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All Journal Science and Technology Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Applied Data Sciences Jurnal Ilmiah Kampus Mengajar Jurnal Keilmuan dan Keislaman
YAN ADITYA PRADANA
Fakultas Ilmu Formal Dan Ilmu Terapan, Universitas Muhammadiyah Madiun
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Transportation

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Journal : Journal of Applied Data Sciences

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The Model of Carbon Price Risk Prediction in European Markets Using Long Short-Term Memory- Geometric Brownian Motion Pradana, Yan Aditya; Mukhlash, Imam; Irawan, Mohammad Isa; Putri, Endah Rokhmati Merdika; Iqbal, Mohammad
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.536

Abstract

Accurate carbon market price prediction is one of the fundamentals in assessing the risks associated with carbon trading. Related studies on carbon price prediction were mainly focused on two major approaches: mathematical and/or machine learning models. Geometric Brownian Motion (GBM) is one of the mathematical models that can represent carbon price movements but requires modifying the sample size and the number of parameters for compiling the simulation numerically. Moreover, two critical parameters: (μ) mu and (σ) sigma need to be estimated to simulate the carbon price movements. In this study, the parameters μ and σ estimation are based on the average return value and standard deviation. However, if the carbon price movement is very volatile, we need to recognize its trend and characteristics by estimating the parameters precisely until there is no significant change (or stable) patterns. That is very expensive and may be intractable on high-dimensional data with less precise prediction. Therefore, we propose a hybrid model for carbon price prediction based on GBM with the parameter estimation using the Long Short-Term Memory (LSTM) model. The LSTM model was chosen because it has high accuracy in parameter estimation without losing the characteristics of the GBM stochastic model. Furthermore, Value at Risk (VaR) is utilized to measure the risk of carbon price volatility predictions. The simulation results showed the proposed model has higher prediction accuracy with a not-too-significant time difference, and the model is proven reliable in measuring future risks.
Co-Authors AA Sudharmawan, AA Aji, Alfian Chrisna Aji, Angelia Sukma Ningrum Afrida Yono Putri Andiyani, Rhavida Anniza Azka, Dea Alvionita Dian Artha Kusumaningtyas Efri Roziaty Efri Roziaty Erma Widyastuti Fadilatin, Nuha Syaj’in Flora Ramona Sigit Prakoeswa Ida Untari Ihwan Susila Imam Mukhlash Irfan Miftahul Fauzi Isnaeni Umi Machromah Iswari, Dian Ardhifa Kurniaji, Ganno Tri Buana Lenny Puspita Dewi MOH. MUDZAKKIR Mohammad Isa Irawan Muhammad Qolbi Shobri Muhammad Syahriandi Adhantoro Mujiono, Edo Priyo Utomo Putro Muthiah, Suci Muzakar Isa Naufal Ishartono Nova Tri Romadloni Nuha Syaj’in Fadilatin Pramudito, Wysnu Putri, Angelia Sukma Ningrum Afrida Yono Putri, Endah R.M. Putri, Endah Rokhmati Merdika Rahmah, Maulida Atsaniatu Santosa Pradana Putra Setya Negara Setyawati, Yayuk Trisakti Handayani Wijanarko, Ardi