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Axient: On-Chain Credit and Loss Allocation for Leveraged Event Markets: A Venue-Agnostic Protocol for Traders, Credit Providers, Market Makers, and Liquidation Backstops

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  • Maksym Nechepurenko

Abstract

A physically backed leveraged event position requires real credit: if collateral C receives leverage L, the protocol supplies (L-1)C and uses the combined amount to acquire recognized event exposure. This paper develops a venue-agnostic on-chain credit architecture for that capital layer and an endogenous model of its capital market. It separates traders, Senior Credit LPs, market makers, liquidators, and Liquidation Backstop Providers; formalizes pool and debt shares, utilization- and risk-sensitive interest, collateral-locked position accounts, venue capabilities, market-maker commitments, withdrawal queues, isolated pools, non-redeemable reserves, and a deterministic loss waterfall; and models endogenous provider participation, leverage demand, liquidity withdrawal, liquidator entry, reserve replenishment, runs, and common-factor contagion. Formal results establish balanced real- and integer-unit accounting, settlement-confirmed debt priority, trader residual ownership, idempotent partial settlement, non-dilutive share issuance, junior-before-Senior impairment, loss-participating withdrawal queues, utilization-equilibrium conditions, and loss-allocation and contagion bounds. The release preserves 28 exact fixtures and 31,082 deterministic checks and adds fixed-seed agent-based experiments with 70,207,488 scalar invariant evaluations and zero failures. The experiments show that layered protection reduces but does not eliminate Senior loss, that 5x leverage materially increases capital pressure, and that market-maker capacity can raise aggregate shortfall if admission expands too quickly. Results are synthetic mechanism comparisons under author-specified behavior, not forecasts of APY, defaults, venue liquidity, or production safety.

Suggested Citation

  • Maksym Nechepurenko, 2026. "Axient: On-Chain Credit and Loss Allocation for Leveraged Event Markets: A Venue-Agnostic Protocol for Traders, Credit Providers, Market Makers, and Liquidation Backstops," Papers 2608.00647, arXiv.org.
  • Handle: RePEc:arx:papers:2608.00647
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    1. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    2. Kaihua Qin & Jens Ernstberger & Liyi Zhou & Philipp Jovanovic & Arthur Gervais, 2023. "Mitigating Decentralized Finance Liquidations with Reversible Call Options," Papers 2303.15162, arXiv.org, revised Mar 2023.
    3. Agathe Sadeghi & Zachary Feinstein, 2026. "Liquidation Dynamics in DeFi and the Role of Transaction Fees," Papers 2602.12104, arXiv.org.
    4. Prasanna Gai & Sujit Kapadia, 2010. "Contagion in financial networks," Bank of England Staff Working Paper series 383, Bank of England.
    5. Pietro Fodra & Mauricio Labadie, 2012. "High-frequency market-making with inventory constraints and directional bets," Papers 1206.4810, arXiv.org.
    6. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    7. Douglas W. Diamond & Philip H. Dybvig, 2000. "Bank runs, deposit insurance, and liquidity," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 24(Win), pages 14-23.
    8. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    9. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    10. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    11. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    12. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    13. Pietro Fodra & Mauricio Labadie, 2012. "High-frequency market-making with inventory constraints and directional bets," Working Papers hal-00675925, HAL.
    14. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    15. Amit Chaudhary & Daniele Pinna, 2022. "A multi-asset, agent-based approach applied to DeFi lending protocol modelling," Papers 2211.08870, arXiv.org, revised Dec 2022.
    16. Larry Eisenberg & Thomas H. Noe, 2001. "Systemic Risk in Financial Systems," Management Science, INFORMS, vol. 47(2), pages 236-249, February.
    17. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
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