Detecting bubbles in world aluminum prices: Evidence from GSADF test
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.
Keyword : aluminum price, generalized sup ADF test, multiple bubbles, macroeconomic factors, supply security, probit regression, determinants
How to Cite
Ni, M., & Wang, X. (2024). Detecting bubbles in world aluminum prices: Evidence from GSADF test. Journal of Business Economics and Management, 25(6), 1120–1139. https://doi.org/10.3846/jbem.2024.22262
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Galán-Gutiérrez, J. A., & Martín-García, R. (2022). Fundamentals vs. financialization during extreme events: From backwardation to contango, a copper market analysis during the COVID-19 pandemic. Mathematics, 10(4), Article 559. https://doi.org/10.3390/math10040559
Gürkaynak, R. S. (2008). Econometric tests of asset price bubbles: Taking stock*. Journal of Economic Surveys, 22(1), 166–186. https://doi.org/10.1111/j.1467-6419.2007.00530.x
Henckens, M. L. C. M., & Worrell, E. (2020). Reviewing the availability of copper and nickel for future generations. The balance between production growth, sustainability and recycling rates. Journal of Cleaner Production, 264, Article 121460. https://doi.org/10.1016/j.jclepro.2020.121460
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International Monetary Fund. (n.d.). IMF data. https://www.imf.org/en/Data
Jiang, W., & Chen, Y. (2022). The time-frequency connectedness among carbon, traditional/new energy and material markets of China in pre- and post-COVID-19 outbreak periods. Energy, 246, Article 123320. https://doi.org/10.1016/j.energy.2022.123320
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Liaqat, A., Nazir, M. S., & Ahmad, I. (2019). Identification of multiple stock bubbles in an emerging market: Application of GSADF approach. Economic Change and Restructuring, 52(3), 301–326. https://doi.org/10.1007/s10644-018-9230-0
Liaqat, A., Nazir, M. S., Ahmad, I., Mirza, H. H., & Anwar, F. (2020). Do stock price bubbles correlate between China and Pakistan? An inquiry of pre- and post-Chinese investment in Pakistani capital market under China-Pakistan Economic Corridor regime. International Journal of Finance & Economics, 25(3), 323–335. https://doi.org/10.1002/ijfe.1754
Liu, Y., Yang, C., Huang, K., & Gui, W. (2020). Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network. Knowledge-Based Systems, 188, Article 105006. https://doi.org/10.1016/j.knosys.2019.105006
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Manberger, A., & Stenqvist, B. (2018). Global metal flows in the renewable energy transition: Exploring the effects of substitutes, technological mix and development. Energy Policy, 119, 226–241. https://doi.org/10.1016/j.enpol.2018.04.056
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