Detecting bubbles in world aluminum prices: Evidence from GSADF test

The aim of this research is to assess the existence of multiple bubbles in the global aluminum market by employing the Generalized Supremum Augmented Dickey-Fuller (GSADF) methodology. This method offers practical time series analysis tools for identifying periods of rapid price escalation, followed by subsequent collapses. Findings indicate the identification of six explosive bubbles occurring between January 1980 and March 2023, during which the aluminum price strayed from its underlying fundamental value. Additionally, this finding is consistent with the asset pricing model, which generally considers both fundamental and bubble components. Based on the empirical results, the aluminum price bubbles are positively influenced by the copper price, GDP, the U. S dollar index, industrialization of China, China’s urbanization rate, whereas the global aluminum production, oil price, and base metal price index have a negative explanatory effect on the aluminum price bubbles. To effectively stabilize the international aluminum price, policymakers are suggested to be vigilant in identifying bubble episodes and monitoring their progression. Additionally, regulatory authorities should implement measures to curb excessive speculative activity during periods of extreme market volatility, thereby mitigating excessive price fluctuations and the formation of aluminum bubbles.

Authors

Institutions

Publication Details

Journal
Journal of Business Economics and Management
Published
2024-12-04
DOI
https://doi.org/10.3846/jbem.2024.22262
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Detecting bubbles in world aluminum prices: Evidence from GSADF test

Menglin Ni, Xiaoying Wang
Journal of Business Economics and Management
Market Dynamics and Volatility
article

Detecting bubbles in world aluminum prices: Evidence from GSADF test

Menglin Ni, Xiaoying Wang
article en

Abstract

The aim of this research is to assess the existence of multiple bubbles in the global aluminum market by employing the Generalized Supremum Augmented Dickey-Fuller (GSADF) methodology. This method offers practical time series analysis tools for identifying periods of rapid price escalation, followed by subsequent collapses. Findings indicate the identification of six explosive bubbles occurring between January 1980 and March 2023, during which the aluminum price strayed from its underlying fundamental value. Additionally, this finding is consistent with the asset pricing model, which generally considers both fundamental and bubble components. Based on the empirical results, the aluminum price bubbles are positively influenced by the copper price, GDP, the U. S dollar index, industrialization of China, China’s urbanization rate, whereas the global aluminum production, oil price, and base metal price index have a negative explanatory effect on the aluminum price bubbles. To effectively stabilize the international aluminum price, policymakers are suggested to be vigilant in identifying bubble episodes and monitoring their progression. Additionally, regulatory authorities should implement measures to curb excessive speculative activity during periods of extreme market volatility, thereby mitigating excessive price fluctuations and the formation of aluminum bubbles.

Journal of Business Economics and ManagementVol. 25(6)
Shandong Women’s University (CN)
National Social Science Fund of China
Sustainable cities and communities
Openalex Percentile: Top 73%
Market Dynamics and Volatility
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.