Quantum Computation for Pricing the Collateralized Debt Obligations

TL;DR

This paper explores the application of quantum computing to price collateralized debt obligations (CDOs) by implementing quantum algorithms for complex financial models, demonstrating potential for more efficient risk assessment.

Contribution

It introduces quantum circuit implementations of CDO pricing models and applies quantum amplitude estimation as an alternative to classical Monte Carlo methods.

Findings

Quantum circuits successfully model CDO risk distributions.

Quantum amplitude estimation reduces computational complexity.

Demonstrated on IBM Qiskit platform.

Abstract

Collateralized debt obligation (CDO) has been one of the most commonly used structured financial products and is intensively studied in quantitative finance. By setting the asset pool into different tranches, it effectively works out and redistributes credit risks and returns to meet the risk preferences for different tranche investors. The copula models of various kinds are normally used for pricing CDOs, and the Monte Carlo simulations are required to get their numerical solution. Here we implement two typical CDO models, the single-factor Gaussian copula model and Normal Inverse Gaussian copula model, and by applying the conditional independence approach, we manage to load each model of distribution in quantum circuits. We then apply quantum amplitude estimation as an alternative to Monte Carlo simulation for CDO pricing. We demonstrate the quantum computation results using IBM Qiskit. Our work addresses a useful task in finance instrument pricing, significantly broadening the application scope for quantum computing in finance.