Search arXiv⌕ Search

arXiv · 2610.38988

Mean Field Games of Optimal Trading with an SPDE Model of the Limit Order Book

Abstract

We develop a mean field game model for optimal trading in which the interaction among traders is mediated by the dynamics of the limit order book. The bid- and ask-side order densities are modelled as stochastic processes with values in infinite-dimensional function spaces that solve stochastic partial differential equations (SPDEs) with stochastic order submission rates and multiplicative infinite-dimensional noise. This extends an existing limit order book framework to capture the aggregate impact of the strategic traders on the order densities. Trading activity affects the order densities, which determine the market depths near the best quotes and hence the mid-price, and the mid-price in turn affects the traders' decisions. We establish the well-posedness and regularity of the resulting density-price system and use it to formulate an $N$-player optimal trading game in which the traders' market orders enter the book through their aggregate order flow. We then study the corresponding mean field game as the number of traders tends to infinity, characterize the mean field equilibrium as a fixed point of the associated consistency condition, and prove its existence and uniqueness under suitable conditions. Finally, we show that the mean field equilibrium induces an $ε_N$-Nash equilibrium for the finite-player game, with $ε_N=O(N^{-1/2})$, and provide a numerical illustration of the equilibrium dynamics.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hanchao Liu, Dena Firoozi. 2026-10-28. Mean Field Games of Optimal Trading with an SPDE Model of the Limit Order Book. https://arxiv.org/abs/2610.38988

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Consumption-Investment Problem in Rank-Based Models

We study a consumption-investment problem in a multi-asset market where the returns follow a generic rank-based model. Our main results are an HJB equation with Neumann boundary conditions for the value function, together with a corresponding verification theorem. The control problem is nonstandard due to the discontinuous nature of the coefficients in rank-based models, requiring a bespoke approach of independent mathematical interest. The special case of first-order models, prescribing constant drift and diffusion coefficients for the ranked returns, admits explicit solutions when the investor (a) is unconstrained, (b) abides by open market constraints or (c) is fully invested in the market. The explicit optimal strategies in all cases are related to the celebrated solution to Merton's problem, despite case (b) being intractable in the classic Merton setting.

q-fin.MF↗

Optimal Investment and Entropy-Regularized Learning Under Stochastic Volatility Models with Portfolio Constraints

We study the problem of optimal portfolio selection under stochastic volatility within a continuous time reinforcement learning framework with portfolio constraints. Exploration is modeled through entropy-regularized relaxed controls, where the investor selects probability distributions over admissible portfolio allocations rather than deterministic strategies. Using dynamic programming arguments, we derive the associated entropy-regularized Hamilton-Jacobi-Bellman equation, whose Hamiltonian involves optimization over probability measures supported on a compact control set. We show that the optimal exploratory policy takes the form of a truncated Gaussian distribution characterized by spatial derivatives of the solution of the resulting nonlinear quasilinear parabolic partial differential equation. Under suitable structural conditions on the model coefficients, we prove the existence of classical solutions to this nonlinear HJB equation for the value function. We then establish a verification theorem and analyze the policy-improvement structure induced by the entropy-regularized Hamiltonian, showing how the resulting sequence of PDEs provides a continuous-time interpretation of actor-critic learning dynamics. Finally, our PDE analysis with a semi-closed form of optimal value and optimal policy enables the design of an implementable reinforcement learning algorithm by recasting the optimal problem in a martingale framework.

q-fin.MF↗

Model Risk Analysis for Entropic Hedging Strategies

Market incompleteness causes residual profit and loss after hedging that is accounted for with valuation adjustments and bid-offer spreads acting as model risk buffers for the derivative price. Entropic risk optimisation is a general framework for modelling these adjustments. When the prices are quadratic Gaussian, the coupled equations for price and hedge ratios are solved exactly, with a decomposition of the entropic risk into convexity, dimensionality, funding and strategy contributions that are attributed in the explanation of realised P&L. The results of the quadratic Gaussian model are applicable when the underlying prices follow Gaussian processes, such as fractional Ornstein-Uhlenbeck processes. The article concludes with a discussion of dynamic hedging in this model and its application in data-driven regressions.

q-fin.MF↗