Cross-chain bridges constitute the single largest vector of systemic risk in Decentralized Finance (DeFi), accounting for over \$2.8 billion in losses since 2021. The fundamental vulnerability lies in the binary nature of existing bridge security models: a bridge is either fully operational or catastrophically compromised, with no intermediate state to contain partial failures. We present ASAS-BridgeAMM, a bridge-coupled automated market maker that introduces Contained Degradation: a formally specified operational state where the system gracefully degrades functionality in response to adversarial signals. By treating cross-chain message latency as a quantifiable execution risk, the protocol dynamically adjusts collateral haircuts, slippage bounds, and withdrawal limits. Across 18 months of historical replay on Ethereum and two auxiliary chains, ASAS-BridgeAMM reduces worst-case bridge-induced insolvency by 73% relative to baseline mint-and-burn architectures, while preserving 104.5% of transaction volume during stress periods. In rigorous adversarial simulations involving delayed finality, oracle manipulation, and liquidity griefing, the protocol maintains solvency with probability $>0.9999$ and bounds per-epoch bad debt to $<0.2%$ of total collateral. We provide a reference implementation in Solidity and formally prove safety (bounded debt), liveness (settlement completion), and manipulation resistance under a Byzantine relayer model.
Three traits of decentralized finance are studied. First, the market impact function is derived for optimal-growth liquidity providers. For a standard random walk, the classic square-root impact is recovered. An extension is then derived to fit general fractional Ornstein-Uhlenbeck processes. These findings break with the linearized liquidity models used in most decentralized exchanges. Second, a Constant Product Market Maker is viewed as a multi-phase Carnot engine, where one phase matches the exchange of tokens by a liquidity taker, and another the change of pool size by a liquidity provider. Third, stablecoin de-pegging is a form of catastrophe risk. By using growth optimization, default odds are linked to the cost of catastrophe bonds. De-pegging insurance can act as a counterweight and a key marketing tool when the law forbids the payment of interest on stablecoins.
Aleksei Adadurov, S. Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors
This paper examines the impact of reducing Ethereum slot time on decentralized exchange activity, with a focus on CEX-DEX arbitrage behavior. We develop a trading model where the agent's DEX transaction is not guaranteed to land, and the agent explicitly accounts for this execution risk when deciding whether to pursue arbitrage opportunities. We compare agent behavior under Ethereum's default 12-second slot time environment with a faster regime that offers 1-second subslot execution. The simulations, calibrated to Binance and Uniswap v3 data from July to September 2025, show that faster slot times increase arbitrage transaction count by 535% and trading volume by 203% on average. The increase in CEX-DEX arbitrage activity under 1-second subslots is driven by the reduction in variance of both successful and failed trade outcomes, increasing the risk-adjusted returns and making CEX-DEX arbitrage more appealing.
We introduce the first fully operational Islamic Credit Default Swap (iCDS) on public blockchain infrastructure, combining a closed-form riba-free pricing formula with a deployed smart contract on Arbitrum. Building on the κ-rate framework of Ackerer, Hugonnier & Jermann [1] and the credit-equivalence theorem of Ahmed, Bhuyan & Islam [3], the fair iCDS spread is s * = κ(1-δ) at ι = 0, where κ is the convergence intensity and δ is the recovery rate. Conventional CDS, discounted at the risk-free rate ι, prices at s conv = κ(1-δ) • ι/(κ+ι)
Michael Neubert, Wolfgang Rams, Patrick Gruhn, Marcel Lötscher
Perpetual futures (often called perpetual swaps) are the dominant crypto-derivatives instrument. They replicate the economic exposure of a futures contract without an expiry date. They replace maturity-based convergence with a funding mechanism that transfers cash flows between longs and shorts, typically every eight hours. This paper explains how perpetuals evolved from early proposals for non-maturing futures into a standardized crypto market instrument, and why key design choices changed over time. It synthesizes recent theoretical and empirical research on funding design, pricing, and arbitrage intuition, market microstructure, liquidation risk, and regulation. Finally, this study proposes a research agenda organized around funding design, constrained arbitrage, transparency, decentralized exchange design, policy, and legal classification, because recent U.S. and EU developments show that the same economic structure may be characterized as a futures contract, swap, CFD-type instrument, or other derivative depending on statutory definitions, venue design, and supervisory interpretation. This paper proposes the following definition: a cryptocurrency perpetual is an open-ended, margin-based derivative that gives synthetic long or short exposure to an underlying crypto asset and replaces expiry-based settlement with periodic funding payments that anchor the contract price to a reference spot price.
This paper presents a comprehensive structural analysis of cryptocurrency derivative markets spanning January 2019 to December 2024, covering Bitcoin (BTC), Ethereum (ETH), and six additional tokens across over 2.83 billion high-frequency transactions on eight major centralized exchanges and three decentralized finance (DeFi) derivative protocols. Using a theoretically grounded multi-method framework—comprising Vector Error Correction Models (VECM), Hasbrouck (1995) and Gonzalo-Granger (1995) information share decompositions, Heston (1993) and rough volatility (Gatheral et al., 2018) stochastic models, DCC-GARCH(1,1) augmented with realized kernel estimators, MIDAS regressions linking high-frequency derivative signals to lowerfrequency on-chain variables, and panel quantile regressions for cross-sectional volatility risk—we deliver six primary empirical contributions. First, perpetual swap markets consistently dominate spot markets in price discovery, contributing 63.4% (BTC) and 58.7% (ETH) of price-efficient information on average, rising to 72.1% and 68.4%, respectively, during the top quartile of volatility days—consistent with informed-agent migration to leveraged venues. Second, the Heston leverage correlation estimate ρ = −0.61 for BTC and ρ = −0.73 for ETH reflects asymmetric tail risk demand rather than balance-sheet leverage, with the implied volatility smirk's left-tail slope strongly cointegrated with funding-rate deviations (r = −0.54, p < 0.001). Third, we estimate a time-varying variance risk premium averaging 14.8 (BTC) and 19.3 (ETH) annualized variance percentage points; panel regressions reveal that on-chain network congestion fees retain significant incremental explanatory power after controlling for VIX, DXY, and credit spreads—a novel identification of a blockchain-specific volatility channel. Fourth, rough volatility models (Hurst exponent H ≈ 0.08 for BTC) significantly outperform classical Heston specifications in fitting near-term implied volatility smiles, with RMSPE reductions of 31.7% for one-week expiry options. Fifth, CME Bitcoin Futures introduction produced a structural break in arbitrage efficiency, reducing basis mean-reversion halflives by 41.2% and lowering adverse-selection costs by 18.6 basis points. Sixth, on-chain DeFi perpetual protocols (GMX v2, dYdX v4) exhibit significantly higher adverse selection costs and lower price discovery shares (mean IS = 0.24) relative to centralized counterparts, but display timevarying convergence during U.S. regulatory uncertainty episodes. Our findings deliver unified implications for derivative pricing theory, risk management, and the architectural design of regulated cryptocurrency derivative markets.
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.
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.
As institutional capital increasingly flows into digital asset markets, and as the intersection of decentralized finance (DeFi) and traditional finance (TradFi) deepens structurally, a critical pricing gap has emerged: the absence of a rigorous Crypto XVA™ framework that addresses the unique risk characteristics of blockchain-based financial instruments. Prior scholarship has examined smart contracts as potential eliminators of counterparty risk (Morini & Sams 2015; Fries & Kohl-Landgraf 2018), but has not systematically constructed the affirmative case for a crypto-native valuation adjustment architecture. This paper addresses that gap. We make three principal contributions. First, we identify the novel risk factors unique to digital asset markets — smart contract vulnerability, oracle dependence, liquidity regime fragmentation, stablecoin reserve opacity, bridge transfer risk, and gas execution uncertainty — and argue each warrants a distinct valuation adjustment category. Second, we establish the critical analytical distinction between duration-bearing instruments (perpetual swaps, LP positions, DeFi lending) and instantaneous transactions (DEX spot swaps, bridge transfers), showing that the appropriate mathematical treatment differs fundamentally between these two classes and that conflating them produces analytically incoherent results. Third, we derive a generalized Crypto XVA integral that collapses appropriately in both regimes and demonstrate its application across five canonical DeFi instrument types with worked numerical examples.
We study the demand for safety and liquidity in the crypto ecosystem. In an environment lacking frictionless access to traditional safe assets, we examine whether stablecoin lending pools provide liquidity services to investors. To do so, we develop a model in which a representative investor allocates liquidity between stablecoin lending pool deposits and traditional safe assets (e.g., MMF shares). The model delivers three predictions: (i) the stablecoin premium co-moves positively with the Treasury premium when investors value liquidity services of stablecoin pools, (ii) Treasury supply decreases the stablecoin premium, and (iii) declines in the perceived liquidity of stablecoin pools --- e.g., due to de-pegs or hacker attacks --- reduce their premium. Our empirical results provide evidence consistent with these predictions. They suggest that investors treat stablecoin lending pools as money-like instruments and that shocks to traditional safe assets transmit to crypto markets. Our findings contribute to the literature on safe assets by showing how safety is intermediated in crypto markets. They also offer new insights into the segmentation and structure of decentralized finance (DeFi) as it evolves alongside traditional financial systems.
This paper proposes a novel decentralized financial instrument-the Algorithmic Yield-Multiplier Note-to mathematically resolve the classical agency friction between debt and equity constituencies. Under legacy market microstructures, the strict fungibility constraints and asynchronous settlement latencies of traditional clearinghouses preclude the issuance of dynamic, cross-asset covenants, thereby exacerbating asset substitution and debt overhang during macroeconomic distress. We circumvent these architectural bottlenecks by migrating corporate liability to a programmable, tokenized infrastructure. We introduce a continuous, state-dependent yield function that algorithmically scales a bondholder's coupon rate relative to their cryptographically verifiable equity holdings, structurally coercing fixed-income investors into an Endogenous Capital Loop. To defend this mechanism against high-frequency decentralized finance (DeFi) exploits, such as flash-loan and snapshot arbitrage, we engineer a continuous, path-dependent knockout barrier, 𝑆 𝑖 (𝑡), that permanently collapses the yield premium upon any instantaneous breach of the requisite equity threshold. Furthermore, we resolve the resultant fungibility crisis by constructing a hybrid Decentralized Exchange (hDEX) utilizing atomic swaps to govern secondary market velocity. By algorithmically enforcing a liquidity tax (𝜏), a yield-surrender covenant (𝛾), and strict cryptographic vesting lock-ups (𝑇 penalty), the mechanism fundamentally traps institutional capital. Ultimately, we demonstrate that this programmable constraint system monetizes investor duration risk and artificially suppresses the realized covariance of the firm's stock. By fusing the fixed-income and equity constituencies, the corporate treasury engineers a Pareto-improving capital structure that dramatically lowers the effective Weighted Average Cost of Capital (WACC) and insulates the enterprise value from systemic market contagion.
Lending protocols in decentralized finance have traditionally relied on over-collateralization mechanisms, where investor protection is primarily ensured through the automatic liquidation of collateral. While effective from an operational perspective, this approach limits the economic role of credit when compared with under-collateralized structures. In such settings, the prudential management of credit risk becomes a central element for protocol sustainability. This paper proposes a prudential framework for decentralized lending protocols by introducing an additional protection layer based on the distinction between tokenized loss-absorbing capital, an operational buffer, and a prudential reserve. The model defines three classes of subordinated instruments-First Loss Token, Contingent Capital Token, and Subordinated Backstop Token-arranged according to a progressive loss waterfall. The model is first applied to public data from Goldfinch and then extended to a TrueFi dataset, with the aim of assessing the ability of the policy to reduce losses borne by senior liquidity providers. The model shows a net reduction in losses. The sensitivity analysis confirms that the mechanism maintains a positive net benefit across variations in instrument costs, risk weights, and loss severity. The results suggest that an explicit prudential layer may contribute to strengthening the resilience of DAO-based credit protocols by making the prudential cost of risk-taking more transparent and by distinguishing between available liquidity, loss-absorbing capital, and protective reserves.
The pervasive volatility and structural complexity of decentralized assets present significant challenges for modern portfolio management. This paper introduces Coin Quest, a novel, high-fidelity cryptocurrency tracking and risk management platform designed to address critical shortcomings in existing market solutions, notably high data latency and the deficiency of robust quantitative risk tools. Our technical proposal mandates a resilient microservices architecture centered on Apache Kafka for high-throughput, low-latency data stream ingestion, ensuring real-time portfolio valuation across disparate exchanges and blockchains. The analytical core of Coin Quest implements the Monte Carlo Simulation (MCS) framework to compute Value at Risk (VaR) and the superior measure, Conditional Value at Risk (CVaR), recognizing the non-normal return distributions inherent to crypto assets. Furthermore, we detail specialized algorithms necessary for comprehensive tracking and valuation of complex Decentralized Finance (DeFi) positions, including the calculation of Impermanent Loss, and quantitative monitoring of NonFungible Tokens (NFTs) using floor price metrics. We conclude by outlining empirical validation requirements demonstrating the system’s capacity to maintain sub-100ms data latency and confirming the superior predictive accuracy of the MCS-based risk model against traditional historical simulations in highly volatile market environments.
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.