Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility

Sümeyra Gazel

doi:10.26650/ibr.2026.55.1640485

Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility

Abstract

This study investigates the causal relationship between clean energy (PBW) and dirty energy (USO) ETFs and the Cable News-based Economic Policy Uncertainty (CN-EPU) index in the U.S., with a focus on the COVID-19 pandemic’s economic impact. Employing symmetric, asymmetric, and time-varying causality tests, we assess whether economic uncertainty influences energy ETFs from April 17, 2020, to November 30, 2023. The results indicate a significant causal relationship between the news-based EPU index and USO under both symmetric and asymmetric conditions. Notably, asymmetric analysis reveals that the negative components of uncertainty emerge as the primary drivers of this relationship, suggesting that investor sentiment reacts more strongly to negative economic signals in the fossil fuel market. In contrast, PBW does not exhibit a consistent causal link with economic uncertainty throughout the study period. However, time-varying analysis identifies short-term causal effects in 2021, coinciding with key policy developments, such as the Fit for 55 initiative, highlighting the role of regulatory shifts in clean energy investment dynamics. These findings underscore the asymmetric impact of economic uncertainty on energy markets and emphasize the significance of policy stability in shaping investor behavior, particularly in times of heightened economic volatility.

Keywords

References

Antonakakis N, Gupta R, Kollias C, Papadamou S. Geopolitical risks and the oil-stock nexus over 1899–2016. Finance Research Letters. 2017; 23:165–173.google scholar
Barnett A, Mumtaz H, Theodoridis K. Forecasting UK GDP growth, inflation and interest rates under structural change: A comparison of models with time-varying parameters. Bank of England Working Papers. 2012; 450:1–55. google scholar
Bekiros S, Gupta R, Paccagnini A. Oil price forecastability and economic uncertainty. Economics Letters. 2015; 132:125–128. google scholar
Bergbrant MC, Bradley D. Did they just say that? Using AI to extract economic policy uncertainty from cable news networks. SSRN [Internet]. 2022 [cited 2023 Dec 9]. Available from: https://ssrn.com/abstract=4059681 google scholar
Bouri E, Iqbal N, Klein T. Climate policy uncertainty and the price dynamics of green and brown energy stocks. Finance Research Letters. 2022; 47:1–5. google scholar
Daxhammer, R,J., Fascar, M., Papp, Z. Behavioral Finance: Limited Rationality in Financial Markets (3rd ed.). München: UVK Verlag google scholar
Dolado JL, Lütkepohl H. Making Wald tests work for cointegrated VAR systems. Econometric Reviews. 1996; 15:369–386. google scholar
Dotsis G, Psychoyios D. The great divergence between the USO fund and WTI spot prices. Applied Economics Letters. 2022;30(13):1749–1753. google scholar

Dutta A, Bouri E, Saeed T. News-based equity market uncertainty and crude oil volatility. Energy. 2021; 222:1–13. google scholar
Dutta A, Bouri E, Saeed T, Vo XV. Impact of energy sector volatility on clean energy assets. Energy. 2020; 212:1–11. google scholar
Dutta A, Bouri E, Rothovius T, Uddin GS. Climate risk and green investment: New evidence. Energy. 2023; 265:1–13. google scholar
Dutta A, Dutta P. Geopolitical risk and renewable energy asset prices: Implications for sustainable development. Renewable Energy. 2022; 196:518–525.google scholar
Erdogan S, Gedikli A, Kirca M. A note on time-varying causality between natural gas consumption and economic growth in Turkey. Resources Policy. 2019; 64:1–5. google scholar
Farid S., Karim S., Naeem M.A., Nepal R., Jamasb T. Co-movement between dirty and clean energy: A time-frequency perspective. Energy Economics. 2023; 119:106565. DOI: 10.1016/j.eneco.2023.106565 google scholar
Gargallo P, Lample L, Miguel J, Salvador M. Dynamic comparison of portfolio risk: Clean vs dirty energy. Finance Research Letters. 2022;47(Part A):1–5. DOI: 10.1016/j.frl.2022.102957. google scholar
Granger CWJ, Yoon G. Hidden cointegration. Department of Economics Working Paper, University of California. 2002;(No. 2002-02). google scholar
Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 1969; 37:424–438. google scholar
Hacker RS, Hatemi-J A. Tests for causality between integrated variables using asymptotic and bootstrap distributions: Theory and application. Applied Economics. 2006;38(13):1489–1500. google scholar
Hatemi-J A. Asymmetric causality tests with an application. Empirical Economics. 2012;43(1):447–456.google scholar
Henriques. I., Sadorsky, P. Oil prices and the stock prices of alternative energy companies, Energy Economics, 30; 998-1010, ISSN 0140-9883, https://doi.org/10.1016/j.eneco.2007.11.001. (2008).google scholar
Hong J, Crichton W, Zhang H, et al. Analysis of faces in a decade of US cable TV news. ACM Conference on Knowledge Discovery and Data Mining (KDD). 2021:1–5.
IEA, International Energy Agency (2023). World Energy Investment. https://iea.blob.core.windows.net/assets/8834d3af-af60-4df0-9643-72e2684f7221/WorldEnergyInvestment2023.pdf (IEA)google scholar
Ivanov SI. The influence of ETFs on the price discovery of gold, silver and oil. J Econ Finan. 2011; 37:453–462.google scholar
Li X, Wei Y, Chen X, Ma F, Liang C, Chen W. Which uncertainty is powerful to forecast crude oil market volatility? New evidence. Int J Fin Econ. 2020; 27:4279–4297. google scholar
Liang C, Liao Y, Ma F, Zhu B. United States oil fund volatility prediction: The roles of leverage effect and jumps. Empirical Economics. 2022; 62:2239–2262.google scholar
Lin AL, Chang H-Y. Volatility transmission from equity, bulk shipping, and commodity markets to oil ETF and energy fund – A GARCH-MIDAS model. Mathematics. 2020; 8:1–21. google scholar
Mand AA, Ghafoor A, Sifat I. Time-varying price dynamics of clean and dirty energy portfolios. Journal of Environmental Management. 2023; 337:1–17. google scholar
Naeem M, Umar Z, Ahmed S, Ferrouhi EM. Dynamic dependence between ETFs and crude oil prices by using EGARCH-Copula approach. Physica A. 2020; 557:1–15. google scholar
Neri F, Magarino IG. Simulating and modelling the DAX index and the USO ETF financial time series by using a simple agent-based learning architecture. Expert Systems. 2019; 37:1–11. google scholar
Ozer M, Kirca M. Analysis of symmetric and asymmetric causal relationships between current account deficit and real exchange rate in Turkey. 1st International Economics and Business Symposium (ECONBUSS). 2018;190–200. google scholar
Philips PC, Shi S, Yu J. Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P. Int Econ Rev. 2015;56(4):1043–1078. google scholar
Shaikh I. On the relation between pandemic disease outbreak news and crude oil, gold, gold mining, silver and energy markets. Resources Policy. 2021; 72:1–15.google scholar
Silva JCA. An S-shaped crude oil price return-implied volatility relation: Parametric and nonparametric estimations. International Journal of Economics and Finance. 2017;9(12):1–18. google scholar
Sun X, Chen X, Wang J, Li J. Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains. North American Journal of Economics and Finance. 2020; 51:1–15. google scholar
Swarup S, Kushwaha GS. Effects of temperature rise on clean energy-based capital market investments: Neural network-based Granger causality analysis. Sustainability. 2022; 14:11163. google scholar
Tang CF. Wagner’s law versus Keynesian hypothesis: New evidence from recursive regression-based causality approaches. ICFAI Journal of Public Finance. 2008;6(4):29–38. google scholar
Tiwari AK, Aye GC, Gupta R, Gkillas K. Gold-oil dependence dynamics and the role of geopolitical risks: Evidence from a Markov-switching time-varying copula model. Energy Economics. 2020; 88:104748.google scholar
Tiwari AK, Trabelsi N, Abakah EJA, Nasreen S, Lee C-C. An empirical analysis of the dynamic relationship between clean and dirty energy markets. Energy Economics. 2023; 124:106766. google scholar
Toda HY, Yamamoto T. Statistical inference in vector autoregressions with possibly integrated processes. J Econom. 1995; 66:225–250. google scholar
Umar M, Riaz Y, Yousaf I. Impact of Russian-Ukraine war on clean energy, conventional energy, and metal markets: Evidence from event study approach. Resource Policy. 2022; 79:102966. google scholar
Wang J, Ma F, Bouri E, Zhong J. Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions. Energy Economics. 2022; 18:105904. google scholar
Wei Y, Liu J, Lai X, Hu Y. Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty? Energy Economics. 2017; 68:141–150. google scholar
Wen Z, Gong X, Ma D, Xu Y. Intraday momentum and return predictability: Evidence from the crude oil market. Economic Modelling. 2021; 95:374–384.google scholar
Yao Y., Ivanovski K., Inekwe J., Smyth R. Human capital and energy consumption: Evidence from OECD countries. Energy Economics. 2019; 84:104534. DOI: 10.1016/j.eneco.2019.104534. google scholar
Yilanci V, Bozoklu S. Price and trade volume relationship in Turkish stock market: A time-varying asymmetric causality analysis. Ege Academic Review. 2014; 14:211–220. google scholar
Yilanci V, Kilci EN. The role of economic policy uncertainty and geopolitical risk in predicting prices of precious metals: Evidence from a time-varying bootstrap causality test. Resource Policy. 2021; 72:102039. google scholar
Yousfi M, Bouzgarrou H. Geopolitical risk, economic policy uncertainty, and dynamic connectedness between clean energy, conventional energy, and food markets. Environmental Science and Pollution Research. 2024; 31:4925–4945 google scholar

Details

Primary Language

English

Subjects

Environment and Climate Finance, Financial Forecast and Modelling, Financial Markets and Institutions

Journal Section

Research Article

Authors

Sümeyra Gazel ^*
0000-0001-8687-0928
Türkiye

Publication Date

May 22, 2026

Submission Date

February 15, 2025

Acceptance Date

January 31, 2026

Published in Issue

Year 2026 Volume: 55 Number: 1

DOI

https://doi.org/10.26650/ibr.2026.55.1640485

IZ

https://izlik.org/JA57AP54TG

Cite

RIS / Bibtex

APA

Gazel, S. (2026). Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility. Istanbul Business Research, 55(1), 95-112. https://doi.org/10.26650/ibr.2026.55.1640485

AMA

1.Gazel S. Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility. IBR. 2026;55(1):95-112. doi:10.26650/ibr.2026.55.1640485

Chicago

Gazel, Sümeyra. 2026. “Clean Vs. Dirty Energy ETFs: How Economic Uncertainty Drives Market Volatility”. Istanbul Business Research 55 (1): 95-112. https://doi.org/10.26650/ibr.2026.55.1640485.

EndNote

Gazel S (May 1, 2026) Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility. Istanbul Business Research 55 1 95–112.

IEEE

[1]S. Gazel, “Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility”, IBR, vol. 55, no. 1, pp. 95–112, May 2026, doi: 10.26650/ibr.2026.55.1640485.

ISNAD

Gazel, Sümeyra. “Clean Vs. Dirty Energy ETFs: How Economic Uncertainty Drives Market Volatility”. Istanbul Business Research 55/1 (May 1, 2026): 95-112. https://doi.org/10.26650/ibr.2026.55.1640485.

JAMA

1.Gazel S. Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility. IBR. 2026;55:95–112.

MLA

Gazel, Sümeyra. “Clean Vs. Dirty Energy ETFs: How Economic Uncertainty Drives Market Volatility”. Istanbul Business Research, vol. 55, no. 1, May 2026, pp. 95-112, doi:10.26650/ibr.2026.55.1640485.

Vancouver

1.Sümeyra Gazel. Clean vs. dirty energy ETFs: How economic uncertainty drives market volatility. IBR. 2026 May 1;55(1):95-112. doi:10.26650/ibr.2026.55.1640485