arXiv subjects

Jonas Gebele

Publications and source records attributed to Jonas Gebele.

8 recordsLinked to original sources

arXiv2026

Executable Arbitrage and Market Efficiency in Prediction Markets

Deterministic payoff identities imply no-arbitrage bounds in winner-takes-all prediction markets, but violations of these bounds need not be exploitable before settlement. We distinguish payoff-space no-arbitrage, which follows from terminal payoffs, from protocol-executable no-arbitrage, which depends on the position transformations available to traders. Polymarket's negative-risk markets make this distinction observable: linked binary markets represent mutually exclusive outcomes, while the NegRisk Adapter operationalizes only the NO-to-YES direction before settlement. We reconstruct depth-aware executable portfolio values and combine them with actor-level transaction histories and on-chain conversion traces to measure payoff-bound violations and exploitation. Our reconstruction estimates \$1.12 million in arbitrage profit across two realization channels: \$1.086 million from converter-enabled strategies and \$32 thousand from settlement-based basket formation. In the CLOB sample, positive violations concentrate on the unsupported YES side, whereas adapter-supported NO-side violations are substantially less frequent and shorter-lived. These patterns are consistent with the view that pre-settlement conversion strengthens enforcement by reducing capital lock-up and enabling inventory recycling. Finally, we implement a prototype bidirectional extension of the NegRisk Adapter that makes the reverse path executable before settlement. Together, our findings show that market efficiency depends not only on payoff structure, but also on whether protocols expose payoff equivalences as executable primitives.

cs.CE↗

arXiv2026

When Certainty Is Not Worth It: Capital Lock-Up and Settlement Discounting in Prediction Markets

Collateralized prediction markets are contingent-claim markets in which economic uncertainty can disappear before winning claims become redeemable. This paper studies the pricing effect of that delay. When collateral remains locked until oracle settlement, a near-certain dollar is a delayed dollar, so prices embed a maturity-dependent settlement discount in addition to beliefs about outcomes. We recover an implied settlement-discount term structure from persistent near-certain contracts using realized settlement times and summarize it as an annualized settlement wedge (ASW). The recovered wedges are positive, maturity-dependent, and time-varying. Adjusting pricesby these curves reduces the near-certainty horizon gradient by roughly 48-88%, indicating that much of the raw maturity pattern reflects priced settlement frictions rather than forecast error alone. Market architecture changes the wedge: negRisk conversion compresses discounts by recycling part of the position into synthetic collateral, while yield-bearing collateral flattens the term structure by reducing the opportunity cost of lock-up. The results show that pricing quality in prediction markets is endogenous to settlement mechanics, collateral productivity, and capital-recycling design. Prediction-market prices therefore aggregate information through a financial infrastructure whose funding conditions are measurable and economically important.

cs.CE↗

arXiv2026

Semantic Non-Fungibility and Violations of the Law of One Price in Prediction Markets

Prediction markets are designed to aggregate dispersed information about future events, yet today's ecosystem is fragmented across heterogeneous operator-run platforms and blockchain-based protocols that independently list economically identical events. In the absence of a shared notion of event identity, liquidity fails to pool across venues, arbitrage becomes capital-intensive or unenforceable, and prices systematically violate the Law of One Price. As a result, market prices reflect platform-local beliefs rather than a single, globally aggregated probability, undermining the core information-aggregation function of prediction markets. We address this gap by introducing a semantic alignment framework that makes cross-platform event identity explicit through joint analysis of natural-language descriptions, resolution semantics, and temporal scope. Applying this framework, we construct the first human-validated, cross-platform dataset of aligned prediction markets, covering over 100 000 events across ten major venues from 2018 to 2025. Using this dataset, we show that roughly 6% of all events are concurrently listed across platforms and that semantically equivalent markets exhibit persistent execution-aware price deviations of 2-4% on average, even in highly liquid and information-rich settings. These mispricings give rise to persistent cross-platform arbitrage opportunities driven by structural frictions rather than informational disagreement. Overall, our results demonstrate that semantic non-fungibility is a fundamental barrier to price convergence, and that resolving event identity is a prerequisite for prediction markets to aggregate information at a global scale.

cs.CE↗

arXiv2025

Cross-Chain Sealed-Bid Auctions Using Confidential Compute Blockchains

Sealed-bid auctions ensure fair competition and efficient allocation but are often deployed on centralized infrastructure, enabling opaque manipulation. Public blockchains eliminate central control, yet their inherent transparency conflicts with the confidentiality required for sealed bidding. Prior attempts struggle to reconcile privacy, verifiability, and scalability without relying on trusted intermediaries, multi-round protocols, or expensive cryptography. We present a sealed-bid auction protocol that executes sensitive bidding logic on a Trusted Execution Environment (TEE)-backed confidential compute blockchain while retaining settlement and enforcement on a public chain. Bidders commit funds to enclave-generated escrow addresses, ensuring confidentiality and binding commitments. After the deadline, any party can trigger resolution: the confidential blockchain determines the winner through verifiable off-chain computation and issues signed settlement transactions for execution on the public chain. Our design provides security, privacy, and scalability without trusted third parties or protocol modifications. We implement it on SUAVE with Ethereum settlement, evaluate its scalability and trust assumptions, and demonstrate deployment with minimal integration on existing infrastructure

cs.CR↗

arXiv2025

CRSet: Private Non-Interactive Verifiable Credential Revocation

Like any digital certificate, Verifiable Credentials (VCs) require a way to revoke them in case of an error or key compromise. Existing solutions for VC revocation, most prominently Bitstring Status List, are not viable for many use cases because they may leak the issuer's activity, which in turn leaks internal business metrics. For instance, staff fluctuation through the revocation of employee IDs. We identify the protection of issuer activity as a key gap and propose a formal definition for a corresponding characteristic of a revocation mechanism. Then, we introduce CRSet, a non-interactive mechanism that trades some space efficiency to reach these privacy characteristics. For that, we provide a proof sketch. Issuers periodically encode revocation data and publish it via Ethereum blob-carrying transactions, ensuring secure and private availability. Relying Parties (RPs) can download it to perform revocation checks locally. Sticking to a non-interactive design also makes adoption easier because it requires no changes to wallet agents and exchange protocols. We also implement and empirically evaluate CRSet, finding its real-world behavior to match expectations. One Ethereum blob fits revocation data for about 170k VCs.

cs.CR↗

arXiv2025

Cross-Chain Arbitrage: The Next Frontier of MEV in Decentralized Finance

Decentralized finance (DeFi) markets spread across Layer-1 (L1) and Layer-2 (L2) blockchains rely on arbitrage to keep prices aligned. Today most price gaps are closed against centralized exchanges (CEXes), whose deep liquidity and fast execution make them the primary venue for price discovery. As trading volume migrates on-chain, cross-chain arbitrage between decentralized exchanges (DEXes) will become the canonical mechanism for price alignment. Yet, despite its importance to DeFi-and the on-chain transparency making real activity tractable in a way CEX-to-DEX arbitrage is not-existing research remains confined to conceptual overviews and hypothetical opportunity analyses. We study cross-chain arbitrage with a profit-cost model and a year-long measurement. The model shows that opportunity frequency, bridging time, and token depreciation determine whether inventory- or bridge-based execution is more profitable. Empirically, we analyze one year of transactions (September 2023 - August 2024) across nine blockchains and identify 242,535 executed arbitrages totaling 868.64 million USD volume. Activity clusters on Ethereum-centric L1-L2 pairs, grows 5.5x over the study period, and surges-higher volume, more trades, lower fees-after the Dencun upgrade (March 13, 2024). Most trades use pre-positioned inventory (66.96%) and settle in 9s, whereas bridge-based arbitrages take 242s, underscoring the latency cost of today's bridges. Market concentration is high: the five largest addresses execute more than half of all trades, and one alone captures almost 40% of daily volume post-Dencun. We conclude that cross-chain arbitrage fosters vertical integration, centralizing sequencing infrastructure and economic power and thereby exacerbating censorship, liveness, and finality risks; decentralizing block building and lowering entry barriers are critical to countering these threats.

cs.CR↗

arXiv2024

Playing the MEV Game on a First-Come-First-Served Blockchain

Maximal Extractable Value (MEV) searching has gained prominence on the Ethereum blockchain since the surge in Decentralized Finance activities. In Ethereum, MEV extraction primarily hinges on fee payments to block proposers. However, in First-Come-First-Served (FCFS) blockchain networks, the focus shifts to latency optimizations, akin to High-Frequency Trading in Traditional Finance. This paper illustrates the dynamics of the MEV extraction game in an FCFS network, specifically Algorand. We introduce an arbitrage detection algorithm tailored to the unique time constraints of FCFS networks and assess its effectiveness. Additionally, our experiments investigate potential optimizations in Algorand's network layer to secure optimal execution positions. Our analysis reveals that while the states of relevant trading pools are updated approximately every six blocks on median, pursuing MEV at the block state level is not viable on Algorand, as arbitrage opportunities are typically executed within the blocks they appear. Our algorithm's performance under varying time constraints underscores the importance of timing in arbitrage discovery. Furthermore, our network-level experiments identify critical transaction prioritization strategies for Algorand's FCFS network. Key among these is reducing latency in connections with relays that are well-connected to high-staked proposers.

cs.CR↗

arXiv2023

A Study of MEV Extraction Techniques on a First-Come-First-Served Blockchain

Maximal Extractable Value (MEV) has become a significant incentive on blockchain networks, referring to the value captured through the manipulation of transaction execution order and strategic issuance of profit-generation transactions. We argue that transaction ordering techniques used for MEV extraction in blockchains where fees can influence the execution order do not directly apply to blockchains where the order is determined based on transactions' arrival times. Such blockchains' First-Come-First-Served (FCFS) nature can yield different optimization strategies for entities seeking MEV, known as searchers, requiring further study. This paper explores the applicability of MEV extraction techniques observed on Ethereum, a fee-based blockchain, to Algorand, an FCFS blockchain. Our results show the prevalence of arbitrage MEV getting extracted through backruns on pending transactions in the network, uniformly distributed to block positions. However, on-chain data do not reveal latency optimizations between specific MEV searchers and Algorand block proposers. We also study network clogging attacks and argue how searchers can exploit them as a viable ordering technique for MEV extraction in FCFS networks.

cs.CR↗