Quantum Computing for Option Portfolio Analysis

TL;DR

This paper presents a quantum algorithm for efficiently estimating Value-at-Risk and Conditional Value-at-Risk for European options portfolios, addressing high-dimensional challenges in risk assessment.

Contribution

It introduces a novel quantum algorithm specifically designed for VaR and CVaR estimation in options portfolios, and explores its limitations for American options pricing.

Findings

Quantum algorithm effectively estimates VaR and CVaR for European options.

Identifies a quantum 'no-go' theorem limiting American options pricing.

Highlights need for alternative strategies in quantum American options pricing.

Abstract

In this paper, we introduce an efficient and end-to-end quantum algorithm tailored for computing the Value-at-Risk (VaR) and conditional Value-at-Risk (CVar) for a portfolio of European options. Our focus is on leveraging quantum computation to overcome the challenges posed by high dimensionality in VaR and CVaR estimation. While our innovative quantum algorithm is designed primarily for estimating portfolio VaR and CVaR for European options, we also investigate the feasibility of applying a similar quantum approach to price American options. Our analysis reveals a quantum 'no-go' theorem within the current algorithm, highlighting its limitation in pricing American options. Our results indicate the necessity of investigating alternative strategies to resolve the complementarity challenge in pricing American options in future research.