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arxiv: 2508.07068 · v2 · submitted 2025-08-09 · 💱 q-fin.CP · q-fin.MF

Proactive Market Making and Liquidity Analysis for Everlasting Options in DeFi Ecosystems

Hardhik Mohanty , Giovanni Zaarour , Bhaskar Krishnamachari This is my paper

Pith reviewed 2026-05-19 00:28 UTC · model grok-4.3

classification 💱 q-fin.CP q-fin.MF
keywords everlasting optionsDeFimarket makingliquidity providershedgingfunding feestransaction costsproactive market maker
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The pith

Liquidity providers in everlasting options can achieve net positive PnL through hedging even under low liquidity and high transaction costs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper models markets for everlasting options, a class of perpetual derivatives meant to sidestep contract rolls and fragmented liquidity in DeFi. It uses a dynamic proactive market maker to track funding fees and costs at different liquidity levels. Simulations show that providers can still reach net positive profits by hedging, even when liquidity is thin and fees are high. A sympathetic reader would care because the result points to practical ways to sustain liquidity in these new instruments and clarifies the incentives that encourage providers to participate.

Core claim

Using a dynamic proactive market maker model, the paper demonstrates through simulations that liquidity providers for everlasting options can target net positive profit and loss by applying effective hedging strategies, even in settings with low liquidity and elevated transaction costs, while also identifying incentives for providers to support market growth and benefits for traders seeking reliable long-term exposure.

What carries the argument

The dynamic proactive market maker model, which simulates order flow, funding fees, and cost structures to evaluate hedging performance across liquidity regimes.

If this is right

  • Providers have clear financial incentives to supply liquidity to everlasting options markets.
  • Traders gain access to reliable, efficient perpetual exposure without repeated contract rolls.
  • Markets for these instruments can remain viable even when liquidity is scarce and fees are high.
  • Hedging offsets can make provider participation profitable rather than loss-making.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar proactive models could be tested on other perpetual DeFi products such as perpetual futures to check whether hedging gains generalize.
  • Real-market validation would require comparing simulated PnL curves against actual provider returns on platforms that already list everlasting options.
  • The framework suggests liquidity providers might adjust hedge ratios dynamically as liquidity varies, an extension left for future calibration.

Load-bearing premise

The simulation parameters and dynamic proactive market maker setup accurately capture real DeFi trading behavior for everlasting options.

What would settle it

Running the same hedging rules on live order-book data from an existing DeFi everlasting-options venue and checking whether observed provider PnL turns positive at the simulated liquidity and cost levels.

Figures

Figures reproduced from arXiv: 2508.07068 by Bhaskar Krishnamachari, Giovanni Zaarour, Hardhik Mohanty.

Figure 1
Figure 1. Figure 1: Slippage: Everlasting vs. Fixed-Expiration [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Behavior of funding fees with different liquidity levels across [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Histograms of Final PnL (Normalized by Q0) for Different Liquidity Levels. around positive PnL values. This indicates that the DPMM combined with ∆-hedging strategy success￾fully minimizes tail risks while maintaining profitabil￾ity. As Q0 increases, both the mean and median PnL values improve, as depicted by the vertical dashed lines in each histogram plot. To further support our claims, we present result… view at source ↗
read the original abstract

Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic proactive market maker. We examine the behavior of funding fees and transaction costs across varying liquidity conditions. Using simulations and modeling, we demonstrate that liquidity providers can aim to achieve a net positive PnL by employing effective hedging strategies, even in challenging environments characterized by low liquidity and high transaction costs. Additionally, we provide insights into the incentives that drive liquidity providers to support the growth of everlasting option markets and highlight the significant benefits these instruments offer to traders as a reliable and efficient financial tool.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper models everlasting options in DeFi using a dynamic proactive market maker. It analyzes funding fees and transaction costs across liquidity regimes and employs simulations to claim that liquidity providers can realize net positive PnL via hedging strategies even under low liquidity and high transaction costs. The work also discusses LP incentives and trader benefits.

Significance. If the simulation framework is shown to be calibrated and robust, the results would offer practical guidance on profitable liquidity provision for perpetual-style derivatives in DeFi, addressing a gap in understanding market-making incentives under realistic frictions. The proactive MM approach and focus on everlasting options are timely for computational finance.

major comments (2)
  1. [§4] §4 (Simulation Methodology): The central positive-PnL claim for LPs relies on simulations whose parameter choices for liquidity depth, slippage, funding rates, and volatility are not calibrated to historical on-chain data or subjected to reported sensitivity sweeps; without these, the net-positive outcome under high costs cannot be distinguished from an artifact of stylized inputs.
  2. [§4.3] §4.3 (Hedging and PnL Results): The hedging strategy equations and exact PnL decomposition (including adverse-selection and jump components) are not provided, so it is impossible to verify whether the reported gains survive realistic crypto price dynamics omitted from the model.
minor comments (2)
  1. [§3] Notation for the proactive market-maker update rule is introduced without a clear reference to prior literature on constant-product or concentrated-liquidity AMMs.
  2. [Figure 2] Figure captions for the liquidity-condition plots do not state the exact parameter values used in each panel.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback, which highlights important aspects for improving the robustness of our simulation results. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core contributions.

read point-by-point responses

  1. Referee: [§4] §4 (Simulation Methodology): The central positive-PnL claim for LPs relies on simulations whose parameter choices for liquidity depth, slippage, funding rates, and volatility are not calibrated to historical on-chain data or subjected to reported sensitivity sweeps; without these, the net-positive outcome under high costs cannot be distinguished from an artifact of stylized inputs.

    Authors: We agree that direct calibration to historical on-chain data and explicit sensitivity sweeps would enhance credibility. Our parameter choices were selected to represent a range of realistic DeFi liquidity regimes drawn from observed market characteristics, but we did not perform formal calibration or report sweeps in the original submission. In the revision we will add a dedicated sensitivity analysis subsection, including sweeps over liquidity depth, slippage, funding rates, and volatility, and will reference publicly available on-chain metrics to justify baseline values. This will allow readers to assess whether the net-positive PnL persists under varied inputs. revision: yes

  2. Referee: [§4.3] §4.3 (Hedging and PnL Results): The hedging strategy equations and exact PnL decomposition (including adverse-selection and jump components) are not provided, so it is impossible to verify whether the reported gains survive realistic crypto price dynamics omitted from the model.

    Authors: We acknowledge that the explicit hedging strategy equations and the full PnL decomposition (separating adverse-selection, jump, and other components) were presented at a high level rather than in complete mathematical form. The underlying proactive market-making model does incorporate these elements, but the manuscript omitted the detailed derivations and component-wise breakdown. In the revised version we will supply the complete hedging equations, the exact PnL decomposition formula, and additional simulation results that include jump processes and other realistic crypto price dynamics to demonstrate robustness. revision: yes

Circularity Check

0 steps flagged

Simulation-based PnL results are independent outputs with no reduction to fitted inputs or self-citations

full rationale

The paper presents a dynamic proactive market maker model for everlasting options and reports net positive LP PnL from simulations under varying liquidity and cost regimes. No equations, parameter-fitting steps, or self-citation chains are shown that define the target PnL outcome in terms of itself or force it by construction. The modeling choices (funding fees, slippage, volatility) are described as inputs to the simulation rather than outputs derived from the claimed result. This is a standard simulation study whose central claim remains falsifiable against external DeFi data and does not reduce to tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis depends on unstated modeling assumptions for the proactive market maker and simulation setup; no explicit free parameters, axioms, or invented entities are identifiable from the abstract alone.

axioms (1)
  • domain assumption The dynamic proactive market maker model accurately captures DeFi market dynamics for everlasting options.
    This modeling choice underpins the examination of funding fees, transaction costs, and PnL outcomes across liquidity conditions.

pith-pipeline@v0.9.0 · 5655 in / 1275 out tokens · 83322 ms · 2026-05-19T00:28:33.689437+00:00 · methodology

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Reference graph

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