Work

Robust Mean Field Linear-Quadratic-Gaussian Games with Unknown $L^2$-Disturbance

Year: 2017

Type: article

Abstract: This paper considers a class of mean field linear-quadratic-Gaussian games with model uncertainty. The drift term in the dynamics of the agents contains a common unknown function. We take a robust optimization approach where a representative agent in the limiting model views the drift uncertainty as an adversarial player. By including the mean field dynamics in an augmented state space, we solve two optimal control problems sequentially, which combined with consistent mean field approximations provides a solution to the robust game. A set of decentralized control strategies is derived by use of forward-backward stochastic differential equations and is shown to be a robust $\varepsilon$-Nash equilibrium.

Source: SIAM Journal on Control and Optimization

DOI: 10.1137/15m1014437

Authors: Jianhui Huang, Minyi Huang

Institutions: Hong Kong Polytechnic University, Carleton University

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funders: Research Grants Council, University Grants Committee, Hong Kong Polytechnic University, Natural Sciences and Engineering Research Council of Canada

Awards: G7172442285, G8969244820