Proceedings of the International Conference on Advance Research in Social and Economic Science (ICARSE 2022)

<Previous Article In Volume
Next Article In Volume>

Uncovering Coal Price Volatility: Comparing Parameter Estimation Approaches for Mean Reversion Modeling

Authors
Muhammad Adam Gana1, Eko Wicaksono1, Shofa Rijalul Haq1, Aldin Ardian1, *
1Mining Engineering Department, Universitas Pembangunan Nasional “Veteran” Yogyakarta, Jalan Padjajaran (Ring Road Utara) 104, Yogyakarta, 55283, Indonesia
*Corresponding author. Email: aldin.ardian@upnyk.ac.id
Corresponding Author
Aldin Ardian
Available Online 27 April 2023.
DOI
10.2991/978-2-38476-048-0_7How to use a DOI?
Keywords
coal price; mean reversion model; parameter estimation; simulation; stochastic process
Abstract

This scientific article examines the modeling of coal price volatility using a mean reversion model (MRM) and compares the performance of different parameter estimation approaches. The aim of the study is to identify which parameter estimation approach is best suited for modeling the volatility of coal prices. The study uses annual discrete time data from 2022 to 2031 to estimate the MRM parameters using three approaches: linear regression method (LRM), least square method (LSM), and moment method (MM). The results show that the MM approach produces the highest volatility, while the LRM has the lowest reversion value but higher volatility than the LSM. The findings suggest that the MM approach may be more suitable for modeling coal price volatility due to its ability to capture higher levels of volatility. These results have implications for understanding the dynamics of the coal market and can inform decisions related to pricing, risk management, and investment in the coal industry.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Advance Research in Social and Economic Science (ICARSE 2022)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
27 April 2023
ISBN
10.2991/978-2-38476-048-0_7
ISSN
2352-5398
DOI
10.2991/978-2-38476-048-0_7How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammad Adam Gana
AU  - Eko Wicaksono
AU  - Shofa Rijalul Haq
AU  - Aldin Ardian
PY  - 2023
DA  - 2023/04/27
TI  - Uncovering Coal Price Volatility: Comparing Parameter Estimation Approaches for Mean Reversion Modeling
BT  - Proceedings of the International Conference on Advance Research in Social and Economic Science (ICARSE 2022)
PB  - Atlantis Press
SP  - 56
EP  - 64
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-048-0_7
DO  - 10.2991/978-2-38476-048-0_7
ID  - Gana2023
ER  -