Control of McKean--Vlasov SDEs with Contagion Through Killing at a State-Dependent Intensity

TL;DR

This paper introduces a new control framework for McKean--Vlasov SDEs with contagion effects through particle killing, providing theoretical foundations and numerical insights for systemic risk management.

Contribution

It rigorously derives the McKean--Vlasov control problem as a limit of finite particle systems and links it to models with boundary hitting times, extending existing theory.

Findings

Established existence of solutions for McKean--Vlasov SDEs with singular interactions.

Connected particle killing models with boundary hitting time models in the limit.

Provided numerical analysis for systemic risk control in financial systems.

Abstract

We consider a novel McKean--Vlasov control problem with contagion through killing of particles and common noise. Each particle is killed at an exponential rate according to an intensity process that increases whenever the particle is located in a specific region. The removal of a particle pushes others towards the removal region, which can trigger cascades that see particles exiting the system in rapid succession. We study the control of such a system by a central agent who intends to preserve particles at minimal cost. Our theoretical contribution is twofold. Firstly, we rigorously justify the McKean--Vlasov control problem as the limit of a corresponding sequences of controlled finite particle systems. Our proof is based on a controlled martingale problem and tightness arguments. Secondly, we connect our framework with models in which particles are killed once they hit the boundary of the removal region. We show that these models appear in the limit as the exponential rate tends to infinity. As a corollary, we obtain new existence results for McKean--Vlasov SDEs with singular interaction through hitting times which extend those in the established literature. We conclude the paper with numerical investigations of our model applied to government control of systemic risk in financial systems.