We develop optimal transport stress testing, liquidation cost modeling, and fund-level capital allocation for lending against prediction market collateral. Building on a companion paper that derives first-passage default probabilities under Hawkes-driven jump-discussion dynamics, this paper addresses three challenges that arise when operating the lending protocol at scale. First, we introduce a Wasserstein stress testing methodology that generates synthetic tail scenarios for markets with insufficient historical depth, proving that it achieves strictly higher effective sample sizes than classical Entropy Pooling when the stress region lies outside the empirical support. We further establish an adversarial robustness guarantee: the stressed risk estimate remains bounded even under worst-case perturbations of the empirical distribution within a Wasserstein ball- a formal resilience property that no existing decentralized finance stress testing methodology provides.
Decentralized finance and stablecoin systems rely Stablecoins increasingly incorporate freeze, pause, and blacklist mechanisms to satisfy regulatory, compliance, and risk-management requirements. However, these controls introduce a critical temporal vulnerability when enforcement actions compete with transaction finality. This paper defines <b><i>Stablecoin Freeze Race Conditions</i></b> as a class of failures in which transfers, redemptions, or collateral movements execute successfully during the latency window between risk detection and freeze enforcement. We analyze how asynchronous control paths enable value escape even in fully permissioned stablecoins and demonstrate why governance authority alone is insufficient. A validator-level, logic-layer enforcement model is proposed to ensure atomicity between risk triggers and monetary state transitions under MiCA-aligned frameworks.
Decentralized Finance (DeFi) lending and borrowing protocols enable investors to take leveraged long and short positions on digital assets without centralized intermediaries, but expose them to a distinctive form of risk: on-chain liquidation triggered by debt and collateral value fluctuations. In this work, we provide a detailed formalization of Aave's lending, borrowing, and liquidation mechanisms, grounded in the protocol's open-source implementation. In doing so, we propose a mathematical modeling of the risk of liquidation, including some stochastic approximations with the purpose of efficient analysis, with different applications. Among them, portfolio optimization problem.
We introduce a model-free structural framework to value liabilities of firms whose primary assets are digital assets typically Bitcoin. Our no-arbitrage approach prices convertible debts and extracts the risk-neutral probabilities of their terminal states using the market information of Bitcoin options, thereby bypassing the restrictive assumptions of traditional structural models. We perform comparative statics analysis to demonstrate how the resulting bond spreads and option values are structurally determined. We then test the framework in a real-world case study of MicroStrategy's convertible bonds, finding that it generates accurate, market-consistent valuations in an out-of-sample setting. Together, our theoretical and empirical results establish a robust, market-based blueprint for pricing the emerging class of crypto-backed credit in general.
Sai Srikanth Madugula, jose Luis de la Rosa Esteva, Daya Shankar
This paper presents an integrated framework for decentralized invoice-backed loan underwriting combining interpretable machine learning, dynamic pricing algorithms, and on-chain trust infrastructure. We develop and validate SHAP-explainable ML models for real-time default probability assessment, design a Reverse Kelly AMM smart contract for optimal risk-adjusted loan pricing, integrate ERC-725 identity and on-chain reputation scoring with an automated insurance reserve, and deploy the system on Ethereum testnet with end-to-end functional and security testing. Stress testing across simulated default and fraud scenarios demonstrates the model achieves AUC-ROC of 0.89 on validation data, maintains LP yields of 12–18% under normal conditions while containing non-performing loan ratios below 3% under adverse scenarios, and sustains reserve solvency across 95th percentile stress events. The framework addresses critical gaps in DeFi lending by bridging regulatory interpretability requirements with decentralized credit assessment, demonstrating both technical feasibility and economic viability for permissionless SME financing at scale.
This study investigates the impact of cryptocurrency implied volatility and the CBOE VIX term structure on Bitcoin returns from March 2021 to May 2025. Using PCA and an orthogonalization framework, we identify the VIX term structure’s slope factor as a primary determinant of contemporaneous Bitcoin returns. While Bitcoin shows strong negative responses to VIX and crypto-implied volatility across all maturities, the VIX slope factor exhibits superior explanatory power. Notably, following the January 2024 U.S. spot Bitcoin ETF approval, Bitcoin's sensitivity to its own implied volatility significantly attenuated, while its responsiveness to the VIX remained unchanged. A placebo test confirms this structural shift, suggesting that ETF institutionalization has altered Bitcoin’s internal risk dynamics without decoupling it from broader equity market volatility.
This paper proposes a structured decentralized finance (DeFi) strategy designed to accumulate Ethereum (ETH) over time while exploiting market volatility through liquidity provision and controlled directional exposure. The framework combines concentrated liquidity provisioning on Uniswap v3 with a hedge position using low-leverage directional exposure and a reserve of stablecoins for counter-cyclical accumulation during market drawdowns. The strategy is implemented on Layer-2 networks-specifically Arbitrum and Base-to reduce transaction costs and capture diversified trading flows. We present a formal mathematical treatment of impermanent loss under concentrated liquidity, Monte Carlo simulations of ETH price paths under three market regimes, and an optimization framework for liquidity range selection. The proposed system transforms three distinct market conditions-sideways volatility, bullish breakouts, and market downturns-into opportunities for yield generation, directional gains, or asset accumulation. Results indicate that the hedged strategy achieves a superior risk-adjusted profile relative to unhedged liquidity provision across all tested volatility regimes.
Ioannis Tzannetos, Danai Balla, Aris Pagourtzis, Vassilios Vescoukis
Non-fungible tokens (NFTs) have created vibrant digital marketplaces where unique assets are exchanged across domains such as art, gaming, and music. While current infrastructures are optimized for pairwise, currency-backed trades, they provide limited support for multi-party swaps of indivisible assets based on user preferences. In practice, liquidity is not always desirable—participants may wish to exchange directly for assets they deem equally valuable, bypassing auctions or currency markets. In this paper, we propose BarterSwap, a protocol to address this gap by leveraging the Top Trading Cycles (TTC) algorithm to enable efficient multi-party NFT exchanges on Ethereum. Our protocol identifies preference-based dependencies among users and executes swaps without requiring external liquidity. We implement and deploy our solution on the Ethereum blockchain, demonstrating that it remains practical for a reasonably large number of participants. Finally, we release our implementation publicly and provide a detailed cost analysis, offering a concrete path toward fair and efficient preference-based NFT exchanges.
Decentralized finance has built fixed-rate and yield-bearing instruments, but not a fixed-income architecture in which duration and convexity arise endogenously from continuously updated collateral-policy logic. Every existing protocol that expresses rate sensitivity either imports it from a traditional financial asset, derives it from an automated market maker price curve, or constructs it as a synthetic derivative position. None generate rate sensitivity endogenously from on-chain collateral architecture. This paper shows that a deterministic collateral release schedule defined on a state-aware policy surface is sufficient to endow an on-chain debt instrument with computable modified duration and asymmetric rate sensitivity by construction. We introduce the Amortizing Collateral Bond (ACB), a crypto-native debt instrument whose financial characteristics emerge from collateral policy architecture rather than from any imported traditional finance instrument. The ACB's modified duration is derived analytically from its release schedule and a protocol-implied discount rate. Asymmetric rate sensitivity—the property that the instrument loses more from rate rises than it gains from rate declines, analogous to the convexity profile of a mortgage-backed security holder—arises from two state-machine-enforced mechanisms: a prepayment option that compresses price appreciation when rates fall, and regime-dependent release schedules that extend duration when rates rise. Neither requires a counterparty or clearing house to enforce the option schedule. We further introduce the Prepayable Vault with Embedded Callable Option, which makes the convexity compression explicit and parameterizable, and Duration-Tranched Vault Certificates, which generalize the structure to multi-tranche pools tranched by rate sensitivity rather than credit quality. In Stage 2, we generalize the discount rate from a protocol-implied single rate to a composite on-chain rate index constructed from observable lending, staking, and funding markets—enabling multi-maturity duration computation and an empirical low-correlation claim against the Treasury curve. We show that this composite index admits a term structure adequate for duration analysis across maturities, and argue that its structural drivers are distinct from those of the Treasury yield curve—not as a portfolio-level diversification claim, but as a property of the rate surface itself. The empirical correlation between the two surfaces is low over the 2022–2024 period; we are explicit that this independence is structural rather than permanent, and that it degrades as institutional capital integrates the two surfaces. We engage directly with the market-readiness constraints—rate surface liquidity, hedging ecosystem development, and the adoption sequencing problem—and frame the contribution honestly as a proof of existence for crypto-native fixed income rather than a complete market design. The paper further introduces the Collateralized Amortizing Obligation (CAO)—a crypto-native structured vehicle with duration-stratified Senior, Mezzanine, and Equity tranches enforced by a deterministic waterfall. The CAO is not a tokenized collateralized mortgage obligation (CMO) or collateralized loan obligation (CLO): its collateral is entirely on-chain, its tranche duration profiles are computable from the policy surface architecture, and its return drivers reference a rate surface with structurally distinct drivers from traditional finance (TradFi) rate markets. Full issuance mechanics, atomic settlement, and the Convexity Swap as the Equity tranche hedging instrument are developed in Paper III. The architecture developed in Paper I is the necessary prerequisite. A well-defined, continuously updated policy surface is the precondition for any of these instruments to be constructible. What follows shows what becomes possible once that precondition is met.
We formulate and solve stochastic control problems that model the core yield-generating strategy of the Ethena protocol, a decentralized finance (DeFi) stablecoin that earns yield by combining a long position in staked Ethereum (stETH) with an equal-sized short position in ETH perpetual futures. The combined position is delta-neutral with respect to the ETH spot price, yet earns carry from two sources: staking rewards on the stETH leg, and funding-rate payments received from long perpetual holders when the perpetual trades at a premium to spot. A key feature of our model is that the control -- the rate of simultaneously buying stETH and shorting the perpetual -- exerts two distinct types of price impact. \textit{Permanent} impact shifts the mid-market prices of both legs, compressing the basis and permanently eroding future funding income. \textit{Temporary} impact reflects execution slippage on each leg. We study both an infinite-horizon discounted problem and a finite-horizon problem in which the protocol maximizes total wealth up to a fixed date $T$, subject to a terminal cost for liquidating any remaining position. In both cases the optimal control is obtained explicitly.
Existing decentralization measures are almost entirely origination-side, quantifying concentration in who mines or validates blocks. We introduce a spectral methodology measuring concentration on the destination side instead: where value ultimately flows once it leaves a validator wallet. Modeling wallet-to-wallet transfers as a Markov chain, we compute near-real-time steady-state probabilities via the Perron-Frobenius theorem to identify long-run terminal recipients. Applied to 76,855 Ethereum wallets from four years of mining data, fund flows collapse to just four terminal accounts. None of these fund destination accounts are among the network's three dominant identifiable revenue-earning miners.
In traditional banking, repeated deposit-and-lend cycles let a single dollar of reserves support multiple dollars of claims. Decentralized finance produces an analogous structure with tokens. Constructing a Token Graph of 10,200 tokens across 200 blockchains, this paper maps the resulting hierarchy and shows that, by late 2025, each dollar of base assets supports $4.7 of total claims. An embedded yield correction disentangles two channels that raw data conflates: a compositional channel, where lending protocols concentrate in deeper tiers and mechanically raise average yields; and a liquidity channel, where each derivation step reduces secondary-market depth and depresses yields in liquidity-sensitive pools. The liquidity channel concentrates in DEX pools and vanishes in lending pools. A yield decomposition shows that the tier gradient operates entirely through fundamental protocol yields, not incentive-token emissions; quantile regressions reveal that the structural associations concentrate in the upper tail of the yield distribution, with near-zero effects at the median. These findings reframe DeFi's "double counting" as a structural risk question and identify liquidity fragmentation as the primary mechanism associated with yield variation across the token hierarchy.
Decentralized Finance (DeFi) enables financial services to operate without centralized intermediaries, using smart contracts and blockchain consensus to ensure transparency and trust minimization. While DeFi protocols like Aave and MakerDAO use overcollateralization to mitigate credit risk, this approach creates capital inefficiencies and limits access to borrowers lacking on-chain assets. This paper introduces Inverum, a novel DeFi lending protocol designed to support undercollateralized loans for Web3 businesses and Decentralized Autonomous Organizations (DAOs). Inverum integrates on-chain credit scoring via soulbound tokens, decentralized liquidity pools, and governance-driven incentives to enable trustless, reputation-based lending. The protocol offers a fully composable framework for exploring undercollateralized lending without relying on traditional identity or off-chain reputation systems, contributing a research-ready model for future experimentation and protocol design.
Luca Pennella, Pietro Saggese, Fabio Pinelli, Letterio Galletta
Decentralized Finance (DeFi) applications introduce novel financial instruments replicating and extending traditional ones through blockchain-based smart contracts. Among these applications, DeFi derivatives protocols enable the creation and trading of decentralized derivative instruments whose value depends on underlying cryptoassets, indices, or other reference variables. Despite their growing significance, however, they remain relatively understudied compared to other DeFi protocols, such as lending protocols and decentralized exchanges. This paper systematically analyzes DeFi derivatives protocols, categorized into perpetuals, options, and synthetics, with the aim of comparing their instrument structures, protocol mechanisms, operational dynamics, and economic agents. We provide a formal characterization of the main classes of decentralized derivative instruments and develop a protocol-agnostic framework that connects instrument-level specifications, market-state variables, and protocol-level mechanisms. We complement the analytical framework with numerical simulations that evaluate how derivative positions evolve under varying economic conditions, including changes in underlying asset prices, volatility, protocol-specific fees, and leverage. Overall, this study provides a structured analytical framework for understanding and comparing the design and functioning of decentralized finance derivatives protocols.
The traditional credit scoring industry, dominated by a few centralized bureaus, suffers from opacity, data insecurity, and a lack of user-controlled data sovereignty. This paper introduces Zk-Cred, a novel decentralized protocol designed to address these challenges by leveraging a unique combination of Fully Homomorphic Encryption (FHE), Zero-Knowledge Proofs (ZKPs), and W3C Verifiable Credentials (VCs). Zk-Cred empowers individuals to generate a verifiable, privacy-preserving credit score without revealing their underlying financial data to any third party. The protocol’s core mechanism involves users encrypting their financial data client-side using an FHE scheme. A decentralized network of nodes then executes a publicly auditable credit scoring model on this encrypted data, computing a score that is only ever decrypted by the user. The user can then generate a ZKP to prove the correctness of the computation and receive a tamper-proof VC representing their creditworthiness. This VC can be presented to financial service providers, such as DeFi lending platforms or traditional institutions, for instant verification. By synthesizing these cryptographic primitives, Zk-Cred offers a new paradigm for credit scoring that is transparent, secure, and user-centric, with significant potential to enhance fairness and access in the global fintech ecosystem.
Total Value Locked (TVL) explicitly reflects the total asset users deposit in Decentralized Finance (DeFi) protocols, similarly to the Asset Under Management (AUM) in traditional finance. This exposes liquidity providers to the risk of short-term liquidity depletion and highlight the urgent need for quantifiable and predictive risk management tools. As its short-term fluctuations can be effectively characterized by on-chain static features (e.g., fee tier, volatility), dynamic features (e.g., token balance changes, slippage), and technical indicators (e.g., MA), and since posterior calibration methods based on high-accuracy point forecasting models can provide more reliable estimations of downside risk boundaries, this study focuses on the USD Coin - Ethereum pool (0.3 % fee tier) of Uniswap V3. The model is constructed using 17 features, with eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine employed for TVL growth-rate regression forecasting. The results are compared with those from Long Short-Term Memory, Gated Recurrent Unit, and Naïve baselines. Furthermore, the research introduces the Liquidity-at-Risk (LaR95) metric to estimate downside risk through both residual-based and quantile regression approaches, and conduct interpretability analysis using SHAP values. XGBoost obviously outperforms Deep learning models on directional accuracy. XGBoost demonstrates a significantly superior performance to deep learning models in predicting the direction of TVL changes. The residual-based LaR95(liquidity-at-risk at the 95% confidence level) derived from its point forecasts exhibits a coverage rate closely aligned with the theoretical level, validating the effectiveness, robustness, and interpretability of the “high-accuracy prediction and residual calibration” framework in DeFi risk management.
MakerDAO is a decentralized lending protocol providing crypto-backed loans with no intermediaries, backed by volatile assets such as Ethereum (ETH). Loans are liquidated if the value of the collateral dips below a threshold. DeFi compared to traditional finance does not have standardized risk models and default is difficult to model. Earlier models such as Poisson Process and Brownian Motion have the unrealistic premise of constant volatility, which makes them less useful in rapidly fluctuating crypto markets. This paper introduces a Geometric Brownian Motion (GBM) model with rolling volatility to capture real-time market dynamics. The model learns to adapt to prevailing price trends by estimating volatility with a rolling window and enhances the accuracy of default risk estimation. Results indicate that rolling volatility increases the predictive ability of GBM, providing a robust solution to credit risk management in DeFi platforms. The GBM with rolling volatility has 0.006 root mean square error and 0.008 mean absolute error.
S Chowdhury, Md Hadiur Rahman Hamim, S Joy, Md. Shakil Ahmed · 7 authors
Credit allowances for individuals who are seeking financial opportunities are vital for their growth and sustainability. Traditional credit scoring systems don’t allow them to get personal loans, as individuals lack collateral or have a limited financial history. This paper approaches a novel solution that makes use of blockchain technology to overcome these issues and improve individuals’ access to financing. The main benefits of blockchain technology are transparency, security, and decentralization, which have the potential to completely transform the credit rating system. We aim to develop a decentralized credit scoring system that incorporates a wide range of parameters by combining international standard scoring systems like FICO and EQUIFAX to make it more efficient. Moreover, the decentralized credit scoring system ensures data integrity and security, reducing the risk of fraud and manipulation in the credit scoring process.
ABSTRACT This study investigates how overconfident cryptocurrency traders influence the connection between returns and risk premia, proxied by option‐adjusted credit spreads. Using daily data from January 2021 to February 2025, we uncover asymmetry and state dependence: returns decline when spreads widen, particularly during crashes, yet they do not recover when spreads narrow. Equity indices exhibit more balanced co‐movements. The asymmetry strengthens in high‐volatility periods and persists after we control for broad market returns and after we substitute a composite crypto index for individual cryptocurrencies. These findings indicate a distinctive pricing mechanism in cryptocurrency markets shaped by overconfident behaviour and credit‐spread dynamics.