Model-independent pricing with insider information: a Skorokhod embedding approach

TL;DR

This paper develops a model-independent framework for pricing and hedging derivatives for insider traders using a Skorokhod embedding approach, incorporating market data and insider information to derive duality results and optimal models.

Contribution

It introduces a novel model-independent method for insider trading scenarios using Skorokhod embedding, extending duality and monotonicity principles to this context.

Findings

Derived duality results for insider trading models.

Established geometric properties of optimal models.

Provided analytic and numerical solutions for pricing and hedging.

Abstract

In this paper, we consider the pricing and hedging of a financial derivative for an insider trader, in a model-independent setting. In particular, we suppose that the insider wants to act in a way which is independent of any modelling assumptions, but that she observes market information in the form of the prices of vanilla call options on the asset. We also assume that both the insider's information, which takes the form of a set of impossible paths, and the payoff of the derivative are time-invariant. This setup allows us to adapt recent work of Beiglboeck, Cox and Huesmann (2016) to prove duality results and a monotonicity principle, which enables us to determine geometric properties of the optimal models. Moreover, we show that this setup is powerful, in that we are able to find analytic and numerical solutions to certain pricing and hedging problems.