Derivatives pricing using signature payoffs

Abstract

We introduce signature payoffs, a family of path-dependent derivatives that are given in terms of the signature of the price path of the underlying asset. We show that these derivatives are dense in the space of continuous payoffs, a result that is exploited to quickly price arbitrary continuous payoffs. This approach to pricing derivatives is then tested with European options, American options, Asian options, lookback options and variance swaps. As we show, signature payoffs can be used to price these derivatives with very high accuracy.

Cited by in corpus (6)

• Neural Controlled Differential Equations for Irregular Time Series
• Deep Signature Transforms
• A Generalised Signature Method for Multivariate Time Series Feature Extraction
• Framing RNN as a kernel method: A neural ODE approach
• Deep Signature FBSDE Algorithm
• Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture