Comparison of GBM, GFBM and MJD Models in Malaysian Rubber Prices Forecasting
DOI:
https://doi.org/10.11113/mjfas.v19n1.2763Keywords:
Geometric Brownian Motion, Geometric Fractional Brownian Motion, Merton Jump-Diffusion, Monte Carlo Simulation, Forecasting, Rubber PriceAbstract
This research studies three mathematical models, namely geometric Brownian motion (GBM), geometric fractional Brownian motion (GFBM) model which was developed by adding the Hurst parameter to GBM to characterize the long-memory phenomenon, and Merton jump-diffusion (MJD) model which captures shocks via GBM. This study sets out to forecast Malaysia rubber prices for the six months period beginning in January 2022 and ending in June 2022, which involves four main steps; calculating the logarithmic return of rubber prices; estimating the parameters for forecasting the rubber prices using the three models; simulating the rubber prices using the GBM, GFBM and MJD models via Monte Carlo simulation; and computing the mean absolute percentage errors (MAPE) and forecast accuracy. Simulation results show that the MJD model is the most accurate model in forecasting the rubber prices.
References
Abidin, S. N. Z., & Jaffar, M. M. (2014). Forecasting share prices of small size companies in Bursa Malaysia using geometric Brownian motion. Applied Mathematics & Information Sciences, 8(1), 107.
Agustini, W. F., Affianti, I. R., & Putri, E. R. (2018, March). Stock price prediction using geometric Brownian motion. Journal of Physics: Conference Series, 974(1), 012047. IOP Publishing.
Malaysian Rubber Board. (2022). Monthly prices for physical rubber smr grades and centrifuged latex (60% drc) in sen/kg [Data file]. Retrieved from: http://www3.lgm.gov.my.
Malaysian Rubber Council. (2021). “World rubber production, consumption and trade” [Data file]. Retrieved from: https://www.myrubbercouncil.com.
Feng, Z. (2018). Stock-Price Modeling by the Geometric Fractional Brownian Motion: A View towards the Chinese Financial Market (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78375.
Hamdan, Z. N., Ibrahim, S. N. I., & Mustafa, M. S. (2020). Modelling malaysian gold prices using geometric brownian motion model. Adv. Math. Sci. J, 9, 7463-7469.
Ibrahim, S. N. I., Misiran, M., & Laham, M. F. (2021). Geometric fractional Brownian motion model for commodity market simulation. Alexandria Engineering Journal, 60(1), 955-962.
Mandelbrot, B. B., & Van Ness, J. W. (1968). Fractional Brownian motions, fractional noises and applications. SIAM Review, 10(4), 422-437.
Si, R. K., & Bishi, B. (2020). Forecasting short term return distribution of S&P BSE stock index using Geometric Brownian motion: An evidence from Bombay stock exchange. Int. J. Stat. Syst, 15(1), 29-45.
Tang, F. (2018). Merton Jump-Diffusion Modeling of Stock Price Data (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78351.
Hsu, Y. T., Liu, M. C., Yeh, J., & Hung, H. F. (2009). Forecasting the turning time of stock market based on Markov–Fourier grey model. Expert Systems with Applications, 36(4), 8597-8603.
Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 23(8), 800-808.
Munis, R. A., Camargo, D. A., da Silva, R. B. G., Tsunemi, M. H., Ibrahim, S. N.I., & Simoes, D. (2022). Price Modeling of Eucalyptus Wood under Different Silvicultural Management for Real Options Approach. Forests, 13(3), 478.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Siti Nur Iqmal Ibrahim, Nasrin Zulaikha Muda
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
