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

We study mean-field games where common noise dynamics are described by integer-valued random measures, for instance Poisson random measures, in addition to Brownian motions. In such a framework, we describe Nash equilibria for mean-field portfolio games of both optimal investment and hedging under relative performance concerns with respect to exponential (CARA) utility preferences. Agents have independent individual risk aversions, competition weights and initial capital endowments, whereas their liabilities are described by contingent claims which can depend on both common and idiosyncratic risk factors. Liabilities may incorporate, e.g., compound Poisson-like jump risks and can only be hedged partially by trading in a common but incomplete financial market, in which prices of risky assets evolve as Itô-processes. Mean-field equilibria are fully characterized by solutions to suitable McKean-Vlasov forward-backward SDEs with jumps, for whose we prove existence and uniqueness of solutions, without restricting competition weights to be small. We obtain an explicit decomposition of the mean field equilibrium strategy into three components being related to the investment, the hedging and the mean field interaction part. If time permits, we moreover show numerical examples and discuss, how related results for Nash equilibria among finely many agents are derived by the same original  change-of-measure method. Joint work with Stefanie Hesse, University of Oxford