Quantum algorithm for calculating risk contributions in a credit portfolio

Miyamoto, Koichi

doi:10.48550/arxiv.2201.11394

Cited by 1 publication

(1 citation statement)

References 48 publications

(69 reference statements)

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“…• Estimation of risk measures such as VaR (see also Sect. 4), Conditional VaR (CVaR) [91] and the corresponding risk contributions [63] • Estimation of credit risk (Economic Capital) [30] • Sensitivity analysis for a (business) risk model at an exchange [15] Miscellaneous. Quantum algorithms have been applied across a range of other areas in finance as well.…”

Section: Research Landscape In Quantitative Financementioning

confidence: 99%

Quantum computing for financial risk measurement

Wilkens¹,

Moorhouse

2023

Quantum Inf Process

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Quantum computing allows a significant speed-up over traditional CPU- and GPU-based algorithms when applied to particular mathematical challenges such as optimisation and simulation. Despite promising advances and extensive research in hard- and software developments, currently available quantum systems are still largely limited in their capability. In line with this, practical applications in quantitative finance are still in their infancy. This paper analyses requirements and concrete approaches for the application to risk management in a financial institution. On the examples of Value-at-Risk for market risk and Potential Future Exposure for counterparty credit risk, the main contribution lies in going beyond textbook illustrations and instead exploring must-have model features and their quantum implementations. While conceptual solutions and small-scale circuits are feasible at this stage, the leap needed for real-life applications is still significant. In order to build a usable risk measurement system, the hardware capacity—measured in number of qubits—would need to increase by several magnitudes from their current value of about $$10^2$$ 10 2 . Quantum noise poses an additional challenge, and research into its control and mitigation would need to advance in order to render risk measurement applications deployable in practice. Overall, given the maturity of established classical simulation-based approaches that allow risk computations in reasonable time and with sufficient accuracy, the business case for a move to quantum solutions is not very strong at this point.

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