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 imple-
mentation 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.
