Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
This chapter examines the dual nature of virtual currencies. It mainly focuses on Bitcoin’s role in both financial innovation and illicit finance. This chapter analyzes the core mechanisms of anonymity and decentralization that make cryptocurrencies attractive to criminal activity. It was exemplified in the landmark Silk Road darknet marketplace case. The discussion traces the evolving regulatory response, from initial enforcement actions to the development of structured frameworks such as the GENIUS Act for stablecoins and the CLARITY Act for digital asset market classification. Further analysis covers the application of traditional securities and commodities laws to decentralized finance (DeFi). The MNGO Markets illustrated its exploitation case. The discussion centers around two blockchain applications: cross-border payments and the creation of immutable smart contracts to comply with General Data Protection Regulation (GDPR). This chapter concludes that cryptocurrencies exist as a dual-purpose technology. The system requires a sophisticated regulatory approach that lowers both financial crime risks and market integrity threats while preserving the potential for technological innovation.
Yongqiang Du, Chen-Xun Weng, Feng Xie, Ming-Yang Li · 13 authors
Popularized by the Bitcoin cryptocurrency, blockchain technology establishes a decentralized digital framework that utilizes cryptographic and consensus protocols to secure data against unauthorized modification. Consequently, blockchain has found broad adoption across diverse fields, including finance, data management, healthcare, and digital asset governance. In the quantum computing era, a paramount objective for blockchain is to preserve its foundational advantages of cryptographic integrity and decentralized fault-tolerant resilience. In principle, quantum digital signatures and quantum Byzantine agreement protocols offer foundational security guarantees and tolerate up to one-half of malicious nodes for blockchain. However, the practical realization of such a quantum-enhanced blockchain remains a significant and multifaceted challenge. Here, we propose and experimentally demonstrate a fully operational hybrid quantum blockchain architecture built on photonic integrated circuits and deployed over commercially available classical telecommunications infrastructure. The system achieves a fault tolerance of nearly one-half, surpassing the classical limit, while reaching consensus on a timescale of seconds. A deployed food traceability application validates the practicality of the proposed architecture, achieving a throughput of approximately 500 transactions per second. This work establishes a foundation for practical quantum blockchains, enabling secure, scalable, and decentralized information processing in the emerging quantum era.
Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relationships, suspicious behaviors, and market dynamics for risk assessment. To address these limitations, we propose ManiScope, an LLM-assisted visual analytics system for analyzing trade-based manipulation risks in cryptocurrency markets. ManiScope provides coordinated views of token distributions, holder relationships, detailed holder behaviors, price dynamics, and suspicious trading patterns. To further enhance user analysis, ManiScope introduces a human-LLM collaborative visual analytics framework. Rather than acting as a basic reactive LLM assistant, the framework positions the LLM as a co-analyst that infers users' analytical intent and emerging hypotheses from interaction context and surfaces relevant visual, statistical, and synthesized evidence for hypothesis evaluation. This design reduces repetitive inspection and strengthens evidence-based reasoning. We evaluate ManiScope through two case studies and a user study with 12 experienced cryptocurrency practitioners. The results suggest that ManiScope supports effective risk assessment of manipulation, reduces manual effort in evidence-seeking, and organizes findings around user hypotheses.
This study investigates the growing role of stablecoins within the global financial system and examines their potential integration into traditional foreign exchange markets. Despite the rapid expansion of stablecoins, empirical evidence comparing their market dynamics with those of non-stable cryptocurrencies remains limited. To address this gap, the study adopts a descriptive case study design based on documentary analysis and secondary quantitative market data. The documentary review establishes the theoretical foundations of stablecoins and their relevance to foreign exchange markets, while the quantitative analysis relies on market data collected from CCData, DefiLlama, and Statista. Weekly market observations covering the period from April 2019 to May 2024 were analyzed using descriptive statistics, comparative analysis, volatility measures, Pearson correlation analysis, and one-way ANOVA. The findings reveal that stablecoins exhibit significantly lower price volatility than Bitcoin while maintaining high levels of market liquidity and trading activity. Among the analyzed assets, Tether (USDT) remains the dominant stablecoin, followed by USD Coin (USDC) and Binance USD (BUSD). The statistical analysis confirms significant differences between stablecoins and Bitcoin, highlighting the distinct market behavior of reserve-backed digital assets. These findings suggest that stablecoins have evolved beyond their traditional role as cryptocurrency trading instruments and are increasingly functioning as efficient mechanisms for cross-border payments, liquidity management, and decentralized finance applications. This study contributes to the literature by providing an integrated empirical comparison of stablecoins and non-stable cryptocurrencies while demonstrating how the stability, liquidity, and operational characteristics of reserve-backed digital assets may facilitate their future integration into traditional foreign exchange markets. The findings also provide practical implications for policymakers, financial institutions, and regulators seeking to develop secure and efficient digital payment infrastructures supported by appropriate regulatory frameworks.
Ignatia Bintang Filia Dei Susilo, Vidya Purnamasari, Sulistya Rini Pratiwi, Yelly Zamaya · 5 authors
The rapid development of smart-contract-based blockchain ecosystems has transformed the perception of digital assets. However, the extent to which these assets are influenced by macroeconomic conditions in emerging markets remains poorly understood. This study aims to examine the long-term and short-term relationships between three major smart-contract platforms: Ethereum (ETH), Build and Build (BNB) Chain, and Solana (SOL), and several Indonesian macroeconomic indicators: money supply (M2), consumer price index (CPI), the rupiah-to-US dollar exchange rate (IDR/USD), and the policy interest rate (BI Rate). The study draws on monthly data spanning April 2023 to September 2025. The findings reveal that each platform exhibits a distinct degree of sensitivity to Indonesian macroeconomic conditions. Overall, the three platforms demonstrate a strong long-run relationship with the selected macroeconomic variables. The rising money supply (M2) tends to have a positive effect on all three platforms, while the influence of the exchange rate varies across ecosystems. Furthermore, this study traces how shocks in macroeconomic variables are transmitted to cryptocurrency prices and identifies distinct volatility patterns across the three platforms. Its findings contribute to understanding the relationship between crypto assets and macroeconomic conditions. It also offers practical insights for portfolio diversification strategies and for developing regulatory frameworks in Indonesia's growing digital asset market.
This study examines the regulation of blockchain technology and cryptocurrencies in Morocco’s foreign exchange market, focusing on the challenges posed by restrictive regulatory frameworks and their implications for financial stability. Although cryptocurrency transactions have been officially prohibited since 2017, their use has continued to expand through informal and peer-to-peer channels, raising concerns about the effectiveness of prohibition-based regulation. Using a systematic literature review based on the PRISMA framework, this study analyzes academic publications, institutional reports, and international regulatory developments covering the period 2018–2026. The findings reveal a persistent mismatch between formal regulation and actual market practices, resulting in regulatory arbitrage, weak enforcement, and the expansion of informal cryptocurrency activities. The analysis further highlights significant macro-financial risks, including capital flight, exchange rate pressures, and reduced monetary policy effectiveness. By explicitly linking cryptocurrency regulation to foreign exchange market dynamics in an emerging economy, the study addresses an underexplored area in the literature. It concludes that Morocco’s current restrictive approach is unlikely to remain effective and argues for the adoption of a more adaptive, risk-based regulatory framework capable of promoting financial innovation while preserving macroeconomic stability and regulatory oversight.
Centralized cryptocurrency exchanges (CEXes) enable fast off-chain conversions between hundreds of coins. It is an open question which algorithmic trading patterns occur on these platforms. A major challenge to measuring CEXes is that their public trade data does not contain addresses or trader identifiers allowing linkage. We propose a novel methodology to infer one-way arbitrage (OWA) trading in anonymized spot trade data from CEXes. We identify 402 M likely OWA sequences in 5 years of trading on Binance (and almost 2 M during 9 years on Kraken), accounting for 0.94 % and 0.13 % of the total traded volume, respectively. While we estimate total profits of $31.2 M on Binance and $975 k on Kraken, profits from individual OWA sequences are less than $1 on average after accounting for trading fees. We also observe that OWA has become faster over time, while the profitability of individual sequences has decreased. Our findings highlight that pricing discrepancies regularly occur in CEXes, and raise questions for future work to identify the precise circumstances that enable profitable OWA.
Cryptocurrency markets exhibit periodic bursts in volatility and volume at one-minute, five-minute, and quarter-hour marks. Using trade data for six Binance perpetual contracts, we link these bursts to algorithmic participation: trade-size roundness declines sharply during them. The Autocorrelation Map, a clock-phase-resolved display, reveals serial dependence in order flow and returns at quarter-hour openings that conventional measures obscure. Opening returns are predictable out of sample, while opening order imbalance predicts returns over four to twelve hours, with much weaker effects at finer clock-time frequencies. Together, these findings characterize periodic algorithmic trading and its cross-frequency variation.
Discover how Tokemak is revolutionizing decentralized finance through its advanced liquidity management solution. Get insight into the protocol's mechanism, governance structure, token purpose, and the significance of its emergence in the DeFi world.
Ravindran Kandasamy, Chandan Chavadi, H. Chittoo, Nidhi Shukla
Online commerce, despite its infinite development possibilities, now raises the specter of global security. A huge amount of personal data is at risk from cyberattacks, such as hacking and identity theft, that harm companies and consumers alike. The traditional way of keeping everything in one place leads to unauthorized access and manipulation, thus requiring stronger security measures. The same decentralized, unbreakable encryption and immutable record keeping that give these barter platforms strong protection against fraud are also features of distributed ledger technology. Decentralization removed control from one single source, making it less likely that there will be any tampering and deception will become slim. Blockchain networks featuring “smart contracts” that make the terms of a deal transparent and enforce contracts without the need for go-betweens. This chapter provides an analysis of how the blockchain can enhance e-privacy in e-commerce, with a focus on the foundations and attributes of blockchain to overcome current threats. As the technology becomes widespread, real cases are proving to revolutionize data security. New Use Cases And Research Using Distributed Ledgers For Enhanced Security.
To make the payment system robust and user friendly, decentralized based Scan and Pay system need to be designed. This paper integrates the Unified Payments Interface (UPI) of India with the Solana-based Blockchain to make the payment system decentralized. Solana offers a high throughput and low-cost based decentralized infrastructure which is combined with the simple and reliable UPI system. So, the proposed system enables cryptocurrency transactions linked to UPI while maintaining user friendliness, scalability, and regulatory compliance. The designed method uses a secure architecture powered by smart contracts and modular design. It offers a viable bridge between centralized financial networks and emerging Web3 ecosystems. Proposed Solana-based UPI is compared with the Non-Solana based UPI which is using Blockchain. Results show that there is improvement of 91% in transaction latency and 95% in transaction cost as compared to the Non-Solana based UPI system.
Existing cybercrime classification schemas capture contact metadata and financial transactions but omit the psychological manipulation techniques perpetrators employ. We present a forensic schema (four categories, 35 questions) adding 11 manipulation indicators and cryptocurrency evidence fields to established forensic foundations. Applied to 10,994 victim reports via large language model (LLM)-driven annotation and validated against two human annotators (mean LLM-human $κ= 0.69$, matching inter-annotator $κ= 0.68$), the schema revealed a statistically distinct manipulation profile for each major fraud type (Cramer's $V$ up to $0.790$). A rationale-based evidence audit nonetheless exposed a forensic detail gap: detection of manipulation techniques was reliable, but victim narratives varied widely in the actionable detail supporting each Yes answer, and blockchain-specific identifiers were nearly absent. These findings point to AI-assisted victim intake with schema-informed follow-up questions as the most direct way to close the gap. The tiered annotation strategy also provides a reusable template for LLM-based extraction from other forensic text domains.
Monero is a privacy-focused cryptocurrency that deploys the Dandelion++ protocol and incorporates anonymity networks (such as Tor and I2P) to prevent malicious attackers from linking transactions with their source IPs. In this paper, we demonstrate that Monero's integration of the Tor network introduces a fundamental vulnerability: a Monero Tor node's originated transactions are exclusively forwarded to two outgoing Tor hidden service nodes (proxy nodes) prior to clearnet propagation, enabling an adversary to capture originated transactions by occupying the target node's outgoing connections. Based on this observation, we propose \textit{ProxyMark}, a three-stage deanonymization framework for the Monero Tor network, comprising node role identification, originated transaction identification, and node location deanonymization. Through experiments on the live Tor network, Monero mainnet, and testnet, we empirically demonstrate the effectiveness of \textit{ProxyMark} in successfully deanonymizing transactions originating from Monero nodes over Tor.
In the previous research of the authors, the dynamics of cryptocurrency using blockchain technology have been studied. The chapter captures the present state of research on legal challenges related to the applicability of cryptocurrency in India by providing a critical review. An overview of pre- and post-pandemic transactions by investors in digital currency has been discussed and reviewed. In the current study, the author(s) try to examine the impact of blockchain technology on trading and business, with an emphasis on the growth and sustainability of the business. The business process will benefit from effective tracking, visibility, security improvements, and cost savings as a result ( Pal et al., 2021 ). Therefore, to ensure the legitimacy of such items, trust and confidence are factors that need to be considered (Loebbecke and Lueneborg, 2018). Through a systematic review of the literature, the application in various aspects of different types of businesses is explored, identifying the challenges in 24 blockchain implementation and looking for future trends along with the regulatory framework of trading and business in India. This chapter is important for scholars, researchers, and even entrepreneurs to understand the pedagogy behind using any technology with safe and secure transactions in business.
Blockchain is a distributed ledger system that uses a decentralized consensus protocol to record transactions securely over a network of computers. Unlike established centralized systems, blockchain runs on a peer to-peer network with every participant (node) having access to the entire database and its full history. This, then, has the advantage of making the system more resilient to corruption or hacking. The ledger (transaction history) is replicated across multiple participants on the hardware framework. Transparency: All participants have the same copy of the blockchain (without a need for a central authority). Autonomous agreements are contracts that execute themselves because their content is directly encoded in them. They apply and effectuate the conditions of a contract without intermediaries when certain predefined stipulations come into play. The decentralized and secure method in which cryptocurrencies operate is through the mechanism of blockchain, which is widely acknowledged. It can simplify the process, decrease fraud, and make it safer. This study pursues a comprehensive survey of the diversified use cases of blockchain throughout the global financial ecosystem. This chapter is an effort to shed light on the Strengths, Weaknesses, Opportunities, and Threats of blockchain technology in financial services.
We show that frictions at cryptocurrency exchanges, captured through consumer complaints, are reflected in the relative pricing of Bitcoin across U.S. trading venues. Using daily Bitcoin prices and CFPB complaint data, we find that average cross-exchange price deviations relative to Coinbase are significantly higher on days with at least one exchange-targeted Bitcoin complaint, indicating that Bitcoin on Coinbase trades at a larger relative discount on those days. The effect strengthens with complaint intensity. The results survive placebo tests using non-exchange complaints and diagnostics for omitted-variable bias. Complaints targeting Coinbase are associated with cumulative abnormal returns for Coinbase stock that are about 3.17 percentage points lower around complaint days. Overall, our evidence indicates that consumer complaints are associated with both cross-exchange Bitcoin price deviations and the equity valuation of the affected exchange.
Zero-knowledge privacy protocols let users hide transaction details on public blockchains. Systems like Tornado Cash, FixedFloat, and the Houdini Private Swap feature recently added to Jumper rely on cryptographic techniques that unlink sender and receiver addresses. These constructions give legitimate users meaningful protection for their financial activity. They also create a straightforward dual-use dilemma. The February 2025 Bybit incident supplies a clear example. Thieves stole $1.5 billion in ETH, the largest cryptocurrency theft on record. The FBI linked the attack to North Korea’s Lazarus Group. The stolen funds moved quickly through Tornado Cash. The resulting lack of transparency triggered a wave of customer withdrawals. Bybit responded by securing loans of several hundred million dollars from other institutions to keep its operations running. Cases like this demonstrate that zk-based privacy tools, when used at large scale for illicit purposes, can accelerate liquidity crises and place costs on market participants who had no involvement in the original theft. The real problem is not the underlying mathematics that delivers privacy. It lies in the missing mechanisms that could impose accountability on criminal actors while leaving the privacy protections for everyone else intact.
The rapid integration of cryptocurrencies into mainstream finance has introduced a novel form of digital collateral into the mortgage lending landscape, yet the consequences for traditional real estate-backed lending applications remain poorly understood. Integrating the Technology–Organization–Environment (TOE) framework with the core-satellite investment model, this study investigates if crypto-backed products displace conventional real estate-backed applications. Utilizing traditional mortgage application records from a financial institution adopting crypto-collateral in 2022, we find that crypto-backed mortgages significantly reduce traditional mortgage approval rates. This "crowding out" effect is intensified by regional cryptocurrency legitimacy and housing illiquidity, while mitigated by property information insensitivity and speculative concerns. Furthermore, results reveal heterogeneous impacts across demographics: this innovation potentially exacerbates racial discrimination while alleviating age-based disparities. These findings underscore the complex interactions between emerging fintech and traditional mortgage lending, suggesting that collateral innovation may redistribute credit access across diverse market segments.
Purpose This study develops a pricing and contract design framework for cryptocurrency catastrophe (CAT) bonds to transfer extreme crypto-native risks, including protocol exploits, exchange breaches and decentralized finance (DeFi) failures, to capital markets. The paper aims to address arbitrage-free valuation, sponsor-optimal contract design and trustless settlement under the unique informational and operational features of blockchain systems. Design/methodology/approach We propose a multi-trigger crypto CAT bond structure that jointly captures short-term catastrophic shocks and long-term systemic deterioration through oracle-reported loss metrics. An arbitrage-free valuation framework is developed under an incomplete market setting using the minimal martingale measure, while sponsor-optimal contract design is formulated under a dual-measure framework. Empirically, crypto loss dynamics are modeled using generalized extreme value distributions and copula-based dependence structures, whereas financial risk factors are modeled through ARIMA–GARCH and vine copulas. A smart-contract-enabled on-chain settlement architecture is further introduced to automate trigger evaluation and cash-flow execution. Findings Empirical results based on REKT crypto incident data demonstrate strong dependence between monthly extreme and aggregate losses, with heterogeneous dependence structures across blockchain ecosystems. Simulation studies show that trigger and principal repayment designs substantially affect bond price distributions and tail risk exposures. Conservative trigger structures generate more stable bond valuations, whereas aggressive structures exhibit greater downside dispersion. The proposed framework supports economically viable risk transfer while enabling transparent and timely settlement through blockchain-based execution. Originality/value This study develops, to the best of our knowledge, the first integrated framework for crypto native catastrophe bonds that combines arbitrage-free pricing, sponsor optimal contract design and smart contract-based on-chain settlement. Unlike traditional CAT bonds or cyber insurance-linked securities the proposed framework explicitly incorporates oracle-based observability, crypto-specific dependence structures and automated settlement, providing a novel mechanism for transferring systemic digital asset risks to capital markets.
Conventional cryptocurrency often leads to increased energy consumption and carbon emissions, while sustainable cryptocurrencies possess the potential to become a green alternative in portfolio management. This study aims to investigate the time-varying connectedness between sustainable cryptocurrency and green financial markets as well as hedging performance when facing market shocks, including COVID-19 and Russia-Ukraine war. TVP-VAR model with Fourier transform and Multivariate GARCH models are employed. The findings indicate that the pairwise connectedness between the sustainable cryptocurrencies and green financial markets has been at a low level, providing diversification benefits in investment portfolio. Besides, short-term connectedness dominates medium- and long-term connectedness. Sustainable cryptocurrencies show higher hedging effectiveness than traditional cryptocurrency.