Generalized Modeling of Oil Futures Volatility Through Uncertainty Indicator Selection: A GARCH–MIDAS–AES Framework

ABSTRACT Building on prior literature that has demonstrated the effectiveness of various uncertainty‐related indicators in enhancing the accuracy of crude oil volatility forecasting, this paper first investigates the type and persistence of the impact of changes in these indicators on volatility and then compares these indicators across different scenarios to determine the optimal strategy for their implementation. We employ a more generalized approach by utilizing the GARCH–MIDAS–AES model, which accommodates features that vary with different indicators. The empirical results, based on data from 1997 to 2022, underscore the importance of considering threshold and leverage effects. We also identify two types of impact: directional and nondirectional. Furthermore, among the uncertainty indicators examined, our findings affirm the predictive prowess of the Financial Uncertainty indicator in the majority of cases. However, during periods of global crisis, the Index of Global Real Economic Activity emerges as a more practical choice.

Authors

Institutions

Publication Details

Journal
Journal of Futures Markets
Published
2025-06-02
DOI
https://doi.org/10.1002/fut.22605
Citations
2
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
6.26

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generalized Modeling of Oil Futures Volatility Through Uncertainty Indicator Selection: A GARCH–MIDAS–AES Framework

Siyue Zheng, Mingliang Xu, Min Zhu
2 citations
Journal of Futures Markets
Market Dynamics and Volatility
6.26
article

Generalized Modeling of Oil Futures Volatility Through Uncertainty Indicator Selection: A GARCH–MIDAS–AES Framework

Siyue Zheng, Mingliang Xu, Min Zhu
article en
2 citations

Abstract

ABSTRACT Building on prior literature that has demonstrated the effectiveness of various uncertainty‐related indicators in enhancing the accuracy of crude oil volatility forecasting, this paper first investigates the type and persistence of the impact of changes in these indicators on volatility and then compares these indicators across different scenarios to determine the optimal strategy for their implementation. We employ a more generalized approach by utilizing the GARCH–MIDAS–AES model, which accommodates features that vary with different indicators. The empirical results, based on data from 1997 to 2022, underscore the importance of considering threshold and leverage effects. We also identify two types of impact: directional and nondirectional. Furthermore, among the uncertainty indicators examined, our findings affirm the predictive prowess of the Financial Uncertainty indicator in the majority of cases. However, during periods of global crisis, the Index of Global Real Economic Activity emerges as a more practical choice.

Journal of Futures MarketsVol. 45(9)
Shanghai Normal University (CN), Fudan University (CN)
National Social Science Fund of China
Openalex Percentile: Top 4%
Market Dynamics and Volatility
6.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Generalized Modeling of Oil Futures Volatility Through Uncertainty Indicator Selection: A GARCH–MIDAS–AES Framework — Siyue Zheng, Mingliang Xu, et al. · Journal of Futures Markets (2025) | TGRS Research Map | TGRS