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.
ABSTRACT This study provides a comprehensive systematic review and bibliometric analysis of 125 peer‐reviewed articles on bid‐ask spread estimators published between 1987 and 2025. Using the PRISMA framework, we map the intellectual evolution of the field, identifying a significant shift from foundational parametric models to data‐driven approaches. While early research focused on simple covariance‐based metrics, the field has recently been transformed by significant technical advances. Our network analysis identifies five major thematic clusters ranging from market dynamics and liquidity definitions to microstructure in high‐frequency and volatile environments. We highlight a critical research priority: utilizing high‐frequency data to validate low‐frequency models for reliable application in unobserved contexts, such as emerging markets and decentralized finance (DeFi). The findings underscore the enduring relevance of estimators in construction of long‐span historical series and noise‐adjusted liquidity measures. Future research must bridge existing methodological silos by integrating behavioral finance perspectives and advancing real‐time analytics for fragmented, high volatile global markets.
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.
The rapid growth of stablecoins has introduced novel forms of systemic risk to the global financial system, fundamentally challenging traditional notions of financial stability. This perspective paper examines the conditions under which stablecoins may become “too big to fail” and analyzes the unique risks posed by algorithmic and decentralized autonomous organization (DAO)-based models. Through comprehensive examination of the Terra Luna/TerraUSD (UST) collapse, Silicon Valley Bank's impact on USDC, and other significant stablecoin failures, we identify critical thresholds for systemic importance and propose an enhanced framework for assessing systemic risk in digital currency ecosystems. Our analysis reveals that traditional metrics of systemic importance inadequately capture the interconnectedness, velocity-driven risks, and reflexive mechanisms inherent in algorithmic stablecoin systems .
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.
In recent years, digital currencies have dominated headlines due to the fast growth in its nominal value. While it remains unclear whether mass adoption of digital currencies by consumers and businesses will occur, the increasing investments by households into cryptocurrencies as risky asset warrants research into the impact of digital currencies. Additionally, digital currencies have a significant impact on money supply and fiat currencies, banking and payments systems, and consequently monetary policy transmission. In recent years, banks have also started leveraging digital assets for crucial functions such as liquidity operations. Additionally, major central banks around the world have started projects to research into Central Bank Digital Currencies (CBDC), as a potential replacement for fiat currencies. This matters for small-open economies such as Singapore, which are highly dependent on open capital flows and trade to grow the economy. This paper, utilising a Structural VAR model, examines the impact of cryptocurrencies on the business cycle in Singapore and whether the adoption of digital currencies into financial markets in past years and in the years to come, could have an impact on the business cycle and monetary policy transmission. Our results show that cryptocurrency has some impact on output when compared to our counterfactual model; these results remain robust even when re-ordered.
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.
The global private credit market has grown to nearly $2 trillion in assets under management, drawing capital from pension funds, endowments, and retail investors seeking yields unavailable in traditional fixed income markets. The first quarter of 2026 marked the sector's first serious stress test since the post-2008 expansion, as redemption waves swept through major private credit vehicles and JPMorgan Chase initiated targeted valuation markdowns on software loans it deemed vulnerable to artificial intelligence disruption. This paper examines those events and their implications for tokenized private credit, a parallel market in which blockchain-based protocols offer on-chain exposure to similar underlying loans. Using Maple Finance's syrupUSDC yield token and SYRUP governance token as primary case studies, the analysis evaluates how credit deterioration in traditional lending markets transmits to decentralized finance platforms. The paper argues that tokenization improves transparency but does not alter the fundamental economics of private lending: borrower default risk, liquidity mismatches, leverage amplification, and sector concentration follow the asset class regardless of its technological wrapper. For policymakers, the emergence of tokenized real-world assets represents a new channel through which credit cycle stress can reach a broader and less sophisticated investor base.
The global private credit market has grown to nearly $2 trillion in assets under management, drawing capital from pension funds, endowments, and retail investors seeking yields unavailable in traditional fixed income markets. The first quarter of 2026 marked the sector's first serious stress test since the post-2008 expansion, as redemption waves swept through major private credit vehicles and JPMorgan Chase initiated targeted valuation markdowns on software loans it deemed vulnerable to artificial intelligence disruption. This paper examines those events and their implications for tokenized private credit, a parallel market in which blockchain-based protocols offer on-chain exposure to similar underlying loans. Using Maple Finance's syrupUSDC yield token and SYRUP governance token as primary case studies, the analysis evaluates how credit deterioration in traditional lending markets transmits to decentralized finance platforms. The paper argues that tokenization improves transparency but does not alter the fundamental economics of private lending: borrower default risk, liquidity mismatches, leverage amplification, and sector concentration follow the asset class regardless of its technological wrapper. For policymakers, the emergence of tokenized real-world assets represents a new channel through which credit cycle stress can reach a broader and less sophisticated investor base.
Decentralized Finance (DeFi) has rapidly emerged as one of the most transformative applications of blockchain technology, enabling permissionless financial services including lending, borrowing, trading, and yield optimization through smart contract-based protocols. The complex interactions among users, protocols, and assets in the DeFi ecosystem naturally form intricate graph structures whose topology encodes critical information about system behavior, risk propagation, and economic vulnerabilities. This paper presents a comprehensive survey of graph-based analytical methods applied to the DeFi ecosystem. We systematically review transaction graphs, protocol dependency graphs, liquidity flow networks, and user interaction graphs, examining how graph-theoretic techniques-including community detection, centrality analysis, temporal graph mining, and anomaly detection-have been applied to understand DeFi dynamics. We further analyze how graph methods reveal systemic risks such as composability-induced contagion, flash loan attack patterns, and liquidity concentration. We propose a unified DeFi Graph Analysis Taxonomy (DGAT) that classifies existing work across graph construction methodologies, analytical objectives, and application domains. Comparative evaluation of current approaches identifies key research gaps and outlines directions for future investigation, particularly in cross-protocol risk modeling and real-time graph analytics for DeFi monitoring.
Financial institutions today are embedded in a multiplex network of interconnected obligations spanning interbank lending, sovereign bond exposures, and decentralized finance liquidity pools. Traditional systemic risk metrics treat each channel independently, ignoring the cross-layer feedback mechanisms through which shocks amplify during crises. We introduce the Multiplex Interdependence Centrality framework, a spectral measure that computes the principal eigenvector of a weighted supra-adjacency matrix coupling multiple financial layers. The multiplex interdependence centrality score captures a node's systemic importance jointly across all layers, accounting for both intra-layer exposure weights and inter-layer coupling intensities. We couple this centrality measure with a threshold-based cascade simulation to validate its predictive power. Using a synthetic three-layer financial network of 100 nodes representing interbank lending, sovereign bonds, and DeFi markets, we demonstrate that MIC achieves a Pearson correlation of r = 0.806 with actual cascade damage that substantially outperforms the centrality of the eigenvector of the single-layer, PageRank, and the centrality of the differences. Our results provide a rigorous quantitative foundation for integrating multiplex network metrics into institutional risk monitoring, central bank stress-testing frameworks, and regulatory oversight of cross-sector financial contagion.
The prohibition of interest-bearing stablecoins marks a critical juncture in digital asset regulation, exposing fundamental tensions between financial innovation and systemic stability. This paper provides the first comparative legal and financial analysis of the European Union's Markets in Crypto-Assets Regulation (MiCA) and the United States' GENIUS Act of 2025, examining how two major jurisdictions reach convergent prohibitions on yield-bearing stablecoins through markedly divergent regulatory architectures. While both frameworks forbid direct interest payments to holders, they diverge sharply in their treatment of decentralized finance (DeFi) protocols, reserve composition requirements, and supervisory allocation. The paper advances three interconnected contributions: (1) a technical examination of reserve management mechanisms and their implications for run dynamics; (2) a scenario-based stress test identifying contagion pathways from stablecoin markets to traditional banking systems; and (3) an analysis of the "DeFi gap," the substantial regulatory perimeter failure whereby third-party protocols offer functionally equivalent yields outside statutory prohibitions. With stablecoin market capitalization exceeding $300 billion, rivaling the deposit bases of mid-sized national banking systems-the regulatory treatment of these instruments will determine whether they evolve as competitors to traditional banks or as complementary infrastructure within a restructured financial system.