Mobeen Ur Rehman, Neeraj Nautiyal, Xuan Vinh Vo, Muhammad Kashif ¡ 5 authors
Abstract Cryptocurrencies have regained mainstream attention, with Bitcoinxâ˛s recent rally renewing investor interest across the digital asset space. This study focuses on the connectedness and spillover effects among seven major digital assets to examine the asymmetric relationships conditional on market conditions and time horizons. To emphasize the significance of short- and long-term trading dynamics, we explore the state dependence of linkages during extreme upward and downward market movements. Our findings suggest a significant connectedness induced by Litecoin and Ethereum. Short-term fluctuations are the dominant drivers of crypto-market vulnerability across quantiles and frequencies. Pronounced upper-quantile connectedness emerges consistently across all markets. Interestingly, major currencies, such as Bitcoin, Ethereum, Ripple, and Dash, act as receivers during upside and median market conditions, whereas Ethereum and Litecoin exhibit transmission effects. Moreover, no connectedness is detected between Ethereum and Bitcoin at extreme quantiles. The findings highlight the need for careful monitoring and risk assessment of extreme events, demanding careful risk monitoring during periods of turmoil.
Statistical arbitrage strategies, including pairs trading, rely on identifying co-movements and static long-term equilibrium relationships between assets, where conventional methods fail to capture non-stationary dynamics, hence reducing trading effectiveness. This study, therefore, addresses this challenge by employing a dynamic co-integration approach combined with deep learning techniques to select suitable cryptocurrency pairs and forecast spread dynamics. The study examines multiple cryptocurrencies, namely: BNB, Ethereum, Litecoin, Ripple, and USDT, using dynamic Johansen co-integration tests to identify pairs with time-varying equilibrium relationships, and model the spread through a Dynamic Weighted Ensemble of Deep Neural Network and Long Short-Term Memory. Forecasting accuracy, trading performance, and predictive uncertainty are evaluated using error metrics, trading outcomes, and 99% prediction intervals. The results indicate that only those cryptocurrencies with dynamically coherent relationships are suitable for mean-reversion strategies. Furthermore, the study found that the Dynamic Weighted Ensemble achieves the best predictive accuracy. At the same time, LSTM captures proportional temporal dynamics effectively, and the ensemble-driven trading signals generate timely buy and sell decisions with low-lag execution and robust management of market volatility. These findings, therefore, highlight the advantages of combining dynamic co-integration and adaptive deep learning for statistical arbitrage.
Kwestan Ahmed Ismael, Heshu Othman Faqe, Mohammed Hussein Abdalla, Hindreen A. Taher
In this work we use historical market data from Bitget to predict weekly open prices of Ethereum (ETH) for a 96-week period with the Prophet forecast model trained by using Particle Swarm Optimization (PSO) algorithm. Because of this, the research delves into automated hyperparameter tuning for Prophet in order to improve forecast performance on cryptocurrency markets where volatility, structural breaks and irregular trading patterns pose a significant challenge to time series prediction. The PSO algorithm is a good method to explore the high dimensional parameter space in which it can strike between the global analysis and local exploitation for detecting minimal forecast errors. Based on evaluating model performance for which we used accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in training, test holdout & full-fit situations. PSO-optimized Prophet: The results show a great in-sample fitting and fast convergence behaviour, as the best CV RMSE is slightly higher than the lowest one should have obtained if used only 10 iterations. Although forecasts exhibit stability and track long-term trends well, the model does not predict short-term fluctuations in the holdout set with high accuracy (wider forecast uncertainty intervals). Our results shed light on the utility of PSO to improve Prophet-based price prediction in cryptocurrencies, reinforce the relevance of uncertainty quantification in asset markets and inform risk-aware decisions of financial agents dealing with unstable assets.
In this study, we evaluated the returns and return volatility of a Brazilian stablecoin linked to fertilizers during periods preceding its discontinuation. In light of the safe haven literature, we also tested the correlation between this stablecoin and a traditional cryptocurrency, Bitcoin, and modeled its behavior during periods of Bitcoinâs extreme returns. In terms of methodology, we employ GARCH-family models (including DCC-GARCH) to analyze daily data from 1 December 2022 to 16 January 2025. We also employ an analysis using Large Language Models (LLMs), evaluating the stablecoin time series considering the period of its discontinuation. The results indicated that as the discontinuation date approached, the stablecoin exhibited statistically significant lower returns and higher volatility. While the DCC-GARCH indicated no correlation between the assets, we found that the stablecoinâs returns exhibited a negative relationship with Bitcoinâs extreme returns, challenging its potential efficacy as a safe haven. This article offers practical contributions for digital asset investors, indicating that even physically backed stablecoins, designed for stability, are subject to significant volatility, idiosyncratic risks, and potential discontinuation.
Perpetual futures account for approximately 93% of cryptocurrency futures trading volume, yet funding rate dynamics across fragmented markets remain understudied. We construct a high-frequency panel dataset comprising 35.7 million one-minute observations across 26 cryptocurrency exchanges (11 centralized, 15 decentralized) spanning 749 symbols over eight consecutive days. Using time-series econometrics, correlation analysis, and Granger causality tests, we characterize funding rate dynamics, market integration, and information flow. We find evidence of a two-tiered market structure: centralized exchanges (CEX) dominate price discovery with 61% higher integration than decentralized exchanges (DEX), and all significant information flow runs CEX-to-DEX with zero reverse causality. While 17% of observations exhibit economically significant arbitrage spreads (âĽ20 basis points), only 40% of top opportunities generate positive returns after transaction costs and spread reversals. Delta-neutral portfolio simulations reveal that successful arbitrage requires both high spreads and sufficient duration before inevitable reversals, with forced exits occurring in 95% of opportunities. The findings show that cryptocurrency derivatives markets exhibit a persistent two-tiered structure in which centralized platforms dominate price discovery while transaction costs and spread reversal risks prevent arbitrage from eliminating large mispricings between platforms, resolving the apparent paradox of substantial price fragmentation coexisting with market efficiency.
The delisting of Binance USD (BUSD) constitutes a major regulatory intervention in the stablecoin market and provides a unique opportunity to examine how targeted regulation affects liquidity allocation, market concentration, and short-run systemic risk in crypto-asset markets. Using daily data for 2023 and a linear and nonlinear Local Projections event-study framework, this paper analyzes the dynamic market responses to the BUSD delisting across major stablecoins and cryptocurrencies. The results show that liquidity displaced from BUSD is reallocated primarily toward USDT and USDC, leading to a measurable increase in stablecoin market concentration, while decentralized and algorithmic stablecoins absorb only a limited share of the shock. At the same time, Bitcoin and Ethereum experience temporary liquidity contractions followed by a relatively rapid recovery, suggesting conditional resilience of core crypto-assets. Overall, the findings document how a regulatory-induced exit of a major stablecoin reshapes short-run market dynamics and concentration patterns, highlighting potential trade-offs between regulatory enforcement and market structure. The paper contributes to the literature by providing the first empirical analysis of the BUSD delisting and by illustrating the usefulness of Local Projections for studying regulatory shocks in cryptocurrency markets.
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.
ABSTRACT Stablecoins attract academic interest because of their valueâpegging mechanisms and price stability. This likely results in distinct market efficiency. This study compares stablecoins (USDC, Tether, Dai) with Bitcoin and Ethereum and assesses long memory through the Hurst exponent while addressing distortions caused by heavy tails and extreme events. Through shuffled and rankâorder series with a slidingâwindow approach, we provide the first reliable timeâvarying analysis. The results show that stablecoins exhibit inefficiency and antiâpersistence, with Tether being relatively more efficient. Their tail properties are highly sensitive to extreme events. In contrast, Bitcoin and Ethereum maintain stable weakâform efficiency even during the COVIDâ19 pandemic. These differences are linked to stablecoins' US dollar pegging mechanisms and regulatory constraints. The findings of this study enable comparisons of market efficiency between stablecoins and unpegged cryptocurrencies and offer insights for regulation and investment decisions.
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 analyse returns on cryptocurrency perpetual futures by first developing a cost-of-carry model tailored to digital assets. The model captures the link between spot and perpetual futures prices, implying a positive convenience yield and negligible off-chain storage costs. Furthermore, we employ a log-linear approximation to demonstrate that expected return of holding perpetual futures derive from the current log basis, misperception of forward-looking spot price, and expected futures-spot spreads over the âmaturityâ of futures contract. We then assess a comprehensive set of 170 return predictors, classified into categories of basis, momentum, liquidity, size, and volatility. Sorting based on these predictors yields 63 statistically significant total returns (i.e. price movement plus funding fee yields, with each exceeding the 5 significance level). Finally, we demonstrate that a two-factor model, based on the log-basis and a price-volume relevant factor, effectively explains all 63 strategies, highlighting the role of systematic drivers in perpetual futures markets.
This paper empirically examines the relationship between Polymarket prediction market odds for the passage of the Digital Asset Market Clarity Act (CLARITY Act) and the price of Bitcoin, while controlling for broader equity market movements. Using daily data from January 1 to April 4, 2026 (94 observations), the analysis first confirms that Bitcoin log-returns and changes in Polymarket odds are stationary (Augmented Dickey-Fuller p
The rapid expansion of decentralized finance (DeFi) has generated rich, transparent on-chain data that remains largely underutilized in high-frequency trading models. Most existing studies rely primarily on centralized exchange (CEX) price feeds, which often suffer from low signal-to-noise ratios (Lim et al., 2021; Lee et al., 2025). This study develops a multi-modal forecasting framework for the WETH/USDC 0.05% fee tier pool on Uniswap v3. We integrate Binance CEX market microstructure data with granular onchain DEX metrics, including swap imbalance, on-chain volume, liquidity depth, tick velocity, and pool liquidity utilization. An XGBoost classifier was trained on synchronized 15-minute bars, with realistic cost-aware backtesting incorporating pool fees and slippage. The model achieved a directional accuracy of 63.26% on out-of-sample data. Feature importance analysis revealed that on-chain variables, particularly volume_usd, liquidity_usd, imbalance, and tick_velocity, ranked among the top predictors. In cost-aware backtesting, the strategy outperformed Buy & Hold by 2.49 percentage points, although absolute returns remained modestly negative due to transaction costs. This research demonstrates the incremental predictive value of integrating Uniswap v3 on-chain DEX metrics with CEX data for high-frequency forecasting. While transaction costs remain a significant challenge, the findings highlight the potential of multi-modal approaches in DeFi markets and provide a foundation for future work using more advanced architectures such as the Temporal Fusion Transformer..
This thesis investigates whether the integration of digital assets, specifically Bitcoin and <br/>Ethereum, into a traditional multi-asset institutional portfolio can produce superior out-of<br/>sample risk-adjusted performance relative to the conventional 60/40 equity-bond benchmark. <br/>Using a Mean-Variance Optimization (MVO) framework enhanced by Ledoit-Wolf <br/>covariance shrinkage, the study evaluates seven distinct portfolio configurations across an <br/>empirical window spanning January 2016 to May 2026, covering multiple market regimes <br/>including the 2020 COVID-19 liquidity shock, the 2022 inflationary spike, and the 2024 <br/>institutional crypto adoption phase. <br/>The empirical design employs a dual-mode out-of-sample validation strategy: a fixed-weight <br/>stability test and a rolling realism test with systematic rebalancing. Results suggest that <br/>portfolios enriched with Bitcoin tended to achieve superior Sharpe and Sortino ratios relative <br/>to the baseline within this sample period and asset universe. The Master Portfolio (Case 7), <br/>combining equities, bonds, Bitcoin, Ethereum, Gold, and Silver, attains a static Sharpe Ratio <br/>of 1.62, a static Sortino Ratio of 3.23 and a Maximum Drawdown of only â6.40%. These <br/>findings are consistent with the primary hypothesis (Hâ) that, within this empirical setting, <br/>Bitcoin can improve out-of-sample risk-adjusted performance, while also providing evidence <br/>consistent with the secondary hypothesis (Hâ) that Bitcoin acts primarily as a portfolio <br/>diversifier rather than a consistent safe haven. All results should be interpreted as conditional <br/>on the sample period, the chosen asset universe, and the rebalancing assumptions. All asset <br/>price data was sourced from Investing.com and the risk-free rate from the FRED 3-Month <br/>Treasury Bill series (TB3MS).
We present the most complete unified taxonomy of Black-Scholes option price sensitivities (Greeks) available in the literature, encompassing 21 distinct measures through third order: first-order (Delta, Vega, Theta, Rho), second-order (Gamma, Vanna, Charm, Vomma, Veta, Vera, Dual Delta, Dual Gamma), third-order (Speed, Zomma, Color, Ultima, DvannaDvol, DvommaDspot), and portfolio-level (Dollar Delta, Dollar Gamma, Lambda). For each Greek we provide: (i) a fully explicit derivation from the dividend-adjusted Black-Scholes formula showing every application of the chain rule and product rule, (ii) alternative derivation paths including risk-neutral expectation differentiation and heat equation Greenâs function representations, (iii) closed-form expressions for both European calls and puts, (iv) identification of put-call parity equivalences, (v) complete asymptotic analysis (deep ITM/OTM, short/long-dated, zero/high vol limits), (vi) monotonicity and convexity properties with extrema locations, (vii) dimensional analysis for practitioner interpretation, and (viii) practical trading context. We derive the Black-Scholes PDE from first principles via ItĂ´âs lemma and the replicating-portfolio argument, establish the risk-neutral pricing connection, and prove the key symmetry lemma that simplifies every Greek derivation. The Gamma-Theta tradeoff is proved directly from the PDE with trading implications. The Vanna-Volga pricing method is derived from smile replication principles. We provide a complete treatment of sticky-strike versus stickydelta hedging conventions, delta hedging theory with continuous and discrete P&L analysis, Greeks under stochastic volatility (Heston model), numerical methods for Greeks computation (finite differences, pathwise, likelihood ratio, and adjoint algorithmic differentiation), and the behavior of Greeks near expiry including pin risk. A differential-geometric interpretation frames the Greeks as gradient, Hessian, and third-order tensor components on the six-dimensional Black-Scholes parameter manifold, with the PDE as a constraint surface. Fourth-order Greeks are derived and a convergence analysis of the Taylor price expansion justifies the third-order truncation for perturbations up to 10% of spot. Publication-quality three-dimensional surface visualizations for all 21 Greeks reveal the topology of each sensitivity across its natural parameter space. Formal proofs of all 12 put-call parity equivalences, a complete 21 Greek formula reference card, and a comprehensive monotonicity and extrema table are provided as appendices. A Python implementation with numerical verification against finite differences accompanies the paper, with over 50 figures. To our knowledge, this constitutes the most comprehensive rigorously derived reference for Black-Scholes Greeks in a single document.
GPU compute has become a multi-hundred-billion-dollar exposure underpinning AI, yet whether its price risk can be hedged with existing assetsâand thus whether the dedicated compute-futures markets announced in 2026 are warrantedâhas, to our knowledge, not been tested. Using daily GPU rental-rate benchmark indices (via Bloomberg) for three generations (A100, H100, B200)âthe first such cross-generation panel we are aware ofâwe ask whether compute is "the new oil": a hedgeable industrial commodity. We document its price dynamicsânewer generations are far more jump-prone than oil or equities (though lower in overall volatility), with no volatility clusteringâand a cross-generation price structure whose discounts shift over time. We then test cross-asset proxy hedging out-of-sample. GPU rental returns are weakly correlated (daily |Ď| Ⲡ0.1) with NVIDIA, semiconductors, compute-infrastructure equities, and the broad market, and no minimum-variance proxy hedge delivers out-of-sample variance reduction distinguishable from zeroâacross the three generations, across horizons from daily to weekly (monthly and quarterly results are indicative only, given few non-overlapping blocks), and after multiple-testing, active-day (stale-filtered), and frontier-roll checks; a naĂŻve one-for-one hedge sharply adds risk. This is a negative result on a short, stale sample: a power analysis shows the effective sample cannot resolve a true variance reduction below roughly 9%, so we report the absence of a detectable conventional hedge rather than proof of exact orthogonality. Even so, no detected hedge removes more than a small fraction of a jump-prone exposure, so the practical case for a direct instrument is little changed by that ceiling. We read the result constructivelyâa suggestive incomplete-market rationale for a dedicated marketâwhile noting that the same weak correlation implies a liquidity paradox for those contracts, and we quantify the heightened exposures (not materially reducible by the proxies we test) in AI-training budgets and GPU-collateralized lending.
This study revisits calendar anomalies in cryptocurrency markets using hourly data for four actively traded cryptocurrencies (Bitcoin, Ethereum, Tether USDt, and BNB) and eight less active cryptocurrencies. While prior studies based on daily returns provide mixed evidence on day-of-the-week (DoW) effects, we show that these patterns are not persistent daily phenomena. Instead, they are driven by a limited number of intraday intervals and do not reflect broad daily behavior.We further document that these effects are localized, asset-specific, and more pronounced among actively traded cryptocurrencies, while largely absent among less active ones.Overall, the findings indicate that cryptocurrency markets exhibit limited and short-lived inefficiencies rather than persistent anomalies. This highlights the importance of employing high-frequency data in studies of continuously traded markets and suggests that digital asset investors may benefit from the presence of abnormal returns only in a limited number of hours on specific days of the week.
This paper documents a robust size-dependent pattern in cryptocurrency return predictability. Small coins exhibit strong short-term reversal, whereas large coins exhibit momentum, and the relation varies monotonically across the size distribution. We further show that these two sides of the pattern reflect different return dynamics: small-coin reversal is driven mainly by rebounds among recent losers, while large-coin momentum reflects their continued underperformance. Liquidity frictions and idiosyncratic volatility explain part of this pattern, but not all of it. These findings point to a "size-momentum puzzle" in cryptocurrencies.
This study develops and tests a theoretical framework linking liquidity, market integration, and return predictability in cryptocurrency markets. Analyzing high-frequency daily data for five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Solana, and Ripple), we employ Random Forest models augmented with rigorous time-series diagnostics and economic significance tests. Our findings establish a fundamental dichotomy: Bitcoin exhibits predictability driven by macroeconomic fundamentals (global risk-free rates, economic policy uncertainty), consistent with its emergence as a macro-asset; altcoins, in contrast, are dominated by internal microstructure (realized volatility, illiquidity) and speculative sentiment. Formal hypothesis tests confirm that (i) lower liquidity predicts higher future returns, (ii) machine learning models systematically underpredict during positively skewed regimes, and (iii) macro integration attenuates microstructure-driven predictability. Out-of-sample R 2 values reach 6.8% for Bitcoin and 9.6% for Ethereum, with Diebold-Mariano statistics rejecting equal predictive accuracy against a random walk at the 1% level. A mean-variance investor would earn a certainty equivalent gain of 2.4% annually by exploiting these forecasts. The results challenge the efficient market hypothesis for 1 digital assets, establish a new taxonomy of cryptocurrency predictability, and provide critical implications for asset pricing, portfolio allocation, and risk management in decentralized finance.
Predicting short-term mid-price movements from limit order book (LOB) data is a fundamental problem in quantitative finance and market microstructure research, with direct applicability to both traditional exchanges and cryptocurrency marketsâincluding centralized exchanges (CEXs) and emerging on-chain LOB protocols in decentralized finance (DeFi). We present three contributions to this domain. First, we propose DA-BiGRU-CNN, a domain-aware dual-branch architecture that decomposes LOB features into price and volume information channels, processes them through dedicated bidirectional GRU encoders with shared microstructure features, and fuses temporal representations via a multi-scale convolutional bottleneck (Conv1d with kernels k = 3,5,7). Second, we provide empirical evidence for a "feature sufficiency" hypothesis: a unidirectional GRU trained on 53 basic features achieves performance statistically equivalent to one trained on 219 extensively engineered featuresâincluding rolling statistics, exponential moving averages, and lag/difference featuresâsuggesting that recurrent hidden states implicitly learn these temporal patterns. Third, we document a "negative ensemble effect" where combining sequential (GRU) and tabular (gradient boosting) models consistently degrades prediction quality, contradicting the widely-held assumption that model diversity improves ensemble performance. On a large-scale dataset of 12,165 LOB sequences (12.1M timesteps), our GRU baseline achieves a weighted Pearson correlation of 0.266, outperforming LightGBM by 58%, while our domain-aware architecture offers an architecturally principled alternative that naturally separates price dynamics from liquidity dynamics.
We document significant reversal in cryptocurrency returns at 8-and 10-week horizons, concentrated in midsize, relatively volatile assets. Using a panel of 70 USDT-quoted tokens on Binance from January 2021 through March 2026, we show that a contrarian strategy of buying past losers and selling past winners, by forming Jegadeesh-Titman (1993) calendar-time overlapping portfolios, earns a 39.6% annualized return (Sharpe 0.96, Newey-West t = 2.10). Reversal is stronger among high-volatility assets, generating a Sharpe ratio of 1.37 (t = 3.19), and is strengthened outside of the largest assets, generating a Sharpe ratio of 1.69 (t = 3.80). The effect is robust across tercile, quintile, and decile sorts; skip period variants; inverse-volatility weighting; and temporal subsamples. A circular block bootstrap with 10,000 replications corroborates the high-volatility result nonparametrically with 95% of Sharpe ratios above 0.67, and the high-versus-low volatility gap positive in 94% of replications. Several economic mechanisms to rationalize these findings are discussed, including the tendency of treasury managers to sell into upswings, and Nagel's (2012) theory that reversal compensates liquidity providers.
This paper introduces Credance-Based Collateral Exchange (CBCE) as a formal category of financial instrument distinct from conventional repo and from existing distributed ledger collateral protocols. Building on the concept of credance-the collective anterior belief that makes a financial transaction possible before any track record exists (Laverriere, 2026)-we argue that a significant class of collateral exchange activity operates on the basis of credance rather than documentation. We formalise credance as a time-varying bilateral function C(Pâ, Pâ, t), define the credance threshold θ as a composite score of transaction history, temporal depth, and costly honouring, and propose a three-mode typology of CBCE instruments. We analyse the conditions under which each mode is optimal, examine the implications for Islamic repo market development and for distributed ledger technology design, and argue that blockchain-based collateral protocols have systematically failed to incorporate credance as a design variable. A credance-aware DLT architectureincluding a credance oracle, a zero-knowledge credance proof mechanism, and a variable collateralisation ratio governed by C(Pâ, Pâ, t)-would represent a genuinely novel class of financial instrument. The paper concludes with a research agenda including a call for collaboration between financial economists and DLT researchers to prototype the proposed architecture.
⢠DeFi stablecoin yields track FFR/SOFR, but with a distinct T+3 structural lag. ⢠A settlement-friction framework links fiat rails to the T+3 transmission lag. ⢠The lag is universal for both compliant USDC and offshore, unregulated USDT. ⢠Basis regressions reveal a predictable settlement wedge after policy moves. ⢠Robust tests rule out protocol outliers, macro trends, and weekend artifacts. Decentralized Finance (DeFi) stablecoin markets increasingly function as a shadow overnight dollar system, yet the speed at which U.S. monetary policy transmits to on-chain yields remains unclear. Focusing on the recent âHigh-for-Longâ regime (2023â2025), I study this pass-through using daily Aave V3 deposit rates for USDC and USDT. Guided by a simple conceptual framework of settlement frictions and arbitrage constraints, I estimate an ordered VAR that controls for equity- and crypto-market cycles. The results show that DeFi yields are tightly anchored to the Federal Funds Rate (and, in robustness, SOFR), challenging the âcrypto-decouplingâ narrative. However, transmission exhibits a distinct T+3 structural latency, universal across both compliant USDC and unregulated USDT, indicating an infrastructural, systemic friction rather than issuer-specific constraints. Robustness tests, alternative-explanations analysis, and quantity-based mechanism checks rule out protocol outliers, broader macro trends, and weekend artifacts, supporting an interpretation based on delayed settlement and execution across fiat rails. Complementary basis regressions provide a direct pricing implication: the on/off-chain spread exhibits a significant, predictable wedge during the settlement window that dissipates thereafter. The findings imply that despite algorithmic immediacy, DeFi remains constrained by fiat infrastructure, and that improving on-chain capital efficiency may require modernizing payment rails alongside issuer-focused regulation.