Robust framework for quantifying the value of information in pricing and hedging

TL;DR

This paper develops a robust framework to quantify the value of asymmetric information in derivative pricing and hedging, extending the classical duality to informed agents under various information arrival scenarios.

Contribution

It introduces a general framework for informed agents' superhedging and pricing, and establishes duality results using robust approach techniques, covering multiple information arrival scenarios.

Findings

Informed agents can be modeled as regular agents restricted to certain paths.

Pricing--hedging duality extends to informed agents under various information scenarios.

Superhedging value satisfies a dynamic programming principle.

Abstract

We investigate asymmetry of information in the context of robust approach to pricing and hedging of financial derivatives. We consider two agents, one who only observes the stock prices and another with some additional information, and investigate when the pricing--hedging duality for the former extends to the latter. We introduce a general framework to express the superhedging and market model prices for an informed agent. Our key insight is that an informed agent can be seen as a regular agent who can restrict her attention to a certain subset of possible paths. We use results of Hou & Ob\l\'oj on robust approach with beliefs to establish the pricing--hedging duality for an informed agent. Our results cover number of scenarios, including information arriving before trading starts, arriving after static position in European options is formed but before dynamic trading starts or arriving at some point before the maturity. For the latter we show that the superhedging value satisfies a suitable dynamic programming principle, which is of independent interest.