Common Noise by Random Measures: Constructing Mean-Field Equilibria for Competitive Investment and Hedging

TL;DR

This paper constructs and analyzes Nash-equilibria in mean-field portfolio games with common noise modeled by random measures, providing existence, uniqueness, and explicit construction methods for the equilibria.

Contribution

It introduces a novel framework for mean-field portfolio games with common noise via jump processes, proving existence and uniqueness of equilibria without small interaction assumptions.

Findings

Existence and uniqueness of solutions to McKean-Vlasov BSDEs with jumps.

Equilibrium strategies can be derived from single-agent optimization problems.

A limiting quadratic hedging game emerges as risk aversion vanishes.

Abstract

We construct Nash-equilibria in mean-field portfolio games of optimal investment and hedging under relative performance concerns with exponential (CARA) utility preferences. Common noise dynamics are modeled by integer-valued random measures, for instance Poisson random measures, in addition to Brownian motions. Agents differ in individual risk aversions, competition weights, and initial capital endowments, while their contingent claim liabilities depend on both common and idiosyncratic risk factors. Mean-field equilibria are characterized by solutions to McKean-Vlasov backward stochastic differential equations with jumps, for which we prove existence and uniqueness of solutions, without assuming mean field interaction to be small. Moreover, we show how the equilibrium can be constructed from the optimal strategy of a single-agent optimization problem (without mean-field interaction) via an appropriate projection. Using successive changes of measure, our derivation provides a decomposition of the equilibrium strategy into three components with clear interpretations. Finally, we show how a limiting mean-field game of quadratic (instead of utility-based) hedging with relative performance concerns arises for vanishing risk aversion.