Abstract This study proposes a methodological strategy composed of econometric techniques and time series modelling to analyse the dynamic asynchrony between Bitcoin and a basket of traditional sustainable financial assets and emerging markets over a 10-year period marked by major economic and financial changes. The centrepiece of this proposal is the Relation Index that combines vector autoregression and detrended cross-correlation analysis to capture linear and nonlinear dependencies, causality, and time-scale sensitive correlations. Thus, this research fills existing gaps in understanding cross-market interdependencies by integrating cryptocurrencies, sustainability indices, and emerging economies into a rigorous multivariate time series framework. Sustainability indices, emerging markets and Bitcoin have shown a growing correlation since 2020, with both interest rates and Bitcoin having strong autoregressive components. The findings indicate that emerging market equities have undergone a structural shift towards synchronisation with global risk assets, with a correlation index that frequently exceeds 0.6 in periods of systemic stress. This evolution highlights the decline in the advantages offered by diversification in developed and developing economies in a complex and interrelated financial environment.
The emergence of decentralized finance (DeFi) has prompted a new, highly interwoven financial system in which the stability of the financial system is fundamentally dependent upon the existence of digital assets, in particular stablecoins, that serve as both a method of conducting transactions, collateral, and a source of liquidity. Although DeFi is said to be efficient, programmable, and disintermediated, the structural complexity and composability of the DeFi system also create new systemic- risk channels that are similar to the impact of fragilities in conventional finance (Auer et al., 2024; Xu et al., 2024). The role of stablecoins in this architecture is to facilitate trading, leverage, and settlement of protocols, though the design and collateralization process puts them at risk of derailing the stablecoin and liquidity shocks and runs (Catalini et al., 2022; Hoang and Baur, 2024). These dynamics are similar to traditional bank run and liquidity crisis theories, in which the lack of coordination and redemption could cause damaging withdrawal effects (Diamond and Dybvig, 1983; Bernardo and Welch, 2004). In the case of the elements of DeFi, the volatility can spread very quickly between lending pools, automated market makers, and cross-chain bridges, facilitating the transfer of stress and volatility across platforms and asset classes (Zieba et al., 2019; Pagnottoni, 2023). The lack of centralized backstops, along with the algorithmic governance and large leverage, also serves to further enhance the risk of local perturbations developing into system-wide contagion. Such vulnerabilities have increased the arguments for risk-sensitive system design, greater transparency, and regulatory coordination to reduce spillovers to the financial system more generally (FSB, 2018; Manaa et al., 2021; Fantacci and Gobbi, 2024). Altogether, the discussion shows that the concept of stablecoins is an important crossroads in the stability environment of DeFi: not only do they allow markets to operate, but also they are a primary medium through which runs and shocks are propagated. The knowledge of these mechanisms is paramount in the formation of the resilient protocol design, supervisory systems, and eventual research on systemic risk of programmable financial systems.
Artificial Intelligence (AI) has become a critical driver of firm survival in the banking industry, particularly for deposit money banks (DMBs) facing increasing challenges such as economic volatility, regulatory compliance, cybersecurity threats, and rising customer expectations. This study explores the role of AI in enhancing operational efficiency, risk management, fraud detection, customer experience, and financial resilience in the banking sector. AI-powered technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), are transforming how banks analyze financial risks, detect fraudulent transactions, automate operations, and provide personalized banking services. Research findings indicate that AI adoption has led to a 35% reduction in loan defaults, a 40% improvement in operational efficiency, and a 60% decline in financial fraud cases, highlighting its transformative potential in ensuring the survival and competitiveness of DMBs. Despite these advancements, AI adoption in the banking sector is hindered by high implementation costs, cybersecurity vulnerabilities, workforce resistance, and regulatory uncertainties. Many banks, particularly in developing economies like Nigeria, struggle with legacy banking systems, lack of AI governance frameworks, and concerns over algorithmic bias in lending decisions. Additionally, AI-driven financial innovations, such as blockchain integration, decentralized finance (DeFi), and AI-powered ESG compliance solutions, are reshaping the banking industry, yet require strategic policy alignment and investment to maximize their benefits. The study identifies gaps in existing literature, including the need for empirical research on AI’s long-term impact on firm survival, its role in financial inclusion, and the ethical challenges of AI governance in banking. To bridge these gaps, future research should focus on developing AI implementation models suited to the challenges of emerging economies, exploring AI’s potential in expanding financial access to underserved populations, and strengthening AI-driven sustainability and ESG compliance frameworks in banking. As AI continues to evolve, deposit money banks must embrace a balanced approach that integrates AI innovation with regulatory oversight, cybersecurity safeguards, and workforce upskilling to ensure long-term survival and competitiveness in the digital financial landscape
Crypto currency has emerged as one of the most disruptive innovations in modern financial history. Beginning with the introduction of Bitcoin in 2009, decentralized digital currencies have challenged traditional financial systems by enabling peer-to-peer transactions without centralized intermediaries. This paper examines the impact of cryptocurrency on global financial systems, including banking, monetary policy, financial inclusion, cross-border payments, and regulatory structures. It explores both opportunities—such as decentralization, efficiency, and innovation—and risks, including volatility, regulatory uncertainty, financial crime, and systemic threats. The study also analyses the rise of decentralized finance (DeFi) and Central Bank Digital Currencies (CBDCs) as responses to the growing influence of blockchain-based financial models. The research concludes that while cryptocurrencies present transformative potential, their long-term integration into financial systems will depend on regulatory clarity, technological scalability, and macroeconomic stability.
The persistence of global financial instability, sovereign debt fragility, inflationary volatility, and asymmetric currency dependence has intensified scholarly debate regarding the structural limitations of centralized fiat-monetary regimes. This study advances a theoretically grounded and institutionally operational Monetary Plurality Framework designed to enhance systemic resilience through diversified currency architecture, asset-anchored valuation, and hybrid governance integration. Drawing upon interdisciplinary monetary theory, comparative institutional analysis, and resilience economics, the research develops a multi-tier monetary ecosystem combining centralized macro-stability with decentralized micro-adaptability enabled by distributed ledger technologies. The findings suggest that monetary diversification reduces crisis transmission, strengthens domestic productive linkage, and improves long-term financial sovereignty. The study contributes to the literature by synthesizing complementary currency theory, asset-backed monetary design, and digital governance economics into a unified resilience-oriented model suitable for volatile global conditions.
As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.
Decentralised finance (DeFi) has profoundly reshaped global capital markets, enabling automatic transactions, eliminating the need for intermediaries, and accelerating transaction settlement times. Despite these significant advancements, institutional involvement in DeFi remains very low. The lack of institutional participation can be attributed to the lack of an enforceable compliance mechanism at the protocol level; that is, once a transaction is confirmed as having been completed on the blockchain, it cannot be undone or disputed in any meaningful way. The existing compliance mechanisms are primarily retrospective, meaning that they generate alerts after a transaction has occurred instead of preventing illicit transfers in advance. Regulated financial institutions that transact in cryptocurrency bear the ultimate financial risk and regulatory burden. The UK FCA has made it very clear through CP25/41 that there are now specific regulatory expectations regarding the existence of adequate pre-settlement controls [2]. We introduce AMTTP Version 4.0, which has been designed to have a four-layer architecture explicitly intended to support deterministic compliance enforcement in DeFi institutions. Layer I provides SDKs, REST APIs, and web applications intended for programmatic and human interaction with AMTTP; Layer II provides a compliance orchestration layer that combines (i) machine learning risk scoring (ii) graph analysis (iii) sanctions screening, and (iv) policy adjudication into a single deterministic decision-making matrix; Layer III consists of an offline training pipeline with a Composite Teacher that uses an AutoencoderEnhanced XGBoost (w = 0.4), seven FATF AML Mode Patterns (w = 0.3), and graph structural properties (w = 0.3) in order to produce pseudo-labels (SLPs) for the Student pipeline across 2,640,000 transactions; and finally, Layer IV supports the physical infrastructure for AMTTP deployment, which consists of 18 smart contracts on Ethereum Sepolia, 17 containerised microservices, and a Database Persistence Tier (MongoDB, Redis, Memgraph, IPFS). The Infrastructure Security features multioracle threshold signatures, replay protection & zkNAF a zeroknowledge proof framework that allows for privacy preserving verification of KYC credentials, risk ranges & non-membership from sanctions. In addition, TLS Encryption, Rate Limiting, Cloudflare Tunnel integration & the UI Integrity Service provide an additional layer of protection at the infrastructure level. This paper aims to demonstrate that deterministic compliance can be integrated into decentralised finance at an architectural level. In order to support this assertion, the client SDKs (TypeScript and Python) are released as open source.1
Decentralized Finance (DeFi) has successfully rebuilt the plumbing of Wall Street (Trading, Lending, Derivatives) but has failed to replicate its engine: Credit. Currently, all DeFi lending is Over-Collateralized. To borrow $1.00, a user must deposit $1.50 in assets. This is not "Credit"; it is merely "Liquidity Swapping." It restricts DeFi to wealthy speculators and excludes 99% of global borrowers who need capital precisely because they do not have assets to pledge. The Klyrox Sovereign Credit Protocol introduces the first scalable framework for Under-Collateralized Lending on-chain. By transforming the Klyrox Identity Token (Epistemic Capital) into a programmable "Credit Score," we allow users to pledge their History instead of their Assets. This paper outlines the mathematical risk models that allow lenders to safely issue loans with 50% or even 0% collateral, unlocking a trillion-dollar market for on-chain personal finance.
Private agents do not internalize the impact of their investment decisions on the sovereign’s bond prices and default risk. Therefore, a standard externality argument implies that investment is insufficient and that a subsidy can improve welfare, if financed by non-distortionary means. We contrast this logic with a countervailing force. When the sovereign is impatient relative to households, plausibly due to political economy factors, it finds laissez-faire capital accumulation excessive and might prefer instead to tax it. We embed both mechanisms in a sovereign default model with decentralized capital investment, long-term public debt, and stochastic trend growth, calibrated to salient features of the Spanish economy. We find that the impatience channel dominates quantitatively, to such an extent that laissez-faire is preferable to the government’s ideal fiscal policy, based on households’ welfare.
Liquidation of collateral are the primary safeguard for solvency of lending protocols in decentralized finance. However, the mechanics of liquidations expose these protocols to predatory price manipulations and other forms of Maximal Extractable Value (MEV). In this paper, we characterize the optimal liquidation strategy, via a dynamic program, from the perspective of a profit-maximizing liquidator when the spot oracle is given by a Constant Product Market Maker (CPMM). We explicitly model Oracle Extractable Value (OEV) where liquidators manipulate the CPMM with sandwich attacks to trigger profitable liquidation events. We derive closed-form liquidation bounds and prove that CPMM transaction fees act as a critical security parameter. Crucially, we demonstrate that fees do not merely reduce attacker profits, but can make such manipulations unprofitable for an attacker. Our findings suggest that CPMM transaction fees serve a dual purpose: compensating liquidity providers and endogenously hardening CPMM oracles against manipulation without the latency of time-weighted averages or medianization.
Amid the institutionalization wave of Decentralized Finance (DeFi), U.S. institutional Liquidity Providers (LPs) have emerged as the core incremental capital for leading Decentralized Exchanges (DEXs). However, the adaptation gap between Uniswap V4's concentrated liquidity mechanism and institutional risk preferences, as well as regulatory compliance requirements, has hindered their market entry. This study focuses on the integration of "technical characteristics - institutional constraints - precise pricing" and constructs a machine learning pricing model optimized across three dimensions: return, risk, and compliance. By integrating Uniswap V4 on-chain data, institutional risk preference data, and market data, a Stacking ensemble architecture combining LightGBM and CNN-LSTM is designed, incorporating 22 core features to achieve precise pricing. Empirical results show that the model's Mean Absolute Error (MAE) on the test set was reduced by 37% compared to the benchmark, and the Root Mean Square Error (RMSE) is reduced by 42%. The Sharpe ratio reaches 1.87 (an increase of 62% compared to the benchmark), with a volatility of 15.3% and a compliance adaptability score of 91. In the case study, a $150 million liquidity supply achieved a 19.7% annualized return and an 8.3% maximum drawdown, successfully passing SEC compliance review. This research fills the gap in institution-oriented pricing models for V4, improves the institutional extension of Automated Market Maker (AMM) pricing theory, and provides a risk-controllable and compliance-adaptable pricing tool for U.S. institutions participating in DeFi, promoting the transformation of the DeFi ecosystem towards standardization and institutionalization. By aligning the V4 Hook mechanism with U.S. regulatory frameworks, this research provides a scalable technical standard for institutional DeFi adoption, reinforcing the competitive advantage of the U.S. Web3 financial ecosystem.
Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph-tabular encoder, with pre-trained weight initialization, and multiple task-specific heads, DeXposure-FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit-exposure forecasting, predicting the joint dynamics of (1) protocol-level flows, and (2) the topology and weights of credit-exposure links. The DeXposure-FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state-of-the-art approaches, including a graph foundation model and temporal graph neural networks. DeXposure-FM further produces financial economics tools that support macroprudential monitoring and scenario-based DeFi stress testing, by enabling protocol-level systemic-importance scores, sector-level spillover and concentration measures via a forecast-then-measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure-FM. Code: https://github.com/EVIEHub/DeXposure-FM.
Emergence of blockchain technology has disrupted a number of economic sectors, particularly financial institutions, with significant effects on their operations. This paper investigates the impact of asset tokenization on the issuance and trading process of financial assets, specifically bonds. It examines the effect of tokenizing the High Yield Bond on the Ethereum blockchain across two key dimensions: On costs, a comparative cost-benefit analysis is conducted before and after tokenization, and on green sustainability, through a comparative analysis on the carbon footprint of the bond before and after Ethereum's merge to proof of stake. The results show that Tokenization improves cost-savings, and it promotes a greener, more sustainable approach when using the Ethereum blockchain post-transition to proof of stake.
We propose Proof of Witness (PoWit), a novel consensus mechanism for digital currency that replaces energy-intensive mining and capital-based staking with independent third-party witness verification. In PoWit, each transaction requires cryptographic signatures from three parties: sender, receiver, and a randomly selected witness. The witness validates the sender’s balance and transaction history before signing, eliminating the need for global consensus while maintaining security guarantees. Our simulation with 10,000 users demonstrates 100% double-spending prevention (n = 10, 000, 99% CI [99.93%, 100%]), 113.9 transactions per second, and complete chain integrity. The non-selective witness assignment achieves theoretical randomness with only 0.27% deviation, making collusion attacks impractical. PoWit offers a sustainable alternative to Proof of Work and Proof of Stake, with significantly lower energy consumption and fairer participation model.
We develop a mathematical framework to optimize leveraged staking ("loopy") strategies in Decentralized Finance (DeFi), in which a staked asset is supplied as collateral, the underlying is borrowed and re-staked, and the loop can be repeated across multiple lending markets. Exploiting the fact that DeFi borrow rates are deterministic functions of pool utilization, we reduce the multi-market problem to a convex allocation over market exposures and obtain closed-form solutions under three interest-rate models: linear, kinked, and adaptive (Morpho's AdaptiveCurveIRM). The framework incorporates market-specific leverage limits, utilization-dependent borrowing costs, and transaction fees. Backtests on the Ethereum and Base blockchains using the largest Morpho wstETH/WETH markets (from January 1 to April 1, 2025) show that rebalanced leveraged positions can reach up to 6.2% APY versus 3.1% for unleveraged staking, with strong dependence on position size and rebalancing frequency. Our results provide a mathematical basis for transparent, automated DeFi portfolio optimization.
Shiyu Zhang, Zining Wang, Jin Zheng, John Cartlidge
Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing decentralized finance (DeFi) ecosystem, numerous studies have analyzed systemic risk through specific channels such as liquidity pressures, leverage mechanisms, smart contract risks, and historical risk events. However, these studies are mostly event-driven or focused on isolated risk channels, paying limited attention to the structural dimension of systemic risk. Overall, this study provides a unified quantitative framework for ecosystem-level analysis and continuous monitoring of systemic risk in DeFi. From a network-based perspective, this paper proposes the DeFi Correlation Fragility Indicator (CFI), constructed from time-varying correlation networks at the protocol category level. The CFI captures ecosystem-wide structural fragility associated with correlation concentration and increasing synchronicity. Furthermore, we define a Risk Contribution Score (RCS) to quantify the marginal contribution of different protocol types to overall systemic risk. By combining the CFI and RCS, the framework enables both the tracking of time-varying systemic risk and identification of structurally important functional modules in risk accumulation and amplification.
Central banks face rising cross-border supervision costs as banking groups operate across fragmented regulatory regimes, making traditional oversight mechanisms ineffective and increasing crisis risk. While a distributed ledger technology (DLT) settlement hub offers unified visibility, it introduces a novel Security Paradox: complex regulatory detection methods increase monitoring costs and collateral requirements, thereby risking the exit of compliant banks and systemic instability. This working paper presents a formal architecture for a multi-currency, permissioned regional DLT settlement hub-designed to address the complexities and inefficiencies of cross-border supervision and settlement-that maintains monetary sovereignty across jurisdictions. By adopting a "slashing reserve" model, in which coordinating central banks jointly set monitoring, penalties, transparency, and collateral, regional hub security is decoupled from collateral through efficient detection and a Byzantine-fault-tolerant quorum structure. This design enables capital-efficient, Basel-consistent settlement, ensuring stability and sovereignty, even during crises, and provides a blueprint for modern cross-border financial infrastructure.
This study examines whether Bitcoin-collateralised lending can operate as a form of risk-disciplined leverage within decentralised finance (DeFi). A stylised framework is developed to characterise how over-collateralisation, automated liquidation rules, and interest-rate formation determine balance-sheet risk and portfolio efficiency. Particular attention is given to loan-to-value (LTV) constraints, custody structures, and liquidity buffers in identifying the conditions under which collateralised Bitcoin borrowing improves capital allocation without generating destabilising leverage cycles. The findings indicate that conservative collateralisation combined with active liquidity management mitigates insolvency risk even under high asset volatility. The analysis provides a formal characterisation of leverage constraints in decentralised lending and extends the literature on risk allocation and capital structure in digital asset markets.
Richard Cantillon (1680s-1734), an Irish-French economist and early pioneer of political economy, observed that those closest to new money creation gain purchasing power before prices adjust throughout the economy. Bitcoin miners occupy precisely this position as the exclusive first receivers of every newly minted bitcoin. Yet unlike banks in fiat systems, miners cannot retain this advantage indefinitely because the protocol subjects them to relentless competition. This paper proposes the Lazy Miner Hypothesis: when mining profitability deteriorates following halving-induced supply shocks, inefficient operators exit first, generating a predictable sequence of revenue compression, hash rate decline, and subsequent price recovery that redistributes first-receiver gains from weak miners to patient investors. Using daily data from September 2014 to January 2026, a miner stress indicator combining depressed revenue with declining computational commitment predicts 90-day forward returns of 36.5 percentage points after controlling for Federal Reserve policy and energy costs. The coefficient is virtually unchanged when WTI crude oil volatility is added, confirming a protocol-native effect. Horse race regressions show miner stress dominates technical oversold indicators. Placebo tests with randomized halving schedules produce no comparable effects, and forward Sharpe ratios confirm genuine alpha. The premium declines by 12.5 percentage points per halving epoch, consistent with market learning. The approval of U.S. spot Bitcoin ETFs in January 2024 significantly diminishes the effect, yet the miner stress signal remains positive and statistically significant, indicating that institutional absorption is underway but incomplete. Bitcoin's competitive mining structure thus transforms Cantillon dynamics from permanent insider advantages into temporary, efficiency-driven rewards that erode as markets mature.
We develop an agent-based model in which inflation emerges from decentralized price-setting and credit-financed production in an endogenous-money economy. Firms operate under working-capital constraints, form market-based price expectations through heterogeneous adaptive learning, and set prices via cost-plus rules with endogenous mark-ups. Bank lending simultaneously creates deposits, while heterogeneous lending rates and credit rationing shape firms' financing costs and, through unit costs, their pricing decisions. The economy features interacting production and credit networks: intermediate-input linkages propagate cost shocks across supply chains, while bank--firm relationships transmit financial conditions across firms. The interaction of network-based pass-through, state-dependent pricing incentives, and evolving credit conditions generates inflationary regimes, including episodes driven by pricing cascades and feedback loops.
This written testimony was submitted to the Financial Services Regulation Committee of the United Kingdom House of Lords, in response to that Committee’s “Call for evidence” on the “Growth and proposed regulation of stablecoins in the UK,” https://committees.parliament.uk/call-for-evidence/3845/. This testimony provides an overview of the global stablecoin market and the current leading uses of stablecoins. The testimony also describes the unacceptable dangers that uninsured nonbank stablecoins pose to financial stability, economic welfare, consumer protection, monetary policy, regulatory compliance, and law enforcement. The testimony presents the following policy recommendations: (1) Stablecoins should be regulated in the same way as bank deposits. Only regulated banks should be allowed to issue or distribute stablecoins. Stablecoins should be required to satisfy the same prudential standards and provide the same consumer safeguards – including deposit insurance – as bank deposits. 2) Stablecoins should be issued and recorded exclusively on permissioned distributed ledgers that are controlled and administered by one or more designated banks. The designated banks should have full responsibility and accountability for ensuring that their stablecoins and their distributed ledgers fulfill all legal and contractual obligations. 3) To ensure compliance with AML/BSA/KYC requirements, stablecoin holders should be prohibited from holding their stablecoins in “unhosted” private digital wallets. (4) Stablecoin reserves should be invested solely in central bank reserves or in government securities with a weighted average maturity of 20 days or less. (5) If – contrary to the foregoing recommendations – nonbanks are allowed to issue stablecoins, those issuers, crypto exchanges, other crypto trading platforms, and their affiliates and business partners should be prohibited from paying interest, rewards, or any other financial inducements to stablecoin holders for owning stablecoins or keeping their stablecoins at designated locations. The author also presented oral testimony (via Zoom) to the Committee, available at https://committees.parliament.uk/event/26299/formal-meeting-oral-evidence-session/.
This paper investigates whether variables associated with the leading decentralized finance (DeFi) lending protocol Aave exert measurable effects on the volatilities of Ethereum (ETH) and Bitcoin (BTC), as well as on their dynamic correlations with the S&P 500. We construct a dataset spanning January 2021 to December 2025, covering both Aave V2 and V3, from which we derive explanatory variables including utilization ratios, supply and borrowing rates and measures of pool-level lending risk. Principal component analysis (PCA) is applied to address dimensionality and multicollinearity, yielding two economically interpretable signals: a crypto inflow component capturing risk appetite, and a stablecoin inflow component consistent with flight-to-safety behavior. Cryptocurrency volatilities are computed using the Garman–Klass (GK) range-based estimator while dynamic cross-asset correlations are recovered via the Dynamic Conditional Correlation (DCC) framework. Granger causality tests and ARDL regression models reveal that the Aave risk appetite signal predicts changes in ETH GK volatility at lags 1 and 5 and dynamic correlations at all lags, with weaker effects on BTC volatility. Secondly, we observe a robust negative cumulative effect on all three dynamic correlations, indicating that expansions in DeFi activity systematically dampen crypto–equity co-movements and also lead to a decoupling of BTC and ETH. These results are robust to different volatility specifications and suggest that on-chain DeFi data contains forward-looking information and can help reduce parameter estimation risk, a critical issue in deriving optimal portfolios involving digital assets. We expect that DeFi protocol-level signals will become a standard component of empirical work on digital-asset pricing and risk.
Decentralized finance (DeFi) is often defended as software rather than regulated intermediation. We examine whether functional control over DeFi applications can be measured directly by tracking address-level concentration in the channels through which sophisticated actors capture rents: governance over risk parameters, liquidations, lending flows, supplier spreads, MEV, and routing. From prior work on AMMs, MEV, lending, and DAO governance, we derive three predictions about how concentration should vary across channels, protocols, and applications. We test the predictions using six data sources: 1,142 risk-tagged Snapshot proposals across 15 governance spaces, $2.05 billion in liquidations across five lending markets, $569.4 billion in actor-level lending flows, DefiLlama rent series, a Uniswap v3 LP sample, and 250 Aave forum risk topics. The evidence supports all three predictions. Discretionary channels concentrate sharply but with protocol-level heterogeneity: the median top-five voting-power share across risk proposals is 96.0 percent, with Aave at 91.2 percent, Uniswap at 84.3 percent, and Radiant at 57.7 percent. Lending markets concentrate more than exchanges in governance, and Compound V3’s top liquidator captures 55.8 percent of volume while Aave V3 has 868 active liquidators. Within lending markets, the deposit base is broad while borrowing is narrow: the Aave V3 top-five borrow share is 84.8 percent against a 17.8 percent deposit share. We treat the evidence as channel-specific screening inputs rather than entity-level control findings, and discuss disclosure, registration, and safe-harbor implications.
The choice-of-law solutions governing the proprietary aspects of bearer financial securities were long marked by great simplicity. When securities were embodied in a paper instrument, applying the law of the place where that paper instrument was located gave the conflict of laws a foreseeable and internationally uniform solution. The dematerialisation of these securities and the advent of distributed ledger technology have rendered that solution obsolete, while the new connecting factors based on the location of the account-keeping intermediary afford no real satisfaction. This article takes stock of these connecting factors and proposes another : that of the securities delivery system operated by the central securities depository.