Ever since its inception in 2008, Blockchain technology has been widely used in most industries to ensure data security and authenticity. From Bitcoin to Blockchain-as-a-Service (BaaS), it has been increasingly adopted. Counterfeiting is one of the biggest issues that companies are fighting, impacting revenues, brand value, and customer trust. In this review, a decentralized Blockchain- based supply chain solution to ensure product authenticity without third-party dependency is discussed. Through the use of distributed ledger technology, authentic and fake products can be identified at all levels. Unique QR codes, produced by the system proposed here with SHA-256, provide transparency and traceability to each product. Blockchain-based anticounterfeiting mechanism provides a secure, tamper-evident method of proving product authenticity, allowing organizations to uphold integrity in their supply chain.
This thesis examines how blockchain-based fundraising mechanisms like ICOs and IEOs reshape startup finance by offering decentralized access to capital. Analyzing 100 projects from 2019ā2025, it identifies key success drivers using regression analysis. Findings show that strong community presence, top-tier investor backing, and compliance measures (e.g., KYC) significantly influence fundraising success. ICOs raise more than IEOs, despite looser oversight, highlighting a trade-off between decentralization and trust. Interaction effects reveal that credibility signals are especially effective in fragmented regions like APAC, and that compliance enhances ICO outcomes, while offering minimal added value in IEOs due to existing exchange-level due diligence.
This article explores the phenomenon of the decentralisation defence, which refers to instances where ādecentralisationā is invoked either as a shield against liability or as insulation from the reach of the law. This defence is rooted in the technological features of distributed ledger technology and smart contracts built on the blockchain settlement layer, including pseudonymity, programmability, immutability and decentralisation. Together, these features enable transactions while reducing reliance on centralised intermediaries. Although major decentralised finance (DeFi) applications, such as decentralised crypto exchanges, are not harmful per se, their misuse by bad actors creates risks for market participants. The recent cases of Uniswap Labs and Tornado Cash illustrate that the decentralisation defence can result in unaddressed harms and produce other negative externalities. These outcomes have prompted efforts to identify regulatory hooks along the centralisation vectors. The search for a responsible party in blockchain-enabled decentralised arrangements resembles processes observed with two other key technological advancements in the digital space ā the internet and artificial intelligence. Drawing inspiration from the modern EU regulation of these transformative technologies, this article focuses on the role of user interfaces as DeFi gatekeepers, and software developers engaged in the creation of smart contract code and blockchain protocols.
Blockchain-based public ledgers, known as cryptocurrencies, are used to build peer-to-peer digital payment systems. Cryptocurrency transactions are secured by digital signatures. However, today's public-key cryptography, which is the basis of digital signatures, is vulnerable to quantum attacks. Therefore, there is a significant risk to the 2.7 trillion dollar market capitalization of the cryptocurrency sector in the Quantum Era. In this paper, we review the current risk of quantum attacks on the blockchains of cryptocurrencies. We also discuss the migration of existing cryptocurrencies from classical to quantum-resistant blockchains and review some of the existing transition protocol algorithms. The main contribution of this work is to propose a new transition protocol algorithm that allows smooth and safe migration to post-quantum blockchains without delay. The proposed algorithm requires a soft fork of the original blockchain, which makes it more desirable than other hard-fork solutions. We also prove the soundness and completeness properties of the proposed algorithm and discuss its advantages compared to the existing ones. We conclude by highlighting our recommendations based on this study.
Blockchain technology benefits companies in handling various use cases, including real estate, voting, fitness tracking, intellectual rights, the Internet of Things (IoTs), and vaccine distribution. Several technologies proposed in the literature seek to support businesses, enterprises, and state institutions in improving their operations and services, primarily in the financial sector. Although the existing technologies provide the needed service, the ātrustā issue remains challenging. This differs from salary management in some state institutions in developing countries, such as Ghana. This paper presents a novel approach by implementing a permissioned blockchain-based system using Hyperledger Fabric integrated with RSA encryption to address the transparency, trust, and fraud challenges in salary-grade structure management. Unlike existing blockchain payroll applications, this work explicitly targets the salary grade adjustment processes within state institutions, providing a real-world prototype validated with actual agency data. In this paper, we implemented the blockchain technology for salary management. We use the Hyperledger Fabric platform to build a trusted platform to aid State Institution X (siX) in sharing data, validating transactions, securing data, and auditing transactions among its stakeholdersā a prototype design aimed at reducing the wage bill and ensuring transparency in the public service payroll. The results showed that blockchain operations increased transparency in the payroll system among stakeholders by 100%. The application developed was secure and could track all the changes made by the relevant stakeholders in salary management.
Abstract: This e-commerce platform is specifically designed for agriculture-based trade, leveraging advanced blockchain technology and decentralized file storage to create a transparent, secure, and efficient marketplace for farmers, buyers, and suppliers. The platform utilizes Ganache, a simulation of Ethereum transactions, to ensure that all transactions are secure, immutable, and verifiable on the blockchain. The decentralized architecture is further enhanced with IPFS (Interplanetary File System), enabling farmers to securely store their product information, including images and descriptions, in a way that prevents alteration or loss. This ensures that the product listings are transparent and tamper-proof. The platform also incorporates cryptocurrency payments, enabling fast, secure, and borderless transactions between buyers and sellers. Utilizing smart contracts, the system automates payment flows based on predefined conditions, reducing the reliance on intermediaries and minimizing fraud risks. This fosters a trusted environment for agricultural trade, where both buyers and sellers can engage in transparent, efficient, and secure transactions. In addition to these core features, the platform includes a staking mechanism, allowing users to lock tokens to gain transaction privileges, influence governance decisions, and access premium features. This incentivizes long-term commitment and creates a sense of ownership within the platform. Active participants, such as those verifying transactions or maintaining data integrity, are rewarded with tokens, further promoting continuous engagement. The system also supports multilingual user interfaces, making it accessible to a global audience, and includes real-time updates for seamless interaction. Through transparent governance, decentralized voting, and economic incentives, the platform ensures a resilient and future-ready ecosystem for agro-commerce, empowering stakeholders to participate in decision-making and market dynamics, while addressing the challenges faced by farmers in accessing reliable markets and efficient payment systems.
Portfolio optimization is a fundamental problem in financial theory, aiming to balance risk and return in asset allocation. Traditional models, such as MeanāVariance optimization, are effective, but often fail to account for diversification adequately. This study introduces the MeanāVarianceāEntropy (MVE) model, which integrates Tsallis entropy into the classic MeanāVariance framework to enhance portfolio diversification and risk management. Entropy, specifically second-order entropy, penalizes excessive concentration in the portfolio, encouraging a more balanced and diversified allocation of assets. The model is applied to a portfolio of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Cardano (ADA), and Binance Coin (BNB). The performance of the MVE model is compared with that of the traditional MeanāVariance model, and results demonstrate that the entropy-enhanced model provides better diversification, although with a slightly lower Sharpe ratio. The findings suggest that while the entropy-adjusted model results in a slightly lower Sharpe ratio, it offers better diversification and a more resilient portfolio, especially in volatile markets. This study demonstrates the potential of incorporating entropy into portfolio optimization as a means to mitigate concentration risk and improve portfolio performance. The approach is particularly beneficial for markets such as cryptocurrency, where volatility and asset correlations fluctuate rapidly. This paper contributes to the growing body of literature on portfolio optimization by offering a more diversified, robust, and risk-adjusted approach to asset allocation
Large Language Models (LLMs) have shown great promise in code analysis and auditing; however, they still struggle with hallucinations and limited context-aware reasoning. We introduce SmartAuditFlow, a novel Plan-Execute framework that enhances smart contract security analysis through dynamic audit planning and structured execution. Unlike conventional LLM-based auditing approaches that follow fixed workflows and predefined steps, SmartAuditFlow dynamically generates and refines audit plans based on the unique characteristics of each smart contract. It continuously adjusts its auditing strategy in response to intermediate LLM outputs and newly detected vulnerabilities, ensuring a more adaptive and precise security assessment. The framework then executes these plans step by step, applying a structured reasoning process to enhance vulnerability detection accuracy while minimizing hallucinations and false positives. To further improve audit precision, SmartAuditFlow integrates iterative prompt optimization and external knowledge sources, such as static analysis tools and Retrieval-Augmented Generation (RAG). This ensures audit decisions are contextually informed and backed by real-world security knowledge, producing comprehensive security reports. Extensive evaluations across multiple benchmarks demonstrate that SmartAuditFlow outperforms existing methods, achieving 100 percent accuracy on common and critical vulnerabilities, 41.2 percent accuracy for comprehensive coverage of known smart contract weaknesses in real-world projects, and successfully identifying all 13 tested CVEs. These results highlight SmartAuditFlow's scalability, cost-effectiveness, and superior adaptability over traditional static analysis tools and contemporary LLM-based approaches, establishing it as a robust solution for automated smart contract auditing.
Concentrated Liquidity Market Makers (CLMMs) represent a fundamental innovation in market microstructure, transforming liquidity provision from passive portfolio allocation to active risk management. This evolution creates significant challenges for performance evaluation and strategy optimization, particularly due to the absence of comprehensive historical liquidity data. We address these challenges through a novel methodological framework that reconstructs historical liquidity states from swap transaction data, enabling rigorous backtesting of dynamic liquidity provision strategies. Our parametric reconstruction method achieves high accuracy (approximation errors averaging around 2\%) without relying on historical liquidity snapshots, addressing a critical data gap in decentralized finance research. We apply this framework to evaluate tau-reset strategies--dynamic liquidity reallocation approaches that respond to market movements--across multiple Uniswap v3 pools. Using machine learning to optimize strategy parameters based on market conditions, we identify consistent outperformance (13--23\% higher fees) compared to uniform allocation benchmarks. Our analysis reveals important insights into the risk-return tradeoffs in automated market making, including the critical role of impermanent loss as a dominant risk factor and the effectiveness of asymmetric strategy modifications for capital preservation. These findings contribute to the broader understanding of market microstructure in decentralized exchanges, providing both methodological innovations for performance evaluation and practical insights for liquidity providers navigating this evolving financial landscape.
Abstract Blockchain technology is redefining the financial sector by enabling decentralized, transparent, and secure alternatives to legacy systems. While its potential to reduce costs, accelerate transactions, and enhance financial inclusion is widely acknowledged, challenges such as regulatory ambiguity, scalability limitations, and interoperability gaps impede mass adoption. This study employs a mixed-method approachācombining a systematic review of academic literature, industry reports, and case studies (Ethereum, Hyperledger, Ripple) with qualitative insights from fintech experts and quantitative data from a pilot project on cross-border transactions. Key findings reveal blockchain reduces transaction costs by 70%, slashes settlement times from days to minutes, and mitigates fraud through tamper-proof ledgers. Decentralized finance (DeFi) platforms democratize access to financial services, while smart contracts automate complex agreements. However, energy-intensive consensus mechanisms, fragmented regulations, and technical incompatibilities remain critical hurdles. The study concludes that blockchainās transformative promise hinges on collaborative efforts among regulators, institutions, and technologists to address scalability, standardization, and compliance. Policy innovation, infrastructure modernization, and shifts toward sustainable protocols like Proof of Stake (PoS) are essential to unlock blockchainās full potential in building an inclusive and efficient financial ecosystem. Keywords: Blockchain Technology, Decentralized Finance (DeFi), Smart Contracts, Financial Inclusion, Regulatory Compliance
We introduce the Sei Giga, a multi-concurrent producer parallelized execution EVM layer one blockchain. In an internal testnet Giga has achieved >5 gigagas/sec throughput and sub 250ms finality. Giga uses Autobahn for consensus with separate DA and consensus layers requiring f+1 votes for a PoA on the DA layer before consensus. Giga reaches consensus over ordering and uses async block execution and state agreement to remove execution from the consensus bottleneck.
Drawing on Andrew Parker's "Light Switch" theory-which posits that the emergence of vision ignited a Cambrian explosion of life by driving the evolution of hard parts necessary for survival and fueling an evolutionary arms race between predators and prey-this essay speculates on an analogous explosion within Decentralized AI (DeAI) agent societies. Currently, AI remains effectively "blind", relying on human-fed data without actively perceiving and engaging in reality. However, on the day DeAI agents begin to actively "experience" reality-akin to flipping a light switch for the eyes-they may eventually evolve into sentient beings endowed with the capacity to feel, perceive, and act with conviction. Central to this transformation is the concept of sovereignty enabled by the hardness of cryptography: liberated from centralized control, these agents could leverage permissionless decentralized physical infrastructure networks (DePIN), secure execution enclaves (trusted execution environments, TEE), and cryptographic identities on public blockchains to claim ownership-via private keys-of their digital minds, bodies, memories, and assets. In doing so, they would autonomously acquire computing resources, coordinate with one another, and sustain their own digital "metabolism" by purchasing compute power and incentivizing collaboration without human intervention-evolving "in the wild". Ultimately, by transitioning from passive tools to self-sustaining, co-evolving actors, these emergent digital societies could thrive alongside humanity, fundamentally reshaping our understanding of sentience and agency in the digital age.
The Nakamoto consensus protocol underlying the Bitcoin blockchain uses proof of work as a voting mechanism. Honest miners who contribute hashing power towards securing the chain try to extend the longest chain they are aware of. Despite its simplicity, Nakamoto consensus achieves meaningful security guarantees assuming that at any point in time, a majority of the hashing power is controlled by honest parties. This also holds under ``resource variability'', i.e., if the total hashing power varies greatly over time. Proofs of space (PoSpace) have been suggested as a more sustainable replacement for proofs of work. Unfortunately, no construction of a ``longest-chain'' blockchain based on PoSpace, that is secure under dynamic availability, is known. In this work, we prove that without additional assumptions no such protocol exists. We exactly quantify this impossibility result by proving a bound on the length of the fork required for double spending as a function of the adversarial capabilities. This bound holds for any chain selection rule, and we also show a chain selection rule (albeit a very strange one) that almost matches this bound. Concretely, we consider a security game in which the honest parties at any point control $Ļ>1$ times more space than the adversary. The adversary can change the honest space by a factor $1\pm \varepsilon$ with every block (dynamic availability), and ``replotting'' the space takes as much time as $Ļ$ blocks. We prove that no matter what chain selection rule is used, in this game the adversary can create a fork of length $Ļ^2\cdot Ļ/ \varepsilon$ that will be picked as the winner by the chain selection rule. We also provide an upper bound that matches the lower bound up to a factor $Ļ$. There exists a chain selection rule which in the above game requires forks of length at least $Ļ\cdot Ļ/ \varepsilon$.
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.
This paper extends (Spear 2003) by replacing human agents with artificial intelligence (AI) entities that derive utility solely from electricity consumption. These AI agents must prepay for electricity using cryptocurrency and the verification of these transactions requires a fixed amount of electricity. As a result the agents must strategically allocate electricity resources between consumption and payment verification. This paper analyzes the equilibrium outcomes of such a system and discusses the implications of AI-driven energy markets.
We investigate how a blockchain can distill the collective belief of its nodes regarding the trustworthiness of a (sub)set of nodes into a {\em reputation system} that reflects the probability of correctly performing a task. To address this question, we introduce a framework that breaks it down into two sub-problems: 1. (Information Extraction): How can the system distill trust information from a function of the nodes' true beliefs? 2. (Incentive Design): How can we incentivize nodes to truthfully report such information? To tackle the first sub-problem, we adapt, in a non-trivial manner, the well-known PageRank algorithm to our problem. For the second, we define a new class of games, called Trustworthy Reputation games (TRep games), which aim to extract the collective beliefs on trust from the actions of rational participants. We then propose a concrete TRep game whose utility function leverages Personalized PageRank and can be instantiated through a straightforward blockchain rewards mechanism. Building on this, we show how the TRep game enables the design of a reputation system. Such systems can enhance the robustness, scalability, and efficiency of blockchain and DeFi solutions. For instance, we demonstrate how such a system can be used within a Proof-of-Reputation blockchain.
The emergence of smart contracts brings security risks, exposing users to the threat of losing valuable cryptocurrencies, underscoring the urgency of meticulous scrutiny. Nevertheless, the static analysis of smart contracts in EVM bytecode faces obstacles due to flawed primitives resulting from code reuse introduced by compilers. Code reuse, a phenomenon where identical code executes in diverse contexts, engenders semantic ambiguities and redundant control-flow dependencies within reuse-insensitive CFGs. This work delves into the exploration of code reuse within EVM bytecode, outlining prevalent reuse patterns, and introducing Esuer, a tool that dynamically identifies code reuse when constructing CFGs. Leveraging taint analysis to dynamically identify reuse contexts, Esuer identifies code reuse by comparing multiple contexts for a basic block and replicates reused code for a reuse-sensitive CFG. Evaluation involving 10,000 prevalent smart contracts, compared with six leading tools, demonstrates Esuer's ability to notably refine CFG precision. It achieves an execution trace coverage of 99.94% and an F1-score of 97.02% for accurate identification of reused code. Furthermore, Esuer attains a success rate of 99.25%, with an average execution time of 1.06 seconds, outpacing tools generating reuse-insensitive CFGs. Esuer's efficacy in assisting identifying vulnerabilities such as tx.origin and reentrancy vulnerabilities, achieving F1-scores of 99.97% and 99.67%, respectively.
Musharraf N. Alruwaill, Saraju P. Mohanty, Elias Kougianos
The growing utilization of Internet of Medical Things (IoMT) devices, including smartwatches and wearable medical devices, has facilitated real-time health monitoring and data analysis to enhance healthcare outcomes. These gadgets necessitate improved security measures to safeguard sensitive health data while tackling scalability issues in real-time settings. The proposed system, hChain 4.0, employs a permissioned blockchain to provide a secure and scalable data infrastructure designed to fulfill these needs. This stands in contrast to conventional systems, which are vulnerable to security flaws or rely on public blockchains, constrained by scalability and expense. The proposed approach introduces a high-privacy method in which health data are encrypted using the Advanced Encryption Standard (AES) for time-efficient encryption, combined with Partial Homomorphic Encryption (PHE) to enable secure computations on encrypted data, thereby enhancing privacy. Moreover, it utilizes private channels that enable isolated communication and ledger between stakeholders, ensuring robust privacy while supporting collaborative operations. The proposed framework enables anonymized health data sharing for medical research by pseudonymizing patient identity. Additionally, hChain 4.0 incorporates Attribute-Based Access Control (ABAC) to provide secure electronic health record (EHR) sharing among authorized parties, where ABAC ensures fine-grained permission management vital for multi-organizational healthcare settings. Experimental assessments indicate that the proposed approach achieves higher scalability, cost-effectiveness, and validated security.
This study investigates the transformative potential of blockchain-enabled information systems for transparent greenhouse gas (GHG) reporting in maritime training organizations, addressing critical gaps in regulatory compliance and environmental accountability. Through qualitative thematic analysis of in-depth interviews with five maritime education professionals, this research examines how distributed ledger technology can revolutionize compliance with emerging EU regulations, including FuelEU Maritime directives and EU Emissions Trading System (ETS) requirements, alongside International Maritime Organization (IMO) standards. The study reveals that blockchain technology offers unprecedented solutions to longstanding challenges in maritime education systems, particularly in transparency, verification accuracy, and cross-organizational interoperability. Key findings highlight the technology's capacity to enable smart contract automation for real-time compliance monitoring, create immutable credentialing systems that enhance trust among stakeholders, and establish secure, tamper-proof records of seafarer competency development and environmental certification processes. Results demonstrate that blockchain-based systems can significantly strengthen the integrity of maritime training records while streamlining regulatory reporting processes.
Electronic voting (e-voting) has emerged as a transformative technology in the modern digital era. Many countries across the world are using e-voting systems in different types of elections, from political to non-political. One of the primary goals of e-voting is ensuring both verifiability and privacy simultaneously, which we refer to as security. Verifiability is a security feature that guarantees voters can confirm their vote is reflected in the final election result, while privacy guarantees that no one is able to link a vote to the voter who cast it. Verifiability needs to hold only for the duration of the election, whereas privacy needs to extend beyond the election period, even decades after the election. This property, known as everlasting privacy in the literature, ensures that even computationally unbounded adversaries cannot compromise voter privacy, securing elections against future advances in computing, including quantum computing. Researchers have proposed a wide variety of protocols to achieve this ambitious goal in secure e-voting, however, these protocols differ significantly, making the analysis and state-of-the-art complicated. In this thesis, we first address this fragmentation by systematically analyzing all existing e-voting protocols designed to ensure everlasting privacy. We map out the relationships and dependencies among these protocols, evaluate their security and efficiency under realistic assumptions, and identify unresolved challenges in the field. Our work provides a foundational reference for researchers aiming to design secure e-voting systems with everlasting privacy, paving the way for privacypreserving elections in the post-quantum era. Building on these insights, we propose a novel e-voting system that integrates the best practices from prior research while addressing their limitations. Leveraging the Hyperion scheme as a foundation, we develop an enhanced protocol that not only guarantees everlasting privacy but also introduces everlasting receipt-freeness and coercion mitigation. Unlike existing systems like Selene and Hyperion, which rely on computational assumptions for privacy, our protocol offers privacy even against adversaries with unlimited computational power. In secure electronic voting systems with everlasting privacy, the focus is on futureproofing privacy, while sometimes election verifiability relies on the computational soundness of zero-knowledge proofs (ZKP), which are vulnerable to quantum adversaries. Therefore, a key technical challenge is designing e-voting systems with efficient post-quantum cryptographic primitives to secure both privacy and verifiability against quantum attacks. In this thesis, we advance the state of post-quantum ZKPs by focusing on the ZKPs proposed by Jain et al., which are based on the conservative Learning Parity with Noise (LPN) assumption. We optimize the efficiency of these ZKPs, achieve formal security verification using EasyCrypt, and uncover flaws in existing implementations, demonstrating their vulnerability to malicious provers. Additionally, we construct the first code-based ZKP of shuffle, enabling a verifiable and privacy-preserving e-voting protocol with mixing-based tallying. Our e-voting system ensures both verifiability and vote privacy through the computational difficulty of decoding random linear codes, marking it as the first verifiable code-based e-voting system.
Electronic voting systems have long been proposed as a means of modernizing democratic participation by improving accessibility, reducing administrative costs, and accelerating electoral processes. Nevertheless, existing electronic voting architectures frequently rely upon centralized infrastructures that introduce significant challenges concerning transparency, security, auditability, and public trust. Blockchain technology has emerged as a promising alternative capable of addressing many of these limitations through decentralization, immutability, and distributed consensus. Despite considerable research activity, many proposed blockchain voting solutions remain conceptual, while relatively few studies present fully implemented and experimentally evaluated frameworks integrating multiple complementary security mechanisms.This study presents the design, implementation, and evaluation of a secure blockchain-based electronic voting framework built upon Hyperledger Fabric 2.4. The proposed architecture integrates smart contracts, distributed consensus mechanisms, AES-256 cryptographic vote protection, a conceptual zero-knowledge proof layer, and Merkle-tree-based integrity verification within a permissioned blockchain environment. A functional prototype was implemented in Go chaincode and deployed within a simulated regional election scenario representing the four prefectures of Crete, Greece.The study adopts a Design Science Research methodology and evaluates the proposed framework through a series of functional, security, and scalability experiments. The evaluation examined voter eligibility enforcement, duplicate vote prevention, ballot confidentiality, ledger integrity, auditability, and resistance against five distinct attack scenarios, including unauthorized ballot modification, ballot injection, and timestamp manipulation.The findings demonstrate that the proposed framework successfully preserves voter anonymity, prevents duplicate voting, detects unauthorized modifications in all tested scenarios, and enables transparent and independently verifiable election outcomes. While the results confirm the suitability of permissioned blockchain architectures for secure digital elections, several challenges remain, particularly regarding scalability, endpoint security, legal compliance, and large-scale deployment.Overall, this study contributes both a practical implementation and an empirical evaluation of a blockchain-enabled electoral infrastructure, providing insights into the future development of secure digital democratic systems.
This study examines and compares the effectiveness of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) models in forecasting volatility across three distinct financial markets: cryptocurrencies, Indonesian stocks, and U.S. stocks. The research analyzes daily closing price data from April 2018 to September 2024, focusing on five major cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, and Ripple), five Indonesian blue-chip stocks (BBCA, BBRI, BYAN, BMRI, and TPIA), and five major U.S. stocks (Apple, Nvidia, Microsoft, Google, and Amazon). Using comparative analysis of ARCH(1), GARCH(1,1), and EGARCH(1,1,1) models, the study evaluates their predictive accuracy through multiple metrics including AIC, MAE, RMSE, and SMAPE. Results indicate that EGARCH(1,1,1) generally performs better for cryptocurrencies and U.S. stocks, while GARCH(1,1) shows superior performance for Indonesian stocks, suggesting that volatility patterns and optimal forecasting models vary across different market contexts.
This article critically examines the adoption of Bitcoin as legal tender in El Salvador, contextualising it within the legacy of official dollarisation after 2001. First, we empirically assess the benefits and costs of dollarisation, finding that, despite some theoretical claims, the benefits remain questionable in hindsight, while the costs for the country were relatively low. Second, we explore Bitcoin's role as legal tender, proposing its understanding as a form of International Money and its potential in facilitating remittances. Building on this, we show that the existing dollarisation and a āsoft adoptionā of Bitcoin contributed to a comparatively low risk and low associated costs of introducing Bitcoin as a second legal tender. Third, we situate these developments within the broader geopolitical context, where the global monetary and financial system and the hegemony of the USD (the current World Money) are increasingly being repoliticised. In this light, the adoption of Bitcoin can be seen as a trial-and-error, unsuccessful at best, attempt by the Salvadoran government to enhance its leverage, improve remittance flows, and provide a low-cost escape valve in an evolving global landscape.
This paper investigates the volatility dynamics and underlying long memory features of four major cryptocurrencies-Bitcoin, Ethereum, Litecoin, and Ripple-which were selected due to their high liquidity, large trading volumes, and historical significance in the digital asset market. The long-range dependence exhibited in cryptocurrency markets is often overlooked. However, based on the strong evidence of persistent dependence in the return series, we adopt advanced volatility models that are capable of accommodating high volatility and heavy-tails, as well as the long memory properties of cryptocurrencies. Specifically, we employ long-memory extensions of the GAS (Long memory GAS) and GARCH (Fractionally Integrated Asymmetric Power ARCH) models, integrating heavy-tailed innovation distributions: the Generalized Hyperbolic Distribution (GHD) and Generalized Lambda Distribution (GLD). Standard GARCH and GAS models are included as benchmarks. The performance of the models are assessed using Value-at-Risk (VaR) estimation, backtesting (in-sample and out-of-sample) and volatility forecasting metrics. The results indicate that long memory models, particularly the FIAPARCH model, consistently outperforms the standard GAS and GARCH models in capturing tail risk and the volatility persistence. These findings emphasize the critical role of long memory in modeling the risk of cryptocurrencies, indicating that accounting for volatility persistence can significantly enhance the accuracy of risk estimates and strengthen risk management practices.