The Quantum Computing Potential to Speed Up Monte Carlo Calculations for Credit Valuation Adjustments

The Quantum Computing Potential to Speed Up Monte Carlo Calculations for Credit Valuation Adjustments
We’re excited to announce the results of a research project we recently conducted with the global bank BBVA. The big news? Our research proposed new circuit designs that could enable financial institutions to generate value from quantum computers years earlier than expected.

Zapata’s scientists partnered with the BBVA Research and Patent team to identify challenges and opportunities for quantum algorithms to speed up Monte Carlo simulations in finance. Monte Carlo simulations are commonly used for credit valuation adjustment (CVA) and derivative pricing. The research proposes novel circuit designs that significantly reduce the resources needed to gain a practical quantum advantage in derivative calculations.

Understanding the CVA value

Let’s assume a restaurant organizes banquets and events. Initially, when it receives a request to organize an event, it does not ask for any upfront fee. The restaurant simply decides to trust the requester de facto taking on the potential risk of the requester not showing up or not fully paying after the event. Under a normal economic environment, this practice may seem reasonable. If some client is a no-show or defaults, there is enough activity to reuse its provisions in other events. However, under a climate of economic stress, where there is less business, events are more scarce or restricted, and the clients default more frequently, the restaurant needs some warrant to compensate for a potential client default. In such circumstances, the restaurant decides to charge a non-refundable down payment fee for making reservations, on top of the normal event cost. That extra guarantee would be the CVA value.

History of Credit Valuation Adjustment

Prior to the 2008 financial crisis, market participants treated large derivative counterparties as very unlikely to fail. So, due to a negligible probability of default, a counterparty’s credit risk was not considered. Rating agencies also contributed to the high credit rating of counterparties (and the small size of derivative exposures). Under normal market conditions, the assumption was that a counterparty’s default on their financial obligations (like other parties) may be considered negligible in the first approximation. However, during the 2008 financial crisis, the market experienced dozens of corporate collapses, including large derivative counterparties.

Fueled by regulatory pressure to minimize systemic financial risk, banks and other financial institutions have been increasingly focused on accounting for credit risk in derivative pricing. In the US, similar regulation exists to stress-test financial scenarios for Comprehensive Capital Analysis and Review (CCAR) and Dodd-Frank compliance. Monte Carlo simulation is the standard approach for this type of risk analysis, but the calculations required — which must account for all possible credit default scenarios — are immensely complex and prohibitively time-consuming for classical computers.

Research findings

This new research reveals practical ways for quantum algorithms to speed up the Monte Carlo simulation process. When we engage our enterprise customers to collaborate on quantum-enabled projects, we perform in-depth studies of how much quantum computing resources will be required to obtain practical benefits for business operations. In-depth research such as this can directly inform the hardware specifications needed for quantum advantage in specific use cases.

From Zapata’s standpoint, this innovative approach to quantum-accelerated Monte Carlo methods uses a novel form of amplitude estimation, combined with additional improvements that make the quantum circuit much shallower, in some cases hundreds of times shallower than the well-known alternatives in the literature. This approach reduces the time needed for a quantum computer to complete the CVA calculation by orders of magnitude, and also dramatically reduces the number of qubits needed to gain a quantum advantage over classical methods.

This joint publication is the result of one in a series of research initiatives that BBVA Research & Patents launched in 2019. These projects, conducted in partnership with leading institutions and companies including Spanish National Research Council, Multiverse, Fujitsu and Accenture, explore the potential advantages of applying quantum computing in the financial sector.

You can read the full paper here.

Francisco Javier F. Alcazar
Zapata Author

Francisco Javier F. Alcazar , Ph.D.

Quantum Application Scientist, Research