) provides ecological validity for these controlled experiments. Our design enables seamless integration of heterogeneous zkVMs, supports automatic task recovery via message persistence, and provides the scheduling primitives necessary for both centralized rollup operators and decentralized multi-prover networks.
We study builder-driven MEV arbitrage on BNB Smart Chain (BSC). BSC's Proposer-Builder Separation (PBS) adopts a leaner design: only whitelisted builders can participate, blocks are produced at shorter intervals, and private order flow bypasses the public mempool. These features have long raised community concerns over centralization, which we empirically confirm by tracing the arbitrage activities of the two dominant builders from Apr. 1, 2025 to Feb. 28, 2026 (full observable activity cycle). Within months, the two leading builders, \bd{48Club} and \bd{Blockrazor}, produced over 87\% of blocks and captured about 90\%+ of MEV profits. We find that profits concentrate in short, low-hop arbitrage routes over wrapped tokens and stablecoins, and that block construction rapidly converges toward monopoly. Beyond concentration alone, our analysis reveals a structural source of inequality: BSC's short block interval and whitelisted PBS collapse the contestable window for MEV competition, amplifying latency advantages and excluding slower builders and searchers. MEV extraction on BSC is not only more centralized than on Ethereum, but also structurally more vulnerable to censorship and fairness erosion.
Truffle is a framework that provides compiling, testing and systematic project management for developing Ethereum decentralized applications. As of now, Truffle provides a way to easily deal with bundling node.js modules of decentralized application using the webpack tool. However, due to the Truffle project structure, server-side node.js modules such as network communication modules are not usable in a direct way. In this paper, to address this issue, we propose a method to use server-side node.js modules through Ethereum smart contracts and event processing mechanism. In the proposed method, a separate node application is associated to the server-side module to execute the module in response to the request of the decentralized application. To this end, we introduce the notion of function gateway, a smart contract for connecting two applications with Ethereum's event-watch processing technique. Also, to use the function gateway contract in a robust way, we introduce a robust function gateway that includes the process of confirming whether or not the event-watch has occurred and the node.js module function has been executed. In addition, we present a decentralized application using node.js module for sending actual e-mails based on the function gateway.
Blockchain technology has revolutionized real-time applications with its decentralized, secure, and immutable framework, wherein the consensus mechanisms play a principal role in deciding transaction speed, security, and scalability. Traditional consensus mechanisms like Proof of Work (PoW) were affected by latency and energy inefficiency, while modern alternatives such as Proof of Stake (PoS), Practical Byzantine FaultTolerance (PBFT), and Delegated Proof-of-Stake (DPoS) realize faster and scalable solutions to real-time applications for Finance, Supply Chain, Healthcare, and IoT. This survey conducts a systematic analysis of the various consensus algorithms, including PoW, PoS, PBFT, and some upcoming models like Proof of History (PoH), in regard to throughput, latency, and security and finds that PoS-based systems and DAG (Directed Acyclic Graph) systems such as Solana and Ethereum 2.0 excel over PoW for low-latency applications with thousands of transactions per second (TPS). Despite these improvements, present-day blockchain technologies are encumbered with challenges like scalability bottlenecks, interoperability challenges, and regulatory restrictions, which prompt the search for future solutions such as hybrid consensus methods (PoS + sharding), Layer-2 scaling approaches (including rollups and sidechains), and AI-based optimizations that could benefit real-time operations of blockchains without compromising security and decentralization.
The dissertation examines statistical arbitrage methods in the cryptocurrency markets using cointegration analysis on Bitcoin, ethereum, Litecoin, Ripple using daily price data of the cryptocurrencies between January 2022 and October 2024. The research deploys strict econometric procedures, such as the Engle-Granger two-step process and Johansen test, to uncover and take advantage of the mean-reverting relationships between the key cryptocurrencies. Findings indicate that there are strong relationships of cointegration especially between Bitcoin-Ether and Ethereum-Litecoin with the relationship between Bitcoin-Ether and Ethereum being very stable in many market regimes. The statistically arbitrage strategies depending on such cointegrated pairs led to large risk-adjusted returns whose Sharpe ratios of 1.58 to 2.45 were markedly higher than buy-and-hold standards. The Bitcoin-Etherer pairs trading strategy had an annualized return of 16.34 evidenced by a volatility of just 8.45 against the volatility of Bitcoin on buy and hold at 54.67. These strategies had low beta (0.09-0.18), which was an affirmative of their market-neutral qualities and their positive alpha generation of between 11-15% per annum.
This study investigates the volatility behaviour of Ethereum (Coinbase) returns using the Generalized Autoregressive Heteroskedasticity GARCH (1,1) model under three distributional assumptions: Normal, Student-t, and the Generalized Error Distribution (GED). Cryptocurrency markets are characterized by extreme price swings, heavy-tailed behaviour, and persistent volatility, making traditional constant-variance models ineffective. Descriptive statistics reveal strong deviations from normality in Ethereum returns, with high kurtosis (7.8454) and an extremely large Jarque–Bera statistic (1797.182 with its p-value less than 5%), indicating excess tail risk and frequent extreme movements. Preliminary analysis reveal that the return series is stationary, free from serial correlation, but exhibits significant ARCH effects, justifying the use of conditional heteroskedasticity models. Empirical results show highly persistent volatility across all models, with α + β values close to unity: approximately 0.99 under the Normal distribution, 1.01 under the Student-t specification, and 0.994 under GED distribution. Model comparison reveals that heavy-tailed error structures outperform the Normal model, with GED achieving the lowest AIC (−3.781), SIC (−3.7629), HQC (−3.7743), and the lowest MAPE (114.6606). These findings demonstrate that flexible distributional assumptions greatly enhance the modelling of extreme and persistent volatility in Ethereum returns. The study emphasizes the importance of adopting heavy-tailed GARCH frameworks when analysing cryptocurrency risk and forecasting volatility.