Conditional Analysis and a Principal-Agent problem

TL;DR

This paper explores the general structure of optimal contracts in discrete-time Principal-Agent problems with linear contracts, revealing the necessity of derivatives in compensation schemes under broad conditions.

Contribution

It extends existing results to more general utility functions and probabilistic settings, demonstrating the importance of derivatives in optimal contracts.

Findings

Optimal contracts often require derivatives for compensation.

Results generalize previous specific-case findings.

Applicable to a wide range of utility functions and probabilistic models.

Abstract

We analyze conditional optimization problems arising in discrete time Principal-Agent problems of delegated portfolio optimization with linear contracts. Applying tools from Conditional Analysis we show that some results known in the literature for very specific instances of the problem carry over to translation invariant and time-consistent utility functions in very general probabilistic settings. However, we find that optimal contracts must in general make use of derivatives for compensation.