Temiz Enerji Hisse Senetleri Üzerinde Belirsizliğin Zamanla Değişen ve Dinamik Etkileri: Ekonometrik Bir Yaklaşım

Year 2025, Issue: Özel Sayı 3, 242 - 261, 31.12.2025

Naime İrem Duran

https://doi.org/10.33203/mfy.1828640

Abstract

Bu çalışmanın amacı, temiz enerji endeksleri ile piyasaları etkileyen belirsizlik endeksleri arasındaki uzun ve kısa dönem ilişkileri incelemektir. Bu amaçla temiz enerji göstergeleri olarak NASDAQ Clean Edge Temiz Enerji Endeksi (CELS) ve FTSE Çevresel Fırsatlar Endeksi (FTEOUS) bağımlı değişken, belirsizliği gösteren faktörler olarak da İklim Politikası Belirsizlik Endeksi(CPU), Twitter Tabanlı Ekonomik Belirsizlik Endeksi(TEU), ABD-Çin Jeopolitik Gerilim Endeksi (UCT) ve Sürdürülebilirlik Belirsizlik Endeksi(ESGUI) bağımsız değişkenler olarak belirlenmiştir. İki bağımlı değişkenin belirlenen bağımsız değişkenlerle uzun dönem ilişkilerini incelemek için ARDL modeli, kısa dönem nedensellik ilişkilerini belirlemek için ise Zamana göre değişen Granger Nedensellik analizi yapılmıştır. Ocak 2012-2023 aylık verilerini içeren analiz sonuçları ESGUI değişkeninin her iki temiz enerji endeksini uzun dönemde etkilediğini gösterirken, kısa dönemli nedensellik analizine göre TEU değişkeni her iki endeksle nedensellik ilişkisine işaret etmektedir. Sonuç olarak elde edilen bulgular, bu endeksler üzerinde, endeksin yapısı ve içerdiği sektörlerle birlikte bilginin yayılım kanallarının etkisi olabileceğini göstermektedir.

Keywords

Sürdürülebi1ir Finans , ARDL modeli , Zamana Göre Değişen Nedensellik Analizi

Time-Varying and Dynamic Effects of Uncertainties on Clean Energy Stocks: An Econometric Approach

Abstract

The aim of this study is to analyze the short- and long-run effects of new-generation uncertainty indicators—including climate policy uncertainty (CPU), social-media-based economic uncertainty (TEU-USA), sustainability uncertainty (ESGUI), and the U.S.–China Geopolitical Tension Index (UCT)—on U.S. green equity markets. Accordingly, for the period January 2012–January 2023, the effects on Nasdaq Clean Edge Clean Energy Index (CELS) and the FTSE Environmental Opportunities U.S. Index (FTEOUS) were examined using ARDL and TVGC methods . The findings indicate that ESGUI is a key long-run determinant for both green indices, while TEU affects only the CELS index in the long and short run . The fact TEU is significant only for CELS suggests that the FTEOUS index exhibits greater resilience to social-media-based economic uncertainty. Overall, the results reveal that the responses of green financial markets to uncertainty shocks may vary depending on index structure, sectoral composition, and information diffusion channels.

Keywords

Sustainable Finance , ARDL Model , Time-Varying Granger Causality Analysis

References

• Afonso, A., Mignon, V. & Saadaoui, J. (2024). On the time-varying impact of China's bilateral political relations on its trading partners: “Doux commerce” or “trade follows the flag?”. China Economic Review, 85, 102184. https://doi.org/10.1016/j.chieco.2024.102184
• Akadiri, S. S., & Ozkan, O. (2025). Energy, critical minerals, and precious metals: Navigating interconnectedness and portfolio strategies in investment risk management. Resources Policy, 110, 105747. https://doi.org/10.1016/j.resourpol.2025.105747Get rights and content
• Alharbey, M. & Ben-Salha, O. (2024) Does climate policy uncertainty predict renewable energy stocks? A quantile-based (a)symmetric causality analysis, Energy Strategy Reviews, 54, 101465. https://doi.org/10.1016/j.esr.2024.101465
• Baker, S. R., Bloom, N., Davis, S. J., & Sammon, M. C. (2021). The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10(4), 742–758. https://doi.org/10.1093/rapstu/raaa008
• Balcilar, M., Ozdemir, Z. A., & Arslanturk, Y. (2010). Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Economics, 32(6), 1398–1410. https://doi.org/10.1016/j.eneco.2010.05.015
• Balcılar, M., & Demirer, R. (2015). Effect of global shocks and volatility on herd behavior in an emerging market: evidence from Borsa Istanbul. Emerging Markets Finance and Trade, 51(1), 140–159. https://doi.org/10.1016/j.eneco.2010.05.015
• Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
• Baker, S.R., Bloom, N., Davis, S., & Renault, T.(2021). Twitter-derived measures of economic uncertainty.https://www.policyuncertainty.com/media/Twitter_Uncertainty_5_13_2021.pdf .
• Banerjee, Ameet-K., Sensoy, A. & Goodell, John W. (2024). Connectivity and spillover during crises: highlighting the prominent and growing role of green energy. Energy Economics, 129, 107224. https://doi.org/10.1016/j.eneco.2023.107224.
• Becker, R., Enders, W., & Lee, J. (2006). A Stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381–409. https://doi.org/10.1111/j.1467-9892.2006.00478.x
• Bouri, E., Demirer, R., Gupta, R., & Pierdzioch, C. (2022). Climate policy uncertainty and the price dynamics of green and brown energy stocks. Finance Research Letters, 47(Part B), 1–5. https://doi.org/10.1016/j.frl.2022.102740
• Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. https://doi.org/10.3982/ECTA6248
• Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for Testing the Constancy of Regression Relationships Over Time. Journal of the Royal Statistical Society: Series B (Methodological), 37(2), 149–163. https://doi.org/10.1111/j.2517-6161.1975.tb01532.x
• Cai, Y., Mignon, V. & Saadaoui, J. (2022). Not all political relation shocks are alike: Assessing the impacts of US–China tensions on the oil market. Energy Economics, 114, 106199. https://doi.org/10.1016/j.eneco.2022.106199.
• Cai, Y., Li, W., Yao, C. & Zhao, Y. (2024). The in-situ excitation on internal electrode of DBD-OES system for cadmium sensitive detection. Spectrochimica Acta Part B: Atomic Spectroscopy, 215, 106907. https://doi.org/10.1016/j.sab.2024.106907.
• Cai,Y. (2025). US-China tensions, global supply chains pressure, and global economy, Economics Letters, 250, 112283. https://doi.org/10.1016/j.econlet.2025.112283.
• Chatziantoniou, I., Filis, G., Eeckels, B., & Apostolakis, A. (2016). Oil prices, tourism income and economic growth: A structural VAR approach for European Mediterranean countries. Tourism Management, 54, 298–302. https://doi.org/10.1016/j.tourman.2012.10.012
• Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
• Du, Y, Ju, J, Ramirez, Carlos D. & Yao, X. (2017). Bilateral trade and shocks in political relations: Evidence from China and some of its major trading partners, 1990–2013. Journal of International Economics,Volume 108, Pages 211-225.https://doi.org/ 10.1016/j.jinteco.2017.07.002.
• Enders, W. (2014). Applied econometric time series (4th ed.). Wiley.
• Enders, W., & Lee, J. (2012). The flexible Fourier form and testing for structural change in time series data. Journal of Applied Econometrics, 27(3), 579–601. https://doi.org/10.1016/j.econlet.2012.04.081
• Engle, R. F., Giglio, S., Kelly, B., Lee, H., & Stroebel, J. (2020). Hedging climate change news. The Review of Financial Studies, 33(3), 1184–1216. https://doi.org/10.1093/rfs/hhz072
• ETFdb. (2025). First Trust Nasdaq Clean Edge Green Energy Index Fund (QCLN) – ETF Profile. ETF Database. https://etfdb.com/etf/QCLN/
• Gavriilidis, K. (2021). Measuring Climate Policy Uncertainty. SSRN. https://ssrn.com/abstract=3847388
• Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791
• Granger, C. W. J., & Yoon, G. (2002). Hidden cointegration. University of California, San Diego,Economics Working Paper No.2002-02. https://escholarship.org/uc/item/9qn5f61j
• Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
• Harvey, A. C. (1976). Estimating regression models with multiplicative heteroscedasticity. Econometrica, 44(3), 461–465. https://doi.org/10.2307/1913974.
• Henriques, I. & Sadorsky, P. (2018). Investor implications of divesting from fossil fuels. Global Finance Journal, 38, 30-44. https://doi.org/10.1016/j.gfj.2017.10.004.
• Ivanovski, K. & Marinucci, N. (2021). Policy uncertainty and renewable energy: Exploring the implications for global energy transitions, energy security, and environmental risk management. Energy Research & Social Science, 82, 102415. https://doi.org/10.1016/j.erss.2021.102415.
• Khurshid, A., Khan, K., Cifuentes-Faura, J. & Chen, Y. (2024). Asymmetric multifractality: Comparative efficiency analysis of global technological and renewable energy prices using MFDFA and A-MFDFA approaches. Energy, 289, 130106. https://doi.org/10.1016/j.energy.2023.130106
• Lu, X., & Lang, Q. (2023). Categorial economic policy uncertainty indices or Twitter-based uncertainty indices? Evidence from Chinese stock market. Finance Research Letters, 55(Part B), 103936. https://doi.org/10.1016/j.frl.2023.103936
• Ma, Y., Li, S. & Zhou, M. (2025). Twitter-based market uncertainty and global stock volatility predictability. The North American Journal of Economics and Finance, 75, Part A, 102256. .https://doi.org/10.1016/j.najef.2024.102256.
• Memon, Bilal-A, Aslam, F., Asadova, S. & Ferreira, P. (2023). Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic. Heliyon, 9, 12, e22694. https://doi.org/10.1016/j.heliyon.2023.e22694.
• Narayan, P. K., & Smyth, R. (2005). Trade liberalization and economic growth in Fiji: An empirical assessment using the ARDL approach. Journal of the Asia Pacific Economy, 10(1), 96–115. https://doi.org/10.1080/1354786042000309099
• Nasdaq. (2025, June 6). Nasdaq Clean Edge Green Energy Index (CELS) Fact Sheet (Version: 2025-Q2). Nasdaq Global Index Watch. https://indexes.nasdaqomx.com/docs/methodology_CELS.pdf
• Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. https://doi.org/10.2307/1913610.
• Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519–1554. https://doi.org/10.1111/1468-0262.00256
• Ongan, S., Gocer, I. & Isik, C (2025). Introducing the new ESG-based sustainability uncertainty index (ESGUI). Sustainable Development. 33(3), 4457-4467. https://doi.org/10.1002/sd.3351.
• Pástor, Ľ., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219–1264. https://doi.org/10.1111/j.1540-6261.2012.01746.x
• Pesaran, M. H., & Shin, Y. (1999).An autoregressive distributed lag modelling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch centennial symposium (pp 371-413). Cambridge University Press.
• Pesaran, M. H., Shin, Y., & Smith, R. J. (2001).Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616.
• Pindyck, R. S. (1991). Irreversibility, uncertainty, and investment. Journal of Economic Literature, 29(3), 1110–1148.
• Reboredo, Juan C. (2015). Is there dependence and systemic risk between oil and renewable energy stock prices?. Energy Economics, 48, 32-45. https://doi.org/10.1016/j.eneco.2014.12.009.
• Rogers, John H., Sun, B. & Sun, T. (2024). U.S.-China tension. SSRN. https://ssrn.com/abstract=4815838
• Sadorsky, P. (2012). Modeling renewable energy company risk. Energy Policy, 40, 39-48. https://doi.org/10.1016/j.enpol.2010.06.064
• Sarker, P. K., Lau, C. K. M., & Pradhan, A. K. (2023). Asymmetric effects of climate policy uncertainty and energy prices on bitcoin prices. Innovation and Green Development, 2(2), 1–6. https://doi.org/10.1016/j.igd.2023.100048
• Seetharam, Y. & Nyakurukwa, K. (2025). The impact of Twitter economic policy uncertainty on clean energy stocks. Computational Economics. https://doi.org/10.1007/s10614-025-10990-5
• Sen, C. & Chakrabarti, G. (2024). Exploring the risk dynamics of US green energy stocks: A green time-varying beta approach. Energy Economics, 139, 107951. https://doi.org/10.1016/j.eneco.2024.107951
• Sinha, A., Murshed, M., Das, N. & Saha, T. (2025). Modeling renewable energy market performance under climate policy uncertainty: A novel multivariate quantile causality analysis. Risk Analysis, 45(7),1984-2038. https://doi.org/10.1111/risa.17714
• Shafiullah, M., Miah, MD., Alam, Md Samsul & Atif, M. (2021). Does economic policy uncertainty affect renewable energy consumption? Renewable Energy, 179, 1500-1521. https://doi.org/10.1016/j.renene.2021.07.092
• Shi, S., Hurn, S. and Phillips, P.C. (2020). Causal change detection in possibly integrated systems: Revisiting the money–ıncome relationship. Journal of Financial Econometrics, 18(1), 158-180. https://doi.org/10.1093/jjfinec/nbz004
• Tedeschi, M., Foglia, M., Bouri, E. & Dai, Peng-F. (2024). How does climate policy uncertainty affect financial markets? Evidence from Europe. Economics Letters, 234, 111443.https://doi.org/10.1016/j.econlet.2023.111443.
• Uğurlu-Y., E. & Dinç-C, Ö. (2024). Climate policy uncertainty, media coverage of climate change, and energy markets: New evidence from time-varying causality analysis, Energy and Climate Change, 5, 100134. https://doi.org/10.1016/j.egycc.2024.100134.
• Urom, C., Mzoughi, H., Ndubuisi, G., & Guesmi, K. (2022). Dynamic dependence between clean investments and economic policy uncertainty. MERIT Working Paper No.2022-07. Maastricht University. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://cris.maastrichtuniversity.nl/ws/files/106567847/wp2022_027.pdf
• Yang, K., Wei, Y., Li, S. & He, J. (2021). Geopolitical risk and renewable energy stock markets: An insight from multiscale dynamic risk spillover. Journal of Cleaner Production, 279, 123429. https://doi.org/10.1016/j.jclepro.2020.123429.
• Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. https://doi.org/10.2307/1391541.