4 Search Results for "Rezabek, Filip"

Document
DOI: 10.4230/LIPIcs.AFT.2025.27
Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets

Authors: Oriol Saguillo, Vahid Ghafouri, Lucianna Kiffer, and Guillermo Suarez-Tangil

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
Polymarket is a prediction market platform where users can speculate on future events by trading shares tied to specific outcomes, known as conditions. Each market on Polymarket is associated with a set of one or more such conditions. To ensure proper market resolution, the condition set must be exhaustive - collectively accounting for all possible outcomes - and mutually exclusive - only one condition may resolve as true. Thus, the collective prices (probabilities) of all related outcomes (whether in a condition or market) should be $1, representing a combined probability of 1 of any outcome. Despite this design, Polymarket exhibits cases where dependent assets are mispriced, allowing for purchasing (or selling) a certain outcome for less than (or more than) $1, guaranteeing profit. This phenomenon, known as arbitrage, could enable sophisticated participants to exploit such inconsistencies. In this paper, we conduct an empirical arbitrage analysis on Polymarket data to answer three key questions: (Q1) What conditions give rise to arbitrage? (Q2) Does arbitrage actually occur on Polymarket?, and (Q3) Has anyone exploited these opportunities? A major challenge in analyzing arbitrage between related markets lies in the scalability of comparisons across a large number of markets and conditions, with a naive analysis requiring O(2^{n+m}) comparisons. To overcome this, we employ a heuristic-driven reduction strategy based on timeliness, topical similarity, and combinatorial relationships, further validated by expert input. Our study reveals two distinct forms of arbitrage on Polymarket: Market Rebalancing Arbitrage, which occurs within a single market or condition (intra-market), and Combinatorial Arbitrage, which spans across multiple markets (inter-market). We use on-chain historical order book data to analyze when these types of arbitrage opportunities have existed, and when they have been executed by users. We find a realized estimate of 40 million USD of profit extracted across both types of arbitrage during our measurement period.
Cite as

Oriol Saguillo, Vahid Ghafouri, Lucianna Kiffer, and Guillermo Suarez-Tangil. Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 27:1-27:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{saguillo_et_al:LIPIcs.AFT.2025.27,
  author =	{Saguillo, Oriol and Ghafouri, Vahid and Kiffer, Lucianna and Suarez-Tangil, Guillermo},
  title =	{{Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{27:1--27:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.27},
  URN =		{urn:nbn:de:0030-drops-247468},
  doi =		{10.4230/LIPIcs.AFT.2025.27},
  annote =	{Keywords: Prediction Markets, Maximal Extractable Value, Large Language Models}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.28
Optimistic MEV in Ethereum Layer 2s: Why Blockspace Is Always in Demand

Authors: Ozan Solmaz, Lioba Heimbach, Yann Vonlanthen, and Roger Wattenhofer

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
Layer 2 rollups are rapidly absorbing DeFi activity, securing over $40 billion and accounting for nearly half of Ethereum’s DEX volume by Q1 2025, yet their MEV dynamics remain understudied. We address this gap by defining and quantifying optimistic MEV, a form of speculative, on-chain MEV whose detection and execution logic reside largely on-chain in smart contracts. As a result of their speculative nature and lack of off-chain opportunity verification, optimistic MEV transactions frequently decide not to execute any trades. In this work, we focus on cyclic arbitrage, which we find is predominantly executed as optimistic MEV on Layer 2s. Using our multi-stage identification pipeline on Arbitrum, Base, and Optimism, we show that in Q1 2025, transactions from cyclic arbitrage contracts account for over 50% of on-chain gas on Base and Optimism and 7% on Arbitrum, driven mainly by "interaction" probes (on-chain computations searching for arbitrage). This speculative probing indicates that cyclic arbitrage on Layer 2s is predominantly executed as optimistic MEV and contributes to generally keeping blocks on Base and Optimism persistently full. Despite consuming over half of on-chain gas, these optimistic MEV transactions pay less than one quarter of total gas fees. Cross-network comparison reveals divergent success rates, differing patterns of code reuse, and sensitivity to varying sequencer ordering and block production times. Finally, OLS regressions link optimistic MEV trade count to ETH volatility, retail trading activity, and DEX aggregator usage. Together, these findings show that optimistic MEV has become a major source of persistent spam-like transaction activity on Layer 2s, dominating blockspace with low-value probes and reshaping the composition of on-chain activity.
Cite as

Ozan Solmaz, Lioba Heimbach, Yann Vonlanthen, and Roger Wattenhofer. Optimistic MEV in Ethereum Layer 2s: Why Blockspace Is Always in Demand. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 28:1-28:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{solmaz_et_al:LIPIcs.AFT.2025.28,
  author =	{Solmaz, Ozan and Heimbach, Lioba and Vonlanthen, Yann and Wattenhofer, Roger},
  title =	{{Optimistic MEV in Ethereum Layer 2s: Why Blockspace Is Always in Demand}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{28:1--28:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.28},
  URN =		{urn:nbn:de:0030-drops-247479},
  doi =		{10.4230/LIPIcs.AFT.2025.28},
  annote =	{Keywords: blockchain, MEV, Layer 2, Ethereum}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.26
Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest

Authors: Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
This paper provides a comprehensive empirical analysis of the economics and dynamics behind arbitrages between centralized and decentralized exchanges (CEX-DEX) on Ethereum. We refine heuristics to identify arbitrage transactions from on-chain data and introduce a robust empirical framework to estimate arbitrage revenue without knowing traders' actual behaviors on CEX. Leveraging an extensive dataset spanning 19 months from August 2023 to March 2025, we estimate a total of 233.8M USD extracted by 19 major CEX-DEX searchers from 7,203,560 identified CEX-DEX arbitrages. Our analysis reveals increasing centralization trends as three searchers captured three-quarters of both volume and extracted value. We also demonstrate that searchers' profitability is tied to their integration level with block builders and uncover exclusive searcher-builder relationships and their market impact. Finally, we correct the previously underestimated profitability of block builders who vertically integrate with a searcher. These insights illuminate the darkest corner of the MEV landscape and highlight the critical implications for Ethereum’s decentralization.
Cite as

Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai. Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 26:1-26:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wu_et_al:LIPIcs.AFT.2025.26,
  author =	{Wu, Fei and Sui, Danning and Thiery, Thomas and Pai, Mallesh},
  title =	{{Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{26:1--26:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.26},
  URN =		{urn:nbn:de:0030-drops-247450},
  doi =		{10.4230/LIPIcs.AFT.2025.26},
  annote =	{Keywords: Decentralized Finance, Maximal Extractable Value, CEX-DEX arbitrages}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2020.10
Cybersecurity Games for Secure Programming Education in the Industry: Gameplay Analysis

Authors: Tiago Gasiba, Ulrike Lechner, Filip Rezabek, and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 81, First International Computer Programming Education Conference (ICPEC 2020)

Abstract
To minimize the possibility of introducing vulnerabilities in source code, software developers may attend security awareness and secure coding training. From the various approaches of how to raise awareness and adherence to coding standards, one promising novel approach is Cybersecurity Challenges. However, in an industrial setting, time is a precious resource, and, therefore, one needs to understand how to optimize the gaming experience of Cybersecurity Challenges and the effect of this game on secure coding skills. This work identifies the time spent solving challenges of different categories, analyzes gaming strategies in terms of a slow and fast team profile, and relates these profiles to the game success. First results indicate that the slow strategy is more successful than the fast approach. The authors also analyze the possible implications in the design and the training of secure coding in an industrial setting by means of Cybersecurity Challenges. This work concludes with a brief overview of its limitations and next steps in the study.
Cite as

Tiago Gasiba, Ulrike Lechner, Filip Rezabek, and Maria Pinto-Albuquerque. Cybersecurity Games for Secure Programming Education in the Industry: Gameplay Analysis. In First International Computer Programming Education Conference (ICPEC 2020). Open Access Series in Informatics (OASIcs), Volume 81, pp. 10:1-10:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{gasiba_et_al:OASIcs.ICPEC.2020.10,
  author =	{Gasiba, Tiago and Lechner, Ulrike and Rezabek, Filip and Pinto-Albuquerque, Maria},
  title =	{{Cybersecurity Games for Secure Programming Education in the Industry: Gameplay Analysis}},
  booktitle =	{First International Computer Programming Education Conference (ICPEC 2020)},
  pages =	{10:1--10:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-153-5},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{81},
  editor =	{Queir\'{o}s, Ricardo and Portela, Filipe and Pinto, M\'{a}rio and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2020.10},
  URN =		{urn:nbn:de:0030-drops-122977},
  doi =		{10.4230/OASIcs.ICPEC.2020.10},
  annote =	{Keywords: education, training, secure coding, industry, cybersecurity, capture-the-flag, game analysis, cybersecurity challenge}
}
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