Funding-Aware Optimal Market Making for Perpetual DEXs

TL;DR

This paper develops a funding-aware market making model for perpetual contracts, integrating stochastic funding rates into optimal liquidity provision, and demonstrates improved performance over classical models in simulations.

Contribution

It introduces a novel control framework coupling inventory and funding payments, solved via a finite-difference HJB scheme, with calibration on real perpetual data.

Findings

Funding-aware HJB improves ETH/BTC trading performance.

Model captures heavy-tailed funding innovations with OU-plus-jump diagnostics.

Simulation results show lower inventory RMS and better mean returns compared to classical models.

Abstract

This paper studies optimal liquidity provision for perpetual contracts when the funding rate is a stochastic state variable. The core extension to classical market making is the coupling between inventory and funding payments: inventory creates both mark-to-market exposure and a state-dependent funding cash flow. A reduced inventory-funding control problem is formulated, solved with a monotone finite-difference Hamilton-Jacobi-Bellman scheme, and bid and ask quote offsets are recovered from discrete inventory value differences. Funding is calibrated on Hyperliquid ETH, BTC, and SOL perpetual data. Gaussian OU funding is retained as a tractable diffusion baseline, while OU-plus-jump diagnostics document the heavy-tailed funding innovations that should enter a future extension. In 100-seed holdout simulations under two official-fill proxy calibrations, the funding-aware HJB improves mean ETH/BTC performance while lowering inventory RMS relative to classical Avellaneda-Stoikov. SOL gains are positive versus unscaled AS but are not a Pareto improvement once a risk-scaled AS diagnostic is included.