Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms

Nikitas Stamatopoulos; Guglielmo Mazzola; Stefan Woerner; William J. Zeng

doi:10.22331/q-2022-07-20-770

Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms

Nikitas Stamatopoulos¹, Guglielmo Mazzola², Stefan Woerner², and William J. Zeng¹

¹Goldman, Sachs & Co., New York, NY
²IBM Quantum, IBM Research – Zurich

Published:	2022-07-20, volume 6, page 770
Eprint:	arXiv:2111.12509v2
Doi:	https://doi.org/10.22331/q-2022-07-20-770
Citation:	Quantum 6, 770 (2022).

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Abstract

We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing quadratically in the target error and we extend this to a quadratic error scaling advantage in market risk computation. We show that employing quantum gradient estimation algorithms can deliver a further quadratic advantage in the number of the associated market sensitivities, usually called $greeks$. By numerically simulating the quantum gradient estimation algorithms on financial derivatives of practical interest, we demonstrate that not only can we successfully estimate the greeks in the examples studied, but that the resource requirements can be significantly lower in practice than what is expected by theoretical complexity bounds. This additional advantage in the computation of financial market risk lowers the estimated logical clock rate required for financial quantum advantage from Chakrabarti et al. [Quantum 5, 463 (2021)] by a factor of ~7, from 50MHz to 7MHz, even for a modest number of greeks by industry standards (four). Moreover, we show that if we have access to enough resources, the quantum algorithm can be parallelized across 60 QPUs, in which case the logical clock rate of each device required to achieve the same overall runtime as the serial execution would be ~100kHz. Throughout this work, we summarize and compare several different combinations of quantum and classical approaches that could be used for computing the market risk of financial derivatives.

Featured image: Maximum likelihood estimation can be used in conjunction with quantum gradient algorithms to obtain better gradient estimates. Left: The discrete probability distribution before measurement of the Vega greek $\partial\theta/\partial\sigma$ for a basket option (blue bars) is fitted to the expected distribution (black line). Right: The global maximum of the log-likelihood (green dot) gives us a better estimate of the true value (dashed red line) than the most likely result if we sample from the probability distribution and take the median (yellow triangle).

Popular summary

Recently, quantum algorithms have been proposed to accelerate the pricing and risk analysis of financial derivatives. These algorithms use quantum amplitude estimation to achieve quadratic advantage compared to the classical Monte Carlo methods that are used in practice for most computationally expensive pricing. Given a desired error $\epsilon$, the quantum advantage stems from the runtime of a classical Monte Carlo simulation scaling as $O(1/\epsilon^2)$ while the quantum algorithms scale as $O(1/\epsilon)$.
A related and important financial application is the computation of the sensitivity of derivative prices to model and market parameters. This amounts to computing gradients of the derivative price with respect to input parameters. A primary business use of calculating these gradients is to enable hedging of the market risk that arises from exposure to derivative contracts. Hedging this risk is of critical importance to financial firms. Gradients of financial derivatives are typically called greeks, as these quantities are commonly labeled using Greek alphabet letters.
In this work, we examine the efficacy of quantum gradient algorithms in the estimation of greeks in a quantum setting. We introduce a method combining gradient algorithms and Maximum Likelihood Estimation (MLE) to estimate the greeks of a path-dependent basket option and show that quantum advantage for calculating risk may be achievable with quantum computers whose clock rates are 7 times slower than that required for pricing itself, indicating another possible avenue for quantum advantage in finance.

► BibTeX data

@article{Stamatopoulos2022towardsquantum,
  doi = {10.22331/q-2022-07-20-770},
  url = {https://doi.org/10.22331/q-2022-07-20-770},
  title = {Towards {Q}uantum {A}dvantage in {F}inancial {M}arket {R}isk using {Q}uantum {G}radient {A}lgorithms},
  author = {Stamatopoulos, Nikitas and Mazzola, Guglielmo and Woerner, Stefan and Zeng, William J.},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {6},
  pages = {770},
  month = jul,
  year = {2022}
}

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Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms

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