Implementation of Geometric Brownian Motion to Predict Crude Oil Prices

Feby Seru; Christian Dwi Suhendra; Agung Dwi Saputro; Gautam Makwana; H Elizabeth

doi:10.25217/numerical.v6i2.2674

Authors

Feby Seru University of Cenderawasih
Christian Dwi Suhendra University of Papua
Agung Dwi Saputro University of Cenderawasih
Gautam Makwana Department of Social Work, Mizoram University, India
H Elizabeth Department of Social Work, Mizoram University, India

DOI:

https://doi.org/10.25217/numerical.v6i2.2674

Abstract

Crude oil has a vital role in the economic growth of a country because crude oil is a source of energy driving the economy. To maintain economic stability, the price of crude oil in the coming period needs to be anticipated by making predictions on world crude oil commodity prices. One of the models that can be used to predict crude oil prices in the short term is Geometric Brownian Motion (GBM). This study aims to implement the GBM model to predict crude oil prices during the Covid-19 pandemic and measure the model's accuracy. This study made crude oil price predictions with several iterations of 50, 100, and 1000. The results showed that the smallest MAPE value was carried out 1000 times in iterations, namely 2.13%. Based on the MAPE value, it can be concluded that the results of crude oil price predictions using GBM have a high level of accuracy.

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