Abstract (Translated):
In this study, the potential relationship between commodity values during the period from 2019 to 2023 was investigated, using the Hurst coefficient as a statistical metric. The results indicated that, generally speaking, the price records exhibited persistence with a coefficient of 0.63, but also revealed a certain degree of randomness. Consequently, a geometric Brownian stochastic process along with Monte Carlo simulation was used to anticipate prices over a one-year horizon in the stock market and, in this way, identify unpredictable fluctuations in each data set. Overall, the simulation results demonstrated a stable average trend despite the random nature of the process, with notable increases in the prices of Brent oil, UK and US copper, and bar steel. On the other hand, HRC steel prices remained constant, while a significant decrease in natural gas prices was anticipated compared to their average historical values.