Prediction of mineral product price based on mean reversion model
Shuwei Huang 1,2,3
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BGRIMM Technology Group
Beijing Key Laboratory of Nonferrous Intelligent Mining Technology
BGRIMM Intelligent Technology Co. Ltd
Submission date: 2022-05-03
Final revision date: 2022-05-13
Acceptance date: 2022-05-28
Publication date: 2022-06-28
Corresponding author
Shuwei Huang   

BGRIMM Technology Group
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2022;38(2):61-75
The mean-reversion model is introduced into the study of mineral product price prediction. The gold price data from January 2018 to December 2021 are selected, and a mean-reverting stochastic process simulation of the gold price was carried out using Monte Carlo simulation (MCS) method. By comparing the statistical results and trend curves of the mean-reversion (MR) model, geometric Brownian motion (GBM) model, time series model and actual price, it is proved that the mean-reversion process is valid in describing the price fluctuation of mineral product. At the same time, by comparing with the traditional prediction methods, the mean-reversion model can quantitatively assess the uncertainty of the predicted price through a set of equal probability stochastic simulation results, so as to provide data support and decision-making basis for the risk analysis of future economy.
This work was jointly supported by the Major Science and Technology Innovation Project of Shandong Province No. 2019SDZY05 and the Scientific Research Fund of the BGRIMM Technology Group No. 02-2035.
Prognozowanie ceny produktu mineralnego w oparciu o model średniej rewersji
cena produktu mineralnego, model średniej rewersji, symulacja Monte Carlo, analiza niepewności
W badaniach predykcji cen produktów mineralnych wprowadzono model średniej rewersji. Wybrano dane dotyczące cen złota od stycznia 2018 do grudnia 2021 r., a symulację ceny złota w procesie odwracania średniej przeprowadzono metodą symulacji Monte Carlo (MCS). Porównując wyniki statystyczne i krzywe trendu modelu średniej rewersji (MR), modelu geometrycznego ruchu Browna (GBM), modelu szeregów czasowych i rzeczywistej ceny, udowodniono, że proces średniej rewersji jest prawidłowy w opisie fluktuacji cen na produkt mineralny. Jednocześnie, porównując z tradycyjnymi metodami predykcji, model średniej rewersji może ilościowo oszacować niepewność przewidywanej ceny za pomocą zestawu wyników symulacji stochastycznej równego prawdopodobieństwa, w celu zapewnienia wsparcia danych i podstawy decyzyjnej do analizy ryzyka przyszłej gospodarki.
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