Partha S. Dey, Aditya S. Gopalan, Vijay G. Subramanian
We investigate the time to consensus in Nakamoto blockchains. Specifically, we consider two competing growth processes, labeled \emph{honest} and \emph{adversarial}, and determine the time after which the honest process permananetly exceeds the adversarial process. This is done via queueing techniques. The predominant difficulty is that the honest growth process is subject to \emph{random delays}. In a stylized Bitcoin model, we compute the Laplace transform for the time to consensus and verify it via simulation.
Blockchain technology has recently undergone substantial investigation into the prospect of integrating it with several service sectors, having originally been designed for the Peer-to-Peer cryptocurrency network, Bitcoin Database security could be an expensive and time-consuming operation. When discussing a legally binding contract, the phrase "automated transaction protocol that, executes the terms of the agreement" is used. The Internet of Things (IoT), big data artificial intelligence technologies, and blockchain technology into the supply chain may help solve the transparency and traceability issue stated in the literature.
Bitcoin's (BTC) Difficulty Adjustment Algorithm (DAA) has been a source of vulnerability for incentive attacks such as selfish mining, block withholding and coin hopping strategies. In this paper, first, we rigorously study the short-term revenue change per hashpower of the adversarial and honest miners for these incentive attacks. To study the long-term effects, we introduce a new efficiency metric defined as the revenue/cost per hashpower per time for the attacker and the honest miners. Our results indicate that the short-term benefits of intermittent mining strategies are negligible compared to the original selfish mining attack, and in the long-term, selfish mining provides better efficiency. We further demonstrate that a coin hopping strategy between BTC and Bitcoin Cash (BCH) relying on BTC DAA benefits the loyal honest miners of BTC in the same way and to the same extent per unit of computational power as it does the hopper in the short-term. For the long-term, we establish a new boundary between the selfish mining and coin hopping attack, identifying the optimal efficient strategy for each parameter. For block withholding strategies, it turns out, the honest miners outside the pool profit from the attack, usually even more than the attacker both in the short-term and the long-term. Moreover, a Power Adjusting Withholding (PAW) attacker does not necessarily observe a profit lag in the short-term. In other words, even without a difficulty adjustment, a PAW attacker makes profits. It has been long thought that the profit lag of selfish mining is among the main reasons why such an attack has not been observed in practice. We show that such a barrier does not apply to PAW and relatively small pools are at an immediate threat.
The integration of Bitcoin into corporate treasuries constitutes a critical strategic choice, motivated by its capacity to bolster liquidity and serve as an inflation hedge, while simultaneously being encumbered by pronounced financial volatility and regulatory ambiguity. This investigation examines sectoral variations in Bitcoin adoption, with particular attention to the manner in which financial risks, regulatory structures, and decentralized governance mechanisms shape corporate conduct across the technology, cryptocurrency mining, retail, healthcare, and e-commerce sectors. Drawing on a cross-sectional dataset encompassing 102 publicly traded firms collectively holding 1,001,861 BTC, the analysis employs MAD-based volatility, Firth logistic regression incorporating a U.S. regulatory dummy to account for the BITCOIN Act of 2025, and heatmap visualization to evaluate risk profiles and adoption patterns. Results demonstrate marked sectoral disparities: the technology and mining sectors command predominant holdings yet confront heightened risk exposure, whereas retail and healthcare sectors proceed with greater caution, guided by considerations of cost-value efficiency and regulatory adherence. The U.S. regulatory dummy is significant, indicating the BITCOIN Act facilitates high Bitcoin adoption, while recent transactional activity is marginally significant. The heatmap accentuates the technology sector’s pre-eminence in aggregate Bitcoin reserves and illuminates the differential influence of regulatory frameworks in non-U.S. jurisdictions. Anchored in Institutional Theory, the Technology Acceptance Model, and Transaction Cost Economics, the study advances the field by quantifying sector-specific risks and visually representing regulatory impacts, thereby furnishing actionable insights for treasury risk management and regulatory policy formulation within a decentralized financial ecosystem.
<p>Este boletim quinzenal gratuito visa analisar o comportamento do Bitcoin, um ativo financeiro digital, oferecendo notícias, análises gráficas e informações sobre as mais recentes novidades, softwares e aplicativos relacionados a essa criptomoeda. Nosso objetivo é enriquecer as discussões em torno da cultura do Bitcoin, colaborando com a Amauta, uma instituição de economia criativa que busca disseminar conhecimento sobre inovação, educação e finanças na comunidade acadêmica e empresarial. Esperamos que este trabalho represente uma contribuição valiosa para o debate.</p> <p>Reconhecemos a importância do Bitcoin e seu impacto na economia global, motivo pelo qual nos dedicamos a fornecer informações atualizadas aos nossos leitores. Acreditamos que ao promover discussões e compreensão sobre o Bitcoin, podemos incentivar a adoção e o uso responsável dessa tecnologia disruptiva.</p> <p>Para além das análises e informações sobre o Bitcoin, incentivamos ativamente nossos leitores a se educarem sobre finanças pessoais e investimentos. Acreditamos que, munidos do conhecimento adequado, todos podem tomar decisões financeiras inteligentes e bem informadas.</p> <p>Comprometemo-nos a fornecer informações de alta qualidade e precisas, esforçando-nos para manter nossos leitores atualizados sobre as últimas tendências e desenvolvimentos no mundo do Bitcoin. Esperamos que este relatório seja do seu agrado e contribua para uma compreensão mais aprofundada do Bitcoin e das finanças pessoais em geral.</p>
A rápida expansão das criptomoedas transformou o cenário financeiro mundial ao introduzir novas formas de transação econômica baseadas em tecnologia digital descentralizada. No Brasil, o crescimento do uso do Bitcoin despertou atenção do meio jurídico devido ao potencial emprego desse ativo virtual em operações de ocultação patrimonial ilícita. O contexto impôs desafios regulatórios e investigativos ao ordenamento jurídico, exigindo respostas normativas para prevenir crimes financeiros digitais. O objetivo do estudo foi analisar a adequação do ordenamento jurídico brasileiro frente aos desafios impostos pela utilização do Bitcoin como instrumento de lavagem de dinheiro, especialmente após a promulgação da Lei 14.478/2022. A pesquisa utilizou abordagem qualitativa, método dedutivo e levantamento bibliográfico e documental, com base em doutrina, legislação e análise jurisprudencial. Antes da Lei nº 14.478/2022, o Poder Judiciário responsabilizava agentes envolvidos em crimes com criptoativos com fundamento na Lei nº 9.613/1998. Com o novo marco regulatório, houve fortalecimento de mecanismos de controle e rastreabilidade e ampliação do dever de cooperação de exchanges. A jurisprudência do STJ e do TRF-3 consolidou entendimento de que Bitcoin possui conteúdo econômico e pode ser objeto de medidas assecuratórias e responsabilização de intermediadoras digitais. O ordenamento jurídico brasileiro encontra-se em processo de adequação progressiva para responder a riscos jurídicos e financeiros vinculados ao uso ilícito de criptomoedas.
ABSTRACT This study examines how blockchain transparency and smart-contract automation, paired with anomaly-detection models, support early detection and calibrated deterrence of manipulation in cryptocurrency markets. Although transparent ledgers and rule-based execution raise the likelihood that irregular activity is flagged and investigated, they do not prevent fraud; my emphasis is detection, deterrence, and post-incident support. I analyze a long-horizon Bitcoin panel using rolling z-score screens and Isolation Forest to surface anomalies consistent with manipulative trading. I fix a false-positive budget ex ante and evaluate capacity-aware performance (Precision@k, PR-AUC, lead time), archiving time-stamped evidence bundles for auditability. Alerts cluster around episodes consistent with pump-and-dump behavior, large-holder moves, and event-driven dislocations, improving investigative triage without prevention claims. The framework provides actionable guidance for exchanges and regulators seeking to strengthen market integrity through auditable records and model-based alerts, and I release a human-in-the-loop agentic AI application that automates ingestion, screening, ranking, and auditable export. Data Availability: A replication package including the agentic AI GenApp (Streamlit code), requirements, and input templates (daily data, events, sentiment) is provided in Appendix B. The package reproduces the pipeline exactly as specified in Section IV and writes time-stamped artifacts for audit; it is intended for detection and deterrence workflows and makes no prevention claims. JEL Classifications: G12; G15; G18; G24; G14; G41; H83.
The rapid emergence of Stable Coins has completely altered the global landscape of digital finance. The benefits of blockchain technology, along with the typical advantages of a fiat currency, in the form of a stable coin, have had a surreal effect on the world of finance. The paper investigates the evolution, comparative merits and systemic risks of Stable Coins compared to Bitcoin, also uses them for advantages in decentralized finance, liquidity and international transactions. The results clearly show that the Stable Coins have become essential infrastructures of finance because of their low volatility, transaction efficiency but also their sensitivity to such issues as regulation and transparency of reserves. The study of the literature of the BIS, IMF and ECB gives evidence of the fact that stable coins will co-exist with the Central Bank Digital Currencies (CBDC), rather than that they will replace them. The proposed method gives evidence of how a system of collaborative regulation and transparency of reserves can be achieved to facilitate innovations but also protect global economic stability.
This study explores how integrating cryptocurrencies into traditional financial portfolios can influence investment performance. Focusing on Bitcoin and Ethereum alongside key European stock indices (BUX, DAX, and FTSE), the analysis examines whether blockchain-based assets can enhance diversification and improve the balance between risk and return. Using weekly market data from 2019 to 2023, the research applies Markowitz mean–variance optimization to identify optimal asset allocations under different objectives such as maximizing the Sharpe ratio, minimizing risk, and maximizing returns. The findings reveal that cryptocurrencies show weak correlations with European stock indices, suggesting meaningful diversification potential. When included in portfolios, Bitcoin and Ethereum can significantly boost returns, though they also increase volatility. Portfolios optimized for risk reduction favored traditional indices, while those targeting higher returns relied predominantly on cryptocurrencies. Overall, combining digital and conventional assets produced a more balanced performance, with the Sharpe-ratio–maximized portfolio demonstrating the best trade‐off between stability and profitability. These results indicate that cryptocurrencies can play a valuable complementary role in modern portfolio construction. They are most suitable for investors willing to accept higher risk in exchange for potentially greater rewards, while more risk‐averse investors may benefit from maintaining a stronger focus on traditional equity indices. The study contributes to understanding how blockchain‐driven assets can expand financial opportunities and supports a broader view of diversification in contemporary investment strategies.
Felipe Castaño, Constantinos Patsakis, Francesco Zola, Fran Casino
LockBit has evolved from an obscure Ransomware-as-a-Service newcomer in 2019 to the most prolific ransomware franchise of 2024. Leveraging a recently leaked MySQL dump of the gang's management panel, this study offers an end-to-end reconstruction of LockBit's technical, behavioral, and financial apparatus. We recall the family's version timeline and map its tactics, techniques, and procedures to MITRE ATT&CK, highlighting the incremental hardening that distinguishes LockBit 3.0 from its predecessors. We then analyze 51 negotiation chat logs using natural-language embeddings and clustering to infer a canonical interaction playbook, revealing recurrent rhetorical stages that underpin the double-extortion strategy. Finally, we trace 19 Bitcoin addresses related to ransom payment chains, revealing two distinct patterns based on different laundering phases. In both cases, a small portion of the ransom is immediately split into long-lived addresses (presumably retained by the group as profit and to finance further operations) while the remainder is ultimately aggregated into two high-volume addresses before likely being sent to the affiliate. These two collector addresses appear to belong to distinct exchanges, each processing over 200k BTC. The combined evidence portrays LockBit as a tightly integrated criminal service whose resilience rests on rapid code iteration, script-driven social engineering, and industrial-scale cash-out pipelines.
This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables.
This research examines deep-learning and machine-learning models for cryptocurrency price prediction, with a keen focus on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies exhibit high volatility, non-linear behavior and are able to react strongly to exogenous events, making their prediction and forecasting challenging. The primary aim of this research is to determine which predictive models yield optimal performance in characterizing these complexities and to provide empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons, and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be observed that GRU exhibited the lowest error rates in the majority of the assets, particularly in short-term predictions. LSTM demonstrated a promising ability to capture long dependencies, whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst among all the deep-learning algorithms, thereby demonstrating the extreme capability of these algorithms in modelling non-linear time sequences. The results confirm GRU as the most viable model for AI-powered crypto prediction and demonstrate the potential of hybrid architecture, at least in certain situations. This study will contribute to the existing debates about the role of deep learning in predicting financial outcomes and provide valuable insights to traders, analysts, and researchers navigating the uncertainties of the digital asset world.
Advanced blockchain technologies and growing environmental and economic uncertainties have Motivated us to investigate the impact of climate policy uncertainty (CPU) and global economic policy uncertainty (GEPU) on five green cryptocurrencies—ADA, EOS, IOTA, XLM, XTZ—selected based on energy efficiency and mining processes. We examined the short- and long-run impacts of alternative assets on these cryptocurrencies using a nonlinear autoregressive distributed lag model. In the long run, these cryptocurrencies are negatively affected by CPU and GEPU, questioning their safe-haven potential. In the short run, ADA, EOS, and XLM share a positive asymmetric relationship with CPU, whereas all cryptocurrencies have a negative asymmetric relationship with GEPU. Therefore, they can be considered a safe haven. In the short and long term, green bonds exert a positive impact, whereas interest rates, the S&P 500, and the gold index negatively impact these cryptocurrencies. In the short run, Bitcoin shows a negative relationship with EOS, IOTA, and XTZ and a positive relationship with ADA and XLM. Over the long term, Bitcoin exhibits a positive correlation with all cryptocurrencies. USD exhibits a positive relationship in the short run and a negative relationship in the long run with all cryptocurrencies. The findings offer practical implications for portfolio construction and investors dealing in the green cryptocurrency market.
Abstract This research paper provides a comprehensive analysis of Bitcoin, the world’s preeminent cryptocurrency, focusing on the economic drivers of its price formation, its broader impact on the economy, and the evolving dynamics of its volatility. Drawing on high-frequency econometric modeling, time-series analysis, and network-based prediction methods, the paper synthesizes insights from leading empirical studies to elucidate the factors shaping Bitcoin’s price, including supply-demand fundamentals, investor behavior, macro-financial indicators, transaction network structure, and the influence of derivative markets. Additionally, it explores Bitcoin’s adoption in key industries, its intrinsic and extrinsic value determinants, and the implications of its volatility for financial stability. The study concludes by reflecting on the future trajectory of Bitcoin as it transitions from speculative asset to potential mainstream medium of exchange, considering regulatory, technological, and market challenges. Keywords: Bitcoin, cryptocurrency, price formation, volatility, supply-demand, GARCH, partial differential equations, transaction networks, futures markets, economic impact
A TANULMÁNY CÉLJAA tanulmány célja a Bitcoin buborékok kialakulásának vizsgálata, megértése. A buborékok erős hasonlóságot mutatnak a Gartner-féle hype-görbe alakjával, ezért az egyes buborékok és a hype-görbe kapcsolata is ismertetésre kerül. Ezek mellett a Bitcoin-buborékok kialakulását elősegítő tényezők feltárására törekedtünk. ALKALMAZOTT MÓDSZERTAN A Bitcoin árfolyamának historikus adatait elemeztük, melyek alapján a buborékok kirajzolódnak. A buborékok létezésének alátámasztására, illetve a Gartner-féle hype-görbével való azonosítás érdekében kiszámoltuk az egyes buborékok különböző időszakaihoz tartozó kockázatokat, hozamokat is. Illesztettük a hype-görbét a Bitcoin árfolyamának alakulására, illetve a korrelációs kapcsolatot is vizsgáltuk. LEGFONTOSABB EREDMÉNYEK A szórásból számított kockázatok, illetve a relatív szórások is alátámasztották a feltételezést, mely szerint az egyes Bitcoin-buborékok követik a Gartner-féle hype-görbe alakját. A görbék illesztése és a korreláció vizsgálata pedig kimutatta, hogy van kapcsolat a hype és az árfolyam alakulása között. A szabályozás szerepe kritikus lehet a kriptovaluták árfolyamának alakulásában, és a különböző országokban bevezetett szabályozó intézkedések jelentős hatást gyakorolhatnak a befektetői bizalomra és az árfolyamokra. A Bitcoin-bányászat felezése szintén fontos esemény, amely befolyásolhatja a kínálatot és keresletet és ennek megfelelően az árfolyamokat is. Az utánzó magatartás, vagyis a befektetők tendenciája arra, hogy mások viselkedését másolják, szintén jelentős tényező a buborékok kialakulásában. Végül az intézményi szereplők stabilizáló hatását ismertettük. GYAKORLATI JAVASLATOK A tanulmányból kiderül, hogy a fent említett tényezők igen nagy befolyást gyakorolnak a Bitcoin árfolyamának alakulására, melyek közül a bányászatért járó jutalmak felezése a leginkább szembetűnő, illetve számítással alátámasztható. Az új szabályozások megjelenésével nem tudunk számolni, viszont a felezéssel járó árfolyamváltozással igen, melynek fő indikátora az utánzó magatartás, hiszen a befektetők hozamaik maximalizálására törekednek. Ezek alapján a tanulmány rávilágít, hogy egy igen kockázatos befektetési formáról van szó, melynek előrejelzése igen nehéz feladat.
Digital Asset Treasury (DAT) companies, public firms that hold large crypto reserves as a core strategy, deliver levered exposure to digital assets but face acute downside risk when equity premia over net asset value multiples (mNAV) compress in bear markets. This paper develops a survival framework that couples conservative treasury policy with an operating line that monetizes holdings independent of mark-to-market gains. Using Strategy (formerly MicroStrategy) as a case, we propose a "BTC-to-sats" payments rail that allocates a small, risk-capped liquidity sleeve of the treasury to Lightning Network channels, generating price-agnostic fee revenue (acquiring bps, routing, hedge/FX spread) while keeping settlement exposure near zero beta to BTC. We formalize a no-forced-sale condition and show how disclosed KPIs allow investors to test whether operating cash flows can bridge an 18 to 24-month bear without liquidations. The feasibility of the rail is supported by Strategy's Lightning initiative and empirical Lightning performance. Our model generalizes across DAT types and provides implementable disclosures that can sustain an mNAV premium through cycles.
This paper investigates the relationship between cryptocurrencies and other financial assets, with a particular focus on the dynamics of information flow between developed and emerging markets. To achieve this objective, the study applies a combined methodology of spillover index analysis and network topology based on graph theory. The analysis covers key cryptocurrencies (Bitcoin and Ethereum), stocks, and conventional currencies over the period November 2017 to September 2022, and distinguishes between short-term and long-run dynamics. The empirical findings show that in the short run, Bitcoin and Ethereum predominantly act as net shock transmitters, whereas in the long run, stocks and conventional currencies, together with Bitcoin and Ethereum, become the principal conveyors of spillover shocks. The network topology analysis corroborates these results by revealing the centrality of these assets in the spillover structure. By integrating spillover and network approaches across different markets and time horizons, this study contributes to the literature by providing a more nuanced understanding of how cryptocurrencies interact with traditional financial assets under varying market conditions.
The hydropower fleet in Austria is ageing and needs to be modernised to adapt to changing conditions in national and international energy systems. The financial viability of hydropower repowering projects remains a challenge because of high investment costs and long payback periods. A part from additional revenuestreams, a Bitcoin mining operation has the potential to be used as a flexible demand source also for curtailment and grid stability services. This thesis provides quantitative evidence on whether a Bitcoin mining operation can serve as an additional revenue stream to improve the investment metrics of a hydro repowering project in Austria, using a dynamic investment calculation and sensitivity analysis.The results show that Bitcoin mining can improve the financial performance especially for run-of-river plants with higher full load hours. These positive effects are sensitive to the volatility of the Bitcoin price and the network hash rate, making long-term returns difficult to predict.
Ethereum ist seit Jahren die größte Smart-Contract-Blockchain und nach Bitcoin die zweitgrößte Blockchain-Plattform. Smart-Contracts, die als dezentrale Anwendungen beschrieben werden können, laufen auf einer gemeinsamen Rechenplattform, auf der alle Teilnehmer auf einer geteilten Codebasis arbeiten. Zur Absicherung ist es nötig, dass ein Konsens über die Ein- und Ausgaben aller Smart-Contracts geschaffen wird. Die Ausführung von Smart-Contract-Code verbraucht sogenannte Gas-Einheiten, die als eine Art Treibstoff betrachtet werden können. Gas-Einheiten zeigen den erforderlichen Rechenaufwand an und haben direkte Auswirkungen auf den realen Energieverbrauch. Daher sollten idealerweise alle Smart-Contracts so implementiert sein, dass sie möglichst wenig Gas-Einheiten verbrauchen. Derartige Codeoptimierungsansätze sind nicht trivial. Zum Zeitpunkt des Verfassens dieser Diplomarbeit gibt es bereits solche Mechanismen, welche teilweise direkt in den gängigen Compilern integriert sind. Solche Mechanismen basieren in der Regel auf festen Mustern, welche manuell beschrieben werden müssen und dann auf Smart-Contracts angewendet werden können. In dieser Arbeit haben wir untersucht, ob klassische Verfahren zur Erkennung von Codeähnlichkeiten verwendet werden können, um Optimierungsmuster automatisch aus Quellcode-Repositories ableiten zu können. Zunächst haben wir einen Symbolic-Execution-Ansatz untersucht, welcher sich aufgrund von technischen Einschränkungen und der Abhängigkeit von veralteten Compiler-Versionen als ungeeignet erwies. Daraufhin haben wir einen Fingerprinting-Ansatz basierend auf Kontrollflussgraph-Blöcken gewählt. Mithilfe von Slither konnten wir Metriken wie Cyclomatic-Complexity, Fan-Out und Informationsfluss-Metriken extrahieren und anschließend Distanzen zwischen Codestücken berechnen, um mit den Ergebnissen potenzielle semantische Code-Klone zu erkennen. Wir haben die Evaluierung unseres Ansatzes auf 1.200 manuell markierten Smart-Contracts aus einem Datensatz mit 160.000 Einträgen durchgeführt, was zu 574 Vergleichen führte und konnten eine korrigierte Genauigkeit von 88% für die Erkennung von semantischen Code-Äquivalenzen auf Blockebene erzielen. Für 1.300 Code-Paare haben wir zusätzlich eine Gasverbrauchsmessung durchgeführt, indem wir die Blöcke in generierte Smart-Contracts verpackt und auf einer lokalen Blockchain ausgeführt haben. Dabei konnten wir tatsächliche gasreduzierende Codeänderungen identifizieren. Trotz einiger wesentlichen Einschränkungen zeigt das, dass das Mining gasoptimiertem Codes aus versionierten Source-Code-Repositories mittels Code-Metriken möglich ist.
We examine how wrapped tokens – tokenized representations of assets on other block/chains – contribute to cryptocurrency price discovery. Based on high-frequency data for Wrapped Bitcoin (wBTC), our results indicate that wBTC accounts for about 10% of the total price discovery of Bitcoin as measured by information shares. We show that wBTC’s contribution to price discovery is positively related to wBTC liquidity and trading volume as well as to important measures of decentralized finance activity. Our results have significant implications for the relationships between crypto-assets on different platforms as well as for systemic risk in the crypto-ecosystem. • Wrapped Bitcoin (wBTC) is a tokenized form of Bitcoin on other blockchains. • wBTC contributes significantly to Bitcoin price discovery. • Price discovery rises with liquidity and trading volume. • wBTC’s price discovery share increases with decentralized finance activity. • Decentralized finance plays an important role in Bitcoin pricing.
This thesis examines three distinct topics on settlement microstructure and market efficiency in both DeFi and TradFi. Chapter one introduces the thesis’ unifying lens, arguing that settlement microstructure drives market efficiency across these markets. It links the three papers by showing how access in Bitcoin private channels, timing in Ethereum intertemporal gas hedging, and composition in equity market retail participation jointly determine fees, latency, liquidity, and price discovery, while previewing the policy framework that renders these mechanisms legible, bounded, and measurable. Chapter two, based on the working paper “Private Settlement in Blockchain Systems” with Dr. Alfred Lehar, provides evidence that the settlement market in blockchain systems is not purely transactional and diverges from the predictions of a simple competitive auction model. Using data from the Bitcoin blockchain, we find that 5.88% of transactions, labeled as private, bypass the competitive auction and are routed directly to miners. Despite being more active than the average user, these transactions are consistently confirmed by a single miner, a statistically unlikely outcome in a competitive environment. Our findings suggest that high-demand users form long-term agreements with miners, paying, on average, 20% lower fees. This chapter also documents how such settlement contracts are structured and operate within an unregulated market. Chapter three, based on the working paper “Gas Tokens: Market for Future Settlement in the Ethereum Blockchain” with Dr. Alfred Lehar, examines the implications of gas tokens as a potential market for future settlement within the Ethereum network. We show that sophisticated and frequent users are more engaged in gas token markets, pre-purchasing tokens to hedge against fluctuations in gas prices and paying, on average, 15.25% lower settlement fees. Moreover, bots actively pursue arbitrage opportunities in gas token markets and hold substantial volumes. Our findings indicate that traded gas token prices have strong predictive power for future gas prices. This research contributes to the development of modern financial instruments for price discovery and hedging within the Ethereum network as a two-sided market. We also empirically analyze the implementation of the Ethereum Improvement Proposal EIP-1559 as a natural experiment. Chapter four, based on my working paper “Silencing the Noise: Amplified Effects, A Causal Study on Price Efficiency”, investigates the causal effects of noise trader removal on market liquidity. In September 2022, an unexpected internet disruption in Iran restricted noise traders while informed traders retained access through brokers. This disruption led to a 6.65-fold increase in the bid-ask spread and a 46.8% decrease in informed trade speed due to market access asymmetry. Social media censorship in affected regions further amplified information asymmetry, resulting in a 7.2% price impact. Using a five-year analysis of political unrest, this study disentangles the effects of unrest and internet disruption on noise trading activity. The findings reveal that political unrest increases regional noise trading activity, whereas internet disruption decreases it. When both unrest and internet disruption occur simultaneously, regional noise trading activity decreases by 23.5%. This paper provides novel insights into market microstructure and the dynamics of liquidity provision through noise trading in emerging markets.
This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.
The rising importance of cryptocurrencies as financial assets pushed their applicability from an object of speculation closer to standard financial instruments such as loans. In this work, we initiate the study of secure protocols that enable fiat-denominated loans collateralized by cryptocurrencies such as Bitcoin. We provide limited-custodial protocols for such loans relying only on trusted arbitration and provide their game-theoretical analysis. We also highlight various interesting directions for future research.