Modeling the Volatility of World Energy Commodity Prices Using the GARCH-Fractional Cointegration Model

Prajna Pramita Izati; Dedy Dwi Prastyo; Muhammad Sjahid Akbar

doi:10.1016/j.procs.2024.03.022

Modeling the Volatility of World Energy Commodity Prices Using the GARCH-Fractional Cointegration Model

Prajna Pramita Izati, Dedy Dwi Prastyo^*, Muhammad Sjahid Akbar

^*Corresponding author for this work

Laboratory of Economics and Financial Data Analytics

Institut Teknologi Sepuluh Nopember

Research output: Contribution to journal › Conference article › peer-review

Abstract

Energy commodity prices usually fluctuate, non-linear and non-stationary. These stylish facts pose a big challenge in predicting the volatility of energy commodity prices because they usually contain long memory. In the energy market, energy commodities are empirically cointegrated, and this characteristic is a consideration for combining GARCH with Fractional Cointegration. This study aims to model and compare the GARCH and GARCH-Fractional Cointegration on the price return volatility of each energy commodity. The results show that the GARCH-Fractional Cointegration model is better for long-memory non-stationary data, while the GARCH model is better for long-memory stationary data.

Original language	English
Pages (from-to)	412-419
Number of pages	8
Journal	Procedia Computer Science
Volume	234
DOIs	https://doi.org/10.1016/j.procs.2024.03.022
Publication status	Published - 2024
Event	7th Information Systems International Conference, ISICO 2023 - Washington, United States Duration: 26 Jul 2023 → 28 Jul 2023

Keywords

Energy Commodity
Fractional Cointegration
GARCH
Modeling

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10.1016/j.procs.2024.03.022

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title = "Modeling the Volatility of World Energy Commodity Prices Using the GARCH-Fractional Cointegration Model",

abstract = "Energy commodity prices usually fluctuate, non-linear and non-stationary. These stylish facts pose a big challenge in predicting the volatility of energy commodity prices because they usually contain long memory. In the energy market, energy commodities are empirically cointegrated, and this characteristic is a consideration for combining GARCH with Fractional Cointegration. This study aims to model and compare the GARCH and GARCH-Fractional Cointegration on the price return volatility of each energy commodity. The results show that the GARCH-Fractional Cointegration model is better for long-memory non-stationary data, while the GARCH model is better for long-memory stationary data.",

keywords = "Energy Commodity, Fractional Cointegration, GARCH, Modeling",

author = "Izati, {Prajna Pramita} and Prastyo, {Dedy Dwi} and Akbar, {Muhammad Sjahid}",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Published by Elsevier B.V.; 7th Information Systems International Conference, ISICO 2023 ; Conference date: 26-07-2023 Through 28-07-2023",

year = "2024",

doi = "10.1016/j.procs.2024.03.022",

language = "English",

volume = "234",

pages = "412--419",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier BV",

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TY - JOUR

T1 - Modeling the Volatility of World Energy Commodity Prices Using the GARCH-Fractional Cointegration Model

AU - Izati, Prajna Pramita

AU - Prastyo, Dedy Dwi

AU - Akbar, Muhammad Sjahid

PY - 2024

Y1 - 2024

N2 - Energy commodity prices usually fluctuate, non-linear and non-stationary. These stylish facts pose a big challenge in predicting the volatility of energy commodity prices because they usually contain long memory. In the energy market, energy commodities are empirically cointegrated, and this characteristic is a consideration for combining GARCH with Fractional Cointegration. This study aims to model and compare the GARCH and GARCH-Fractional Cointegration on the price return volatility of each energy commodity. The results show that the GARCH-Fractional Cointegration model is better for long-memory non-stationary data, while the GARCH model is better for long-memory stationary data.

AB - Energy commodity prices usually fluctuate, non-linear and non-stationary. These stylish facts pose a big challenge in predicting the volatility of energy commodity prices because they usually contain long memory. In the energy market, energy commodities are empirically cointegrated, and this characteristic is a consideration for combining GARCH with Fractional Cointegration. This study aims to model and compare the GARCH and GARCH-Fractional Cointegration on the price return volatility of each energy commodity. The results show that the GARCH-Fractional Cointegration model is better for long-memory non-stationary data, while the GARCH model is better for long-memory stationary data.

KW - Energy Commodity

KW - Fractional Cointegration

KW - GARCH

KW - Modeling

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U2 - 10.1016/j.procs.2024.03.022

DO - 10.1016/j.procs.2024.03.022

M3 - Conference article

AN - SCOPUS:85193199882

SN - 1877-0509

VL - 234

SP - 412

EP - 419

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 7th Information Systems International Conference, ISICO 2023

Y2 - 26 July 2023 through 28 July 2023

ER -

Modeling the Volatility of World Energy Commodity Prices Using the GARCH-Fractional Cointegration Model

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Keywords

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