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.
This study, among many others, provides an initial quantitative contribution to the emerging literature as well as existing empirical evidence regarding contagion risks across cryptocurrency markets over time. Using VAR (Vector Autoregressive) and SVAR (Structural Vector Autoregressive) models with Granger causality, along with Student’s t-copulas, we find that Bitcoin is likely to act as an independent asset in this market, while Ripple and Litecoin tend to be recipients of contagion effects, and Ethereum appears to be a primary source of contagion. Our study offers additional insight into the investigation of contagion risks between both historical and future cryptocurrency values by employing Student’s t-copulas for joint distribution analysis and aims to determine whether these contagion effects remain consistent over time. The results suggest, in both cases, that all cryptocurrencies tend to move negatively in extreme value conditions. Investors are therefore encouraged to pay closer attention to “bad news” and market movement patterns in order to make timely decisions regarding buying, holding, and selling. Note: This thesis reflects the state of cryptocurrency markets and related quantitative models as of 2019-2021. The findings and conclusions are intended to preserve the integrity of the research conducted during this specific time period. I acknowledges that this field evolves rapidly, and an updated analysis may be presented in a forthcoming research paper.
This review synthesizes the emerging literature on behavioral finance in cryptocurrency perpetual futures and perpetual swaps. It uses a constrained systematic review of accessible repositories, publisher pages, and citation trails for studies published or posted from January 2021 to April 2026. The synthesis separates 13 direct perpetual-futures studies from 6 adjacent behavioral studies that inform interpretation. The central question is how behavioral mechanisms shape trading, pricing, and market quality in perpetual futures markets. The strongest direct evidence concerns speculative demand and basis risk, leverage choice and liquidation risk, funding-rate carry and arbitrage behavior, informed trading and market quality, and exchange-design effects across centralized and decentralized venues. Direct evidence on classic behavioral constructs such as fear of missing out, overconfidence, disposition effects, and learning remains sparse in perpetual-specific settings. Three conclusions stand out. First, perpetuals are behaviorally distinctive because funding fees, leverage, mark-to-market margining, and auto-liquidation create a high-frequency feedback system between prices and trader positions. Second, the strongest causal evidence indicates that perpetual contracts increase spot-market trading volume but worsen adverse-selection conditions when informed trading rises around funding windows. Third, exchange design matters because inverse, linear, quanto, oracle-priced, and VAMM-based contracts expose traders to different incentives and liquidation dynamics. The most important research gaps concern trader-level identification, CEX-DEX comparisons using comparable data, contract-type heterogeneity, and causal tests of leverage-rule changes.
This thesis examines the implications of market frictions in international finance and macroeconomics in three contexts. The first chapter documents the effect of trading relationships on client trading outcomes in the over-the-counter (OTC) foreign exchange (FX) derivatives market. The second chapter documents the effect of nominal wage setting frictions on employment. The third chapter examines the behavior of non-U.S. central banks when firms engage in currency mismatch, borrowing more in dollars than given by their dollar operating exposures, emphasizing how imperfect regulation may affect U.S. dollar interest rates. In the first chapter, joint with Gerardo Ferrara, I study whether clients that rely more heavily on a dealer in the OTC FX derivatives market have worse trading outcomes after the dealer is adversely shocked. Using granular transaction-level data, we document that trading relationships are persistent—in an active trading week, clients are more likely to trade with a dealer that they had a relationship with and relied on more heavily. Then, we exploit the March 2023 collapse of Credit Suisse as an exogenous shock to exposed clients’ set of trading alternatives when relationships are persistent. Using difference-in differences analyses, we find that, although Credit Suisse’s EURUSD notional traded and trade count declined, clients that relied less heavily on Credit Suisse did not differentially reduce their Credit Suisse-specific trading activity relative to more reliant clients. Instead, more reliant clients continued trading at the client level and increased activity with other existing dealer relationships without incurring additional costs, relative to less reliant clients. These findings suggest that search and bargaining frictions were not particularly costly for heavily reliant clients after the shock—relationship persistence did not differentially prevent them from reallocating activity to existing alternative dealers, or lead to relatively greater costs, when their relationship dealer came under stress. In the second chapter, joint with Gert Bijnens, Hugo Monnery, and Laura Nicolae, I empirically document the effect of wage changes, driven by wage indexation to inflation, on firm-level employment growth. In Belgium, nearly all employees’ wages are indexed to inflation and firms are grouped into labor agreements that determine the exact timing and frequency at which wages are indexed, e.g. every year or every month. Using firm-level administrative data, we estimate two-stage least squares regressions of firm-level employment growth on wage growth, instrumented by the wage growth implied by the firm’s indexation policy. We find that employment contracts by 0.4% over four quarters for each 1% increase in wages. This result is robust to including NACE sector-date fixed effects and to using only variation in firms’ indexation timing, controlling for their chosen indexation frequency. About one-third of the response comes via anticipation of future wage increases. The elasticity is more than twice as large in magnitude in the post-pandemic period than before it, suggesting strong nonlinearities. Overall, these results show that, by preventing inflation from reducing real wages, inflation indexation reduces employment. In the third chapter, joint with Mitali Das, Gita Gopinath, Taehoon Kim, and Jeremy Stein, I document an externality of central banks’ imperfect regulation of firms that engage in currency mismatch, which results from central banks’ dollar reserve accumulation decisions. We explore how foreign central banks behave when firms engage in currency mismatch. Using a panel of 56 countries, we document that central bank holdings of dollar reserves are correlated with the dollar-denominated bank borrowing of their non-financial corporate sectors. Then, we build a model in which the central bank can deal with private-sector mismatch, and the associated risk of a domestic financial crisis, by: (i) imposing ex ante financial regulations; or (ii) accumulating dollar reserves to serve as an ex post dollar lender of last resort. The model highlights a novel externality: individual central banks may over-accumulate dollar reserves, relative to what a global planner would choose. Under imperfect regulation of currency mismatch, individual central banks do not internalize that their hoarding of reserves exacerbates a global scarcity of dollar-denominated safe assets, which lowers dollar interest rates and encourages firms to further increase the currency mismatch of their liabilities. Relative to the decentralized outcome, a global planner may therefore prefer higher capital requirements and reduced holdings of dollar reserves.
This paper studies volatility prediction for Ethereum in the post-Merge era. Using daily ETH/USD returns from 15 September 2022 to 23 April 2026, we compare standard GARCH(1,1), Heston-Nandi GARCH(1,1), cross-validated and aggregated EWMA predictors, and Nadaraya-Watson kernelregression predictors. The kernel forecasts are constructed from a rank-transformed state vector that captures recent volatility and signed-return conditions, allowing the conditional variance function to be nonlinear and state dependent. The results show that forecast performance is strongly horizon dependent. At the one-day horizon, the kernel predictor using the fitted GARCH volatility state delivers the lowest final cumulative squared prediction error, outperforming the standard GARCH benchmark and all EWMA-type competitors. At the ten-day-ahead horizon, the advantage of local nonparametric information weakens, and the mean-reverting structure of GARCH becomes more valuable. The estimated kernel surface reveals that predicted ETH volatility is highest when elevated recent volatility coincides with negative signed-return pressure. Conditional quantile results further show that kernel-based VaR improves lower-tail risk forecasts, especially at the 1% quantile. Overall, the evidence suggests that post-Merge Ethereum volatility is persistent, asymmetric, heavytailed, and nonlinear, and is best modelled by combining economically meaningful volatility states with flexible nonparametric forecasting maps.
We use novel intraday data to study the price discovery process in cryptocurrency markets around U.S. monetary policy, inflation, and labor market announcements. Our analysis reveals the following: (1) volatility, trading volume, and bid-ask spreads rise sharply at announcement times and remain elevated for up to 30 minutes relative to comparable non-announcement intervals, indicating that cryptocurrency investors pay attention to these announcements and that information is quickly incorporated; (2) announcement surprises associated with higher yields or "risk-off" conditions cause substantial cryptocurrency price declines - pointing to a potential strengthening of U.S. monetary policy transmission to the real economy in recent years; (3) the time-varying magnitude and direction of price reactions more closely resemble those of U.S. equities than those of fiat currencies or commodities, suggesting that cryptocurrencies behave primarily as risk-sensitive assets; (4) price impact estimates of order flow around announcements are consistent with rising institutional participation in cryptocurrency markets and their crucial role for price discovery.
We construct a protocol-native valuation signal for Ethereum based on demand-side fees expressed as a share of token supply. The signal measures the log deviation of current fee intensity from its trailing median, a dimensionless ratio denominated entirely in ETH. It predicts subsequent token returns at 10 to 60 day horizons with in-sample R-squared up to 22.8% and expanding-window out-of-sample R-squared of 14.4% at 45 days. The signal retains predictive power after macroeconomic controls, standard crypto risk factors, and momentum controls, and predicts ETH-specific relative returns. Predictability emerges only after the Dencun hard fork (March 2024), which separated execution fees from data availability fees, making the demand signal empirically detectable. Our findings demonstrate that demand-side economic flows are capitalized into token prices in the absence of firms, contracts, or residual cash flow rights, extending valuation logic to rule-based economic systems.
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.
This paper examines whether social media sentiment derived from Twitter and Reddit improves the explanation and prediction of cryptocurrency volatility. Using Bitcoin and Ethereum as benchmark assets, we combine sentiment indicators with GARCH-type models and the HAR-RV framework. Results suggest that cryptocurrency volatility is primarily driven by internal market dynamics rather than social media sentiment.
João Pires da Cruz, Daniel Costa, Pedro Granate, Armando Teixeira · 6 authors
We study the formation and evolution of trading networks in non-fungible token (NFT) markets using transaction-level data from two major collections, Bored Ape Yacht Club (BAYC) and Azuki. We introduce a simple transaction-based clustering rule that identifies dynamically evolving trading networks formed by buyer-seller interactions. These networks correspond to persistent trading structures linking wallets through sequences of transactions. We document three main empirical regularities. First, trading networks emerge endogenously and exhibit heavy-tailed size distributions consistent with preferential attachment dynamics. Second, the internal connectivity of large networks displays scale-free degree distributions characteristic of growing trading systems. Third, the lifetime of trading networks follows approximately exponential statistics, indicating a memoryless extinction process. These findings suggest that NFT markets are organized around evolving clusters of trading relationships rather than isolated transactions. The results replicate across collections, indicating that trading network formation is a robust structural feature of NFT markets. Our findings provide new evidence on the microstructure of digital asset markets and the mechanisms governing the formation and persistence of trading relationships.
This paper investigates the extent to which cognitive heuristics, social influence, and digitally-mediated sentiment drive the extreme volatility of cryptocurrency, Decentralised Finance (DeFi), and Non-Fungible Token (NFT) markets, and the degree to which these dynamics deviate from the Efficient Market Hypothesis. Using an integrative narrative review and a synthesis of empirical evidence from 2014–2025, the paper develops the Integrated Digital Asset Behavioural Model (IDABM), a four-variable framework relating market stability to social velocity (Sv ), heuristic load (Hl ), platform gamma (Pγ ), and liquidity leverage (Ll ). The analysis draws on demographic and sentiment data, case evidence from the 2022 Terra/ Luna and FTX collapses, and a comparative cross-asset bias taxonomy. The findings indicate that digital asset markets constitute a pure sentiment environment in which the absence of conventional valuation anchors produces heuristic dominance and structurally amplified herding behaviour. The paper concludes that effective regulation must shift from informational disclosure toward behavioural guardrails — including algorithmic accountability, regulation of gamified trading interfaces, and behavioural literacy requirements.
We document a sizeable disposition effect in the market for non-fungible tokens (NFTs). Using a comprehensive transaction dataset from OpenSea, we show that NFT holders systematically realize gains prematurely while holding onto losses, mirroring behavior documented in traditional equity markets. Consistent with a high participation rate of retail investors and the lack of clear fundamental values, the effect is significantly more severe than in equity markets. We further find that the magnitude of the disposition effect attenuates in December, consistent with end-of-year tax-loss harvesting incentives, suggesting that on-chain transactions can be monitored by tax authorities. Finally, to address the NFT market's episodic illiquidity, we introduce a novel measure of the disposition effect based on the time-to-sale of listed assets. Our findings extend behavioral finance theory to digital-asset markets and provide new tools for studying the disposition effect in illiquid trading environments.
Aim: This study examines whether and how the disposition effect shapes Ethereum investors’ selling decisions. It asks whether investors are more likely to realize gains than losses, whether this asymmetry strengthens during high-volatility periods, and whether it weakens around major protocol upgrades, including the Merge, Shapella, and Dencun. Methodology: The study builds a high-frequency address-day panel for 2020–2024 using public on-chain data and labeled centralized-exchange deposit clusters as conservative proxies for sell decisions. Rolling cost bases are reconstructed under FIFO and value-weighted rules, and unrealized gains and losses are linked to realized sales through discrete-time logit and Cox hazard models. The design also includes event windows and robustness checks. Findings: The framework is designed to identify three mechanisms: asymmetric realization of gains over losses, stronger gain realization under high volatility, and attenuation around major protocol-upgrade events. Implications: The study offers a transparent design for analyzing behavioral bias in crypto-asset markets with verifiable blockchain data. It is relevant to exchanges, regulators, and market designers concerned with investor behavior and risk management. Originality/value: The article extends behavioral finance to Ethereum by using public ledger data rather than brokerage records and by integrating behavioral bias, volatility regimes, and protocol events in one framework.
We document stable cross-asset patterns in cryptocurrency limit-order-book microstructure: the same engineered order book and trade features exhibit remarkably similar predictive importance and SHAP dependence shapes across assets spanning an order of magnitude in market capitalization (BTC, LTC, ETC, ENJ, ROSE). The data covers Binance Futures perpetual contract order books and trades on 1-second frequency starting from January 1st, 2022 up to October 12th, 2025. Using a unified CatBoost modeling pipeline with a direction-aware GMADL objective and time-series cross validation, we show that feature rankings and partial effects are stable across assets despite heterogeneous liquidity and volatility. We connect these SHAP structures to microstructure theory (order flow imbalance, spread, and adverse selection) and validate tradability via a conservative top-of-book taker backtest as well as fixed depth maker backtest. Our primary novelty is a robustness analysis of a major flash crash, where the divergent performance of our taker and maker strategies empirically validates classic microstructure theories of adverse selection and highlights the systemic risks of algorithmic trading. Our results suggest a portable microstructure representation of short-horizon returns and motivate universal feature libraries for crypto markets.
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.
Marco Dessalvi, Massimo Bartoletti, Alberto Lluch-Lafuente
Decentralized Finance (DeFi) has revolutionized financial markets by enabling complex asset-exchange protocols without trusted intermediaries. Automated Market Makers (AMMs) are a central component of DeFi, providing the core functionality of swapping assets of different types at algorithmically computed exchange rates. Several mainstream AMM implementations are based on the constant-product model, which ensures that swaps preserve the product of the token reserves in the AMM - up to a trading fee used to incentivize liquidity provision. Trading fees substantially complicate the economic properties of AMMs, and for this reason some AMM models abstract them away in order to simplify the analysis. However, trading fees have a non-trivial impact on users' trading strategies, making it crucial to develop refined AMM models that precisely account for their effects. In this work, we extend a foundational model of AMMs by introducing a new parameter, the trading fee ϕ ∈ (0,1], into the swap rate function. Fee amounts increase inversely proportional to ϕ. When ϕ = 1, no fee is applied and the original model is recovered. We analyze the resulting fee-adjusted model from an economic perspective. We show that several key properties of the swap rate function, including output-boundedness and monotonicity, are preserved. At the same time, other properties - most notably additivity - no longer hold. We precisely characterize this deviation by deriving a generalized form of additivity that captures the effect of swaps in the presence of trading fees. In particular, we prove that when ϕ < 1, executing a single large swap yields strictly greater profit than splitting the trade into smaller ones. Finally, we derive a closed-form solution to the arbitrage problem in the presence of trading fees and prove its uniqueness. All results are formalized and machine-checked in the Lean 4 proof assistant.
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.
On January 10, 2024 (July 23rd, 2024), the Commission approved the listing of eleven (eight) bitcoin-based (Ethereum-based) exchange-traded products (ETPs) in NYSE Arca, Nasdaq and Cboe BZX. Using these approvals as a natural experiment, we employ a program evaluation framework to study the impact of the introduction of crypto-based ETPs on the liquidity of spot bitcoin/Ethereum markets in crypto trading platforms. We use the most recently available blockchain data supplied by Kaiko. The estimation strategy identifies that while the level of liquidity has not changed, that the time series trading pattern has, and that successive returns are more (less) related. However, though returns are on average more correlated we find that the returns in the bitcoin and Ethereum spot market overall show improvements after the introduction of these ETPs globally as compared to before. The findings further shed light on the workings of different aspects of crypto asset markets.
Despite trading volumes in the tens of billions, NFT markets are illiquid: median quoted spreads of 48-200% far exceed equity levels, though execution-based measures covering nearly all sales yield effective costs of only 2-5%. Using over 410 million orderbook events-including, for the first time, comprehensive bid-side data-across six major collections, we document a distinctive institutional response: a two-tier orderbook in which collection-level floor bids, rather than token-specific orders, supply the dominant source of buy-side liquidity. A small number of algorithmic market makers provide these bids but face adverse selection inherent in collection-level offers, experiencing market-adjusted post-purchase returns of-3% to-7%. In panel regressions, collection identity absorbs over 30 percentage points of R-squared , dominating all observable spread determinants. A comparison of the same 10 000 assets under two market structures reveals that the native bilateral marketplace achieves tighter spreads (54% vs. 200%), suggesting that ease of bidding generates liquidity activity but not price efficiency.
NFTs provided an extraordinary real-time laboratory for bubble economics: returns were exceptionally right-skewed, illiquidity pervaded even the most active platforms, and a handful of trades drove aggregate performance. Investors extrapolating from realized returns without recognizing selection bias and survivorship faced a substantial risk of disappointment. As our data and simulations confirm, successful NFT investing during the bubble required an almost perfect confluence of timing, liquidity, and luck. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org .
Although many academic studies have examined volatility spillovers and dynamic correlations between stock markets, they have largely overlooked the perspective of Small and Medium-Sized Enterprise (SME) markets. On this basis, this study explores the interconnectedness and volatility correlation between Decentralized Finance (DeFi) markets and SME markets. To understand the correlation between these markets, we empirically analyse six European SME market indices—the FTSE AIM All-Share Index (AIM), BIST SME Industrial Index (BISTSME), Euronext Growth All-Share Index (EURONEXT), First North All-Share Index (FIRSTNORTH), IBEX Medium Cap Index (IBEXC), and Scale All-Share Performance Index (SCALE)—alongside three cryptocurrencies: Aave (AAVE), Ethereum (ETH), and Uniswap (UNI); two stablecoins: Dai (DAI) and USD Coin (USDC); and one synthetic asset: Synthetix (SNX). The study employed BEKK-GARCH and DCC-GARCH to analyse the existence of spillover effects and correlations from October 5, 2020, to August 18, 2024. The findings indicate that AAVE, ETH, and UNI, in particular, transmit significant volatility to the EURONEXT and FIRSTNORTH markets. However, bidirectional volatility spillover was detected between EURONEXT and AAVE, ETH, UNI, USDC, and SNX, and FIRSTNORTH and AAVE, ETH, UNI, and SNX. This suggests volatility interdependence between these markets and the existence of potential risk contagion channels.
This study investigates the relationship between dirty and clean cryptocurrencies and traditional stock index returns using the Quantile-Quantile (QQR) and Quantile-Quantile Granger Causality (QQGC) methods. The analyses were conducted using daily data from January 2018 to May 2025. QQR results show both positive and negative relationships between dirty and clean cryptocurrencies and the returns of the S&P 500, FTSE 100, TSX, and ASX indices at the low, medium, and high quantiles. According to the QQGC results, both dirty and clean cryptocurrencies showed predictive power for the returns of the S&P 500, FTSE 100, TSX, and ASX indices at different quantiles. Furthermore, it was found that both dirty and clean cryptocurrencies exhibit strong predictive power for S&P 500 and FTSE 100 returns, particularly in the middle quantiles. The results obtained reveal that distinguishing between dirty and clean cryptocurrencies under different market conditions provides important insights for investors' portfolio diversification strategies and risk management practices.
AbstractThis article investigates the economic and behavioral feasibility of creating personalizedcryptocurrencies (Fan Coins) linked to the performance of soccer players, using quantitative methods inEconometrics, focusing on box office revenue, sponsorships and sports betting. The analysis includes theThe cases of Diego Ribas (Flamengo), Neymar (Santos), and top scorers from São Paulo and Palmeiras.proposal is to analyze how reputation and sports performance can be transformed into digital assets withmeasurable value. Furthermore, it is proposed to use artificial intelligence systems to manage sponsorportfolios and a fan club application with a subscription system to foster a new sports business model basedon data and personalization.Keywords: Fan Coin; Cryptocurrency; Econometrics; Sports Economics; Game Theory;Behavioral Finance; Sports Betting; Artificial Intelligence; Financial Modeling; Digital Sponsorship
This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread rises with order size, strike, maturity, and estimated volatility, and falls with trading volume. By underlying, wrapped Bitcoin options show larger and more persistent spreads, while Ethereum options are closer to the benchmark. The framework offers a data-driven analysis for monitoring and calibrating on-chain option pricing logic.