Optimal hedging of an informed broker facing many traders

ArXiv ID: 2506.08992 “View on arXiv”

Authors: Philippe Bergault, Pierre Cardaliaguet, Wenbin Yan

Abstract

This paper investigates the optimal hedging strategies of an informed broker interacting with multiple traders in a financial market. We develop a theoretical framework in which the broker, possessing exclusive information about the drift of the asset’s price, engages with traders whose trading activities impact the market price. Using a mean-field game approach, we derive the equilibrium strategies for both the broker and the traders, illustrating the intricate dynamics of their interactions. The broker’s optimal strategy involves a Stackelberg equilibrium, where the broker leads and the traders follow. Our analysis also addresses the mean field limit of finite-player models and shows the convergence to the mean-field solution as the number of traders becomes large.

Keywords: optimal hedging, mean-field games, Stackelberg equilibrium, informed broker, market impact

Complexity vs Empirical Score

• Math Complexity: 9.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced mathematical tools such as mean-field games, Stackelberg equilibria, backward HJ equations, and convergence proofs, indicating very high mathematical complexity. However, it lacks any empirical backtesting, code, or real-world data implementation, with the excerpt focusing solely on theoretical model derivation and existence proofs.

  flowchart TD
    A["Research Goal: Optimal hedging for an informed broker with market impact"] --> B["Methodology: Mean-field game & Stackelberg equilibrium"]
    B --> C["Inputs: Broker's private info & trader interactions"]
    C --> D["Process: Finite-player model & mean-field limit"]
    D --> E["Computation: Deriving equilibrium strategies"]
    E --> F["Outcome: Optimal hedging strategy & convergence proof"]