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2023-06-26

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cc-by (c) Alaminos Aguilera, David et al., 2023
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/200352

Quantum Monte Carlo simulations for estimating FOREX markets: A speculative attacks experience

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https://doi.org/10.1057/s41599-023-01836-2

Abstract

The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the implementation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes.

Subject

Mercat financer, Mètode de Montecarlo, Mètodes de simulació, Previsió econòmica

Subject (English)

Financial market, Monte Carlo method, Simulation methods, Economic forecasting

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ALAMINOS AGUILERA, David, SALAS COMPAS, M. Belén and FERNÁNDEZ-GÁMEZ, Manuel A. Quantum Monte Carlo simulations for estimating FOREX markets: A speculative attacks experience. Humanities & Social Sciences Communications. 2023. Vol. 10, num. 353. ISSN 2662-9992. [consulted: 13 of August of 2026]. Available at: https://hdl.handle.net/2445/200352

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