Mariana Chambino -Polytechnic Institute of Setúbal, (ESCE/IPS), 2910-761 Setúbal, Portugal
Rui Dias -Polytechnic Institute of Setúbal, (ESCE/IPS), 2910-761 Setúbal, Portugal
Paulo Alexandre – Polytechnic Institute of Setúbal, (ESCE/IPS), 2910-761 Setúbal, Portugal
Rosa Galvão – Polytechnic Institute of Setúbal, (ESCE/IPS), 2910-761 Setúbal, Portugal
Keywords:
Events of 2020 and 2022;
Energy metals;
Persistence;
Arbitrage
Abstract: The drive for sustainability and carbon emissions reduction is fueling the demand for clean energy solutions, with energy metals playing a crucial role in this transition. The environmental and ethical implications of mining and supplying these materials impact market dynamics, influenced by environmental regulations and consumer preferences for sustainable sources. This article aims to analyze the persistence of commodities, including gold (XAU), silver (XAG), platinum (XPT), aluminum (MAL3), nickel futures (NICKELc1), and copper futures (HGU3), from July 13, 2018, to July 11, 2023. The study divides the sample into four sub-periods: tranquil, COVID-19 pandemic, pre-conflict, and conflict (Russian invasion of Ukraine). Results indicate varying behaviors, with some commodities showing anti-persistence, suggesting distinct patterns, while others exhibit efficiency or random walk behavior. Understanding these patterns and market efficiency is valuable for informed investment strategies and risk management amid evolving global economic conditions.
Download file
LIMEN Conference
9th International Scientific-Business Conference – LIMEN 2023 – Leadership, Innovation, Management and Economics: Integrated Politics of Research – SELECTED PAPERS, Hybrid (Graz University of Technology, Graz, Austria), December 7, 2023
LIMEN Selected papers published by the Association of Economists and Managers of the Balkans, Belgrade, Serbia
LIMEN Conference 2023 Selected papers: ISBN 978-86-80194-79-0, ISSN 2683-6149, DOI: https://doi.org/10.31410/LIMEN.S.P.2023
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission.
Suggested citation
Chambino, M., Dias, R., Alexandre, P., & Galvão, R. (2023). Eco-Metals Unveiled: A Deep Dive into Commodity Resilience. In V. Bevanda (Ed.), International Scientific-Business Conference – LIMEN 2023: Vol 9. Selected papers (pp. 141-149). Association of Economists and Managers of the Balkans. https://doi.org/10.31410/LIMEN.S.P.2023.141
References
Adekoya, O. B., & Oliyide, J. A. (2022). Commodity and financial markets’ fear before and during COVID-19 pandemic: Persistence and causality analyses. Resources Policy, 76. https://doi.org/10.1016/j.resourpol.2022.102598
Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20(2), 249–272. https://doi.org/10.1016/S0261-5606(00)00048-6
Dias, R., Alexandre, P., Teixeira, N., & Chambino, M. (2023). Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies. Energies, 16(13). https://doi.org/10.3390/en16135232
Dias, R., Chambino, M., & Horta, N. H. (2023). Long-Term Dependencies in Central European Stock Markets: A Crisp-Set Analysis. Economic Analysis Letters. https://doi.org/10.58567/eal02010002
Dias, R., Horta, N., & Chambino, M. (2023). Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts. Energies, 16(9). https://doi.org/10.3390/en16093937
Dias, R., Teixeira, N., Alexandre, P., & Chambino, M. (2023). Exploring the Connection between Clean and Dirty Energy: Implications for the Transition to a Carbon-Resilient Economy. Energies, 16(13), 4982. https://doi.org/10.3390/en16134982
Dias, R. M., Chambino, M., Teixeira, N., Alexandre, P., & Heliodoro, P. (2023). Balancing Portfolios with Metals: A Safe Haven for Green Energy Investors? https://doi.org/10.20944/preprints202309.1249.v1
Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517
Dutta, A., Bouri, E., Das, D., & Roubaud, D. (2020). Assessment and optimization of clean energy equity risks and commodity price volatility indexes: Implications for sustainability. Journal of Cleaner Production, 243, 118669. https://doi.org/10.1016/J.JCLEPRO.2019.118669
Erer, D., Erer, E., & Güngör, S. (2023). The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-023-00484-4
Fama, E. F. (1965). Random Walks in Stock Market Prices. Financial Analysts Journal, 21(5), 55-59. https://doi.org/10.2469/faj.v21.n5.55
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383. https://doi.org/10.2307/2325486
Fama, E. F. (1991). Efficient Capital Markets: II. The Journal of Finance, 46(5), 1575. https://doi.org/10.2307/2328565
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3–25. https://doi.org/10.1016/0304-405X(88)90020-7
Guedes, E. F., Santos, R. P. C., Figueredo, L. H. R., da Silva, P. A., Dias, R. M. T. S., & Zebende, G. F. (2022). Efficiency and Long-Range Correlation in G-20 Stock Indexes: A Sliding Windows Approach. Fluctuation and Noise Letters, 21(04). https://doi.org/10.1142/s021947752250033x
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
Memon, B. A., Yao, H., & Naveed, H. M. (2022). Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets. Resources Policy, 77. https://doi.org/10.1016/j.resourpol.2022.102715
Mokni, K., & Youssef, M. (2020). Empirical analysis of the cross-interdependence between crude oil and agricultural commodity markets. Review of Financial Economics, 38(4). https://doi.org/10.1002/rfe.1096
Santana, T. P., Horta, N., Revez, C., Dias, R. M. T. S., & Zebende, G. F. (2023). Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COVID-19 Case Study. Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15053945
Wang, H., & Jia, N. (2019). Multifractal analysis of the multivariate cross-correlation between metal futures and spot markets in China. Xitong Gongcheng Lilun Yu Shijian/System Engineering Theory and Practice, 39(9). https://doi.org/10.12011/1000-6788-2018-1340-13
Wang, Y., Bouri, E., Fareed, Z., & Dai, Y. (2022). Geopolitical risk and the systemic risk in the commodity markets under the war in Ukraine. Finance Research Letters, 49, 103066.
Zebende, G. F., Santos Dias, R. M. T., & de Aguiar, L. C. (2022). Stock market efficiency: An intraday case of study about the G-20 group. Heliyon, 8(1), e08808. https://doi.org/10.1016/j.heliyon.2022.e08808