Subgame-perfect equilibrium strategies for time-inconsistent recursive stochastic control problems

TL;DR

This paper develops a game-theoretic framework using the stochastic maximum principle to find subgame-perfect equilibrium strategies for time-inconsistent recursive stochastic control problems, with applications in finance.

Contribution

It introduces a novel approach combining the stochastic maximum principle with game theory to characterize equilibrium strategies in time-inconsistent control problems.

Findings

Characterization of equilibrium strategies via a generalized Hamiltonian function.

Existence of non-trivial equilibrium policies in financial investment problems.

Extension of the method to multidimensional cases.

Abstract

We study time-inconsistent recursive stochastic control problems, i.e., for which the Bellman principle of optimality does not hold. For this class of problems classical optimal controls may fail to exist, or to be relevant in practice, and dynamic programming is not easily applicable. Therefore, the notion of optimality is defined through a game-theoretic framework by means of subgame perfect equilibrium: we interpret our preference changes which, realistically, are inconsistent over time, as players in a game for which we want to find a Nash equilibrium. The approach followed in our work relies on the stochastic (Pontryagin) maximum principle: we adapt the classical spike variation technique to obtain a characterisation of equilibrium strategies in terms of a generalised second-order Hamiltonian function defined through pairs of backward stochastic differential equations, even in the multidimensional case. The theoretical results are applied in the financial field to finite horizon investment-consumption policies with non-exponential actualisation. Here the existence of non-trivial equilibrium policies is also ascertained.