Kyle-Back Equilibrium Models and Linear Conditional Mean-field SDEs

TL;DR

This paper develops a rigorous mathematical framework for Kyle-Back insider trading models using conditional mean-field SDEs, establishing well-posedness, deriving explicit strategies, and confirming equilibrium existence.

Contribution

It introduces the first rigorous analysis of Kyle-Back models within the conditional mean-field SDE framework, including new well-posedness results and explicit optimal strategies.

Findings

Proved well-posedness of linear CMFSDEs using reference probability measures.

Derived explicit optimal trading strategies under Gaussian assumptions.

Established existence of Kyle-Back equilibrium in the new framework.

Abstract

In this paper we study the Kyle-Back strategic insider trading equilibrium model in which the insider has an instantaneous information on an asset, assumed to follow an Ornstein-Uhlenback-type dynamics that allows possible influence by the market price. Such a model exhibits some further interplay between insider's information and the market price, and it is the first time being put into a rigorous mathematical framework of the recently developed {\it conditional mean-field} stochastic differential equation (CMFSDEs). With the help of the "reference probability measure" concept in filtering theory, we shall first prove a general well-posedness result for a class of linear CMFSDEs, which is new in the literature of both filtering theory and mean-field SDEs, and will be the foundation for the underlying strategic equilibrium model. Assuming some further Gaussian structures of the model, we find a closed form of optimal intensity of trading strategy as well as the dynamic pricing rules. We shall also substantiate the well-posedness of the resulting optimal closed-loop system, whence the existence of Kyle-Back equilibrium. Our result recovers many existing results as special cases.