Enerji Korkusunun Temiz Enerji ETF Volatilitesi Üzerine Etkisi: TVP-VAR Uygulaması

Year 2023, Volume: 23 Issue: 1, 215 - 230, 30.03.2023

Arife Özdemir Höl , Nazlıgül Gülcan , Namıka Boyacıoğlu

https://doi.org/10.11616/asbi.1212753

Abstract

Son dönemlerde hem küresel ısınmadan kaynaklı iklim değişikliğiyle mücadele eylem planları kapsamında hem de ekonomilerine katkıda bulunmak amacıyla tüm dünyada temiz enerjiye olan ilgi artmıştır. Temiz enerji sektöründe yer alan yatırımcılara yol gösterici olması açısından bu çalışmada enerji korkusunun temiz enerji yatırım fonları (ETF) volatilitesine etkisi araştırılmaktadır. Araştırmada enerji korkusunu temsilen CBOE Ham Petrol Volatilite Endeksi (OVX) ile CBOE Enerji Sektörü ETF Volatilite Endeksi (VXXLE), temiz enerji ETF’lerini temsilen de iShares Global Clean Energy ETF (ICLN), First Trust NASDAQ Clean Edge Green Energy ETF (QCLN), Invesco WilderHill Clean Energy ETF (PBW) dikkate alınmıştır. Araştırma kapsamı 02.01.2015-11.02.2022 dönemi volatilite serilerinden oluşmaktadır. Antonakakis vd. (2019a) tarafından geliştirilen TVP-VAR yönteminin kullanıldığı çalışma sonucunda PBW temiz enerji ETF ve VXXLE’nin volatiliteyi yaydığı, ICLN, QCLN temiz enerji ETF’i ve OVX’in ise volatiliteyi aldığı, temiz enerji ETF’lerini tek etkileyen korku endeksinin VXXLE olduğu, OVX’in ise temiz enerji ETF’lerini etkilemediği sonucuna ulaşılmıştır. Ayrıca korku endekslerinin varyansında meydana gelen değişmelerin büyük çoğunluğunun kendileri tarafından açıklandığı ve korku endekslerinin birbirlerini etkileme güçlerinin daha fazla olduğu, temiz enerji ETF’lerinin varyansında meydana gelen değişmelerin büyük çoğunluğunun diğer temiz enerji ETF’leri tarafından açıklandığı belirlenmiştir.

Keywords

Temiz Enerji, ETF, Volatilite, TVP-VAR Model

Supporting Institution

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Project Number

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Thanks

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Effect of Energy Fear on Clean Energy ETF Volatility: TVP-VAR Application

Abstract

In recent times, interest in clean energy has increased all over the world, both within the scope of action plans to combat climate change caused by global warming and in order to contribute to their economies. In this paper, the effect of energy fear on clean energy Exchange Traded Fund (ETF) volatility is investigated in order to guide investors in the clean energy sector. In the research, the CBOE Crude Oil Volatility Index (OVX) and the CBOE Energy Sector ETF Volatility Index (VXXLE) represent energy fear and the iShares Global Clean Energy ETF (ICLN), First Trust NASDAQ Clean Edge Green Energy ETF (QCLN), Invesco WilderHill Clean Energy ETF (PBW) represents clean energy ETFs. The scope of the research consists of volatility series for the period 02.01.2015-11.02.2022. As a result of the study, in which the TVP-VAR method developed by Antonakakis et al. (2019a) was used, VXXLE and PBW clean energy ETFs are net volatility transmitter; the ICLN, QCLN clean energy ETFs and OVX are net volatility receiver, the only fear index affecting clean energy ETFs is the VXXLE; OVX did not affect clean energy ETFs. In addition, it has been determined that the majority of the changes in the variance of the fear indices are explained by themselves and the fear indices have more power to affect each other, and the vast majority of the changes in the variance of clean energy ETFs are explained by other clean energy ETFs.

Keywords

Clean Energy, ETF, Volatility, TVP-VAR Model

Project Number

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References

• Ahmad, W. (2017). On the Dynamic Dependence and Investment Performance of Crude Oil and Clean Energy Stocks. Research in International Business and Finance, 42, s.376-389. http://dx.doi.org/10.1016/j.ribaf.2017.07.140
• Ahmad, W., Sadorsky, P. ve Sharma, A. (2018). Optimal Hedge Ratios for Clean Energy Equities. Economic Modelling, 72, s.278-295. https://doi.org/10.1016/j.econmod.2018.02.008
• Antonakakis, N., Gabauer, D. ve Gupta, R. (2019b). International Monetary Policy Spillovers: Evidence from a Time-varying Parameter Vector Autoregression. International Review of Financial Analysis, 65, 101382. https://doi.org/10.1016/j.irfa.2019.101382
• Antonakakis, N., Cunado, J., Filis, G., Gabauer, D. ve De Gracia, F. P. (2019a). Oil and Asset Classes Implied Volatilities: Dynamic Connectedness and Investment Strategies. Available at SSRN 3399996. http://dx.doi.org/10.2139/ssrn.3399996
• Barunik, J. ve Krehlik, T. (2018). Measuring the Frequency Dynamics of Financial and Connectedness and Systemic Risk. J. Financ. Economet. 16, s.271–296. https://doi.org/10.1093/jjfinec/nby001
• Bhattacharya, M., Paramati, S. R., Ozturk, I. ve Bhattacharya, S. (2016). The Effect of Renewable Energy Consumption on Economic Growth: Evidence from Top 38 Countries. Applied Energy, 162, s.733-741. https://doi.org/10.1016/j.apenergy.2015.10.104
• BloombergNEF, https://about.bnef.com/ (Erişim Tarihi: 10.07.2022)
• Bolgün, K. E. ve Akçay, M. B. (2009), Türk Finans Piyasalarında Entegre Risk Ölçüm ve Yönetim Uygulamaları Risk Yönetimi, Genişletilmiş 3. Baskı, İstanbul: Scala Yayıncılık.
• Bondia, R., Ghosh, S. ve Kanjilal, K. (2016). International Crude Oil Prices and the Stock Prices of Clean Energy and Technology Companies: Evidence from Non-linear Cointegration Tests with Unknown Structural Breaks. Energy, 101, s.558-565. http://dx.doi.org/10.1016/j.energy.2016.02.031
• Cleveland, C. J. ve Morris C. (2006). Dictionary of Energy, Italy: Elsevier.
• Çelik, İ., Sak, A. F., Özdemir Höl, A. ve Vergili, G. (2022). The Dynamic Connectedness and Hedging Opportunities of Implied and Realized Volatility: Evidence from Clean Energy ETFs. North American Journal of Economics and Finance, 60(101670), s.1-21. https://doi.org/10.1016/j.najef.2022.101670
• Çınar, M. ve Öz, R. (2017). Enerji Tüketimi ve Ekonomik Büyüme İlişkisine Yenilenebilir Enerji Bağlamında Bir Öneri. International Journal of Academic Value Studies (Javstudies), 3(13), s.40-54.
• Demirgil, B. ve Birol, Y. E. (2020). Yenilenebilir Enerji Tüketimi ve Ekonomik Büyüme İlişkisi: Türkiye İçin Bir Toda-Yamamoto Nedensellik Analizi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 21(1), s.68-83. https://doi.org/10.37880/cumuiibf.671591
• Diebold, F. X. ve Yilmaz, K. (2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28, s.57-66. https://doi.org/10.1016/j.ijforecast.2011.02.006
• Diebold, F. X. ve Yilmaz, K. (2014). On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms. Journal of Econometrics, 182(1), s.119-134. https://doi.org/10.1016/j.jeconom.2014.04.012
• Dutta, A. (2017). Oil Price Uncertainty and Clean Energy Stock Returns: New Evidence from Crude Oil Volatility Index. Journal of Cleaner Production, 164, s.1157-1166. http://dx.doi.org/10.1016/j.jclepro.2017.07.050
• Dutta, A. (2018). Oil and Energy Sector Stock Markets: an Analysis of Implied Volatility Indexes. Journal of Multinational Financial Management, 44, s.61-68. https://doi.org/10.1016/j.mulfin.2017.12.002
• Dutta, A., Bouri, E., Saeed, T. ve Vo, X. V. (2020). Impact of Energy Sector Volatility on Clean Energy Assets. Energy, 212(118657), s.1-11. https://doi.org/10.1016/j.energy.2020.118657
• Elliot, G., Rothenberg T. J. ve Stock, J. H. (1996), Efficient Tests for an Autoregressive Unit Root, Econometrica, 64, s.813-836. https://doi.org/10.2307/2171846
• Fahmy, H. (2022). The Rise in Investors’ Awareness of Climate Risks after the Paris Agreement and the Clean Energy-oil-technology Prices Nexus. Energy Economics, 106(105738), s.1-17. https://doi.org/10.1016/j.eneco.2021.105738
• Ferrer, R., Shahzad, S. J. H., Lopez, R. ve Jareno, F. (2018). Time and Frequency Dynamics of Connectedness between Renewable Energy Stocks and Crude Oil Prices. Energy Economics, 76, s.1-20. https://doi.org/10.1016/j.eneco.2018.09.022
• Fuentes, F. ve Herrera, R. (2020). Dynamics of Connectedness in Clean Energy Stocks. Energies, 13, 3705, s.1-18. https://doi.org/10.3390/en13143705
• Garman, M. B. ve Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53, s.67-78. http://dx.doi.org/10.1086/296072
• Gençyürek, A. G. ve Ekinci, R. (2021). Temiz Enerji Sektörü, Teknoloji Sektörü ve Ham Petrol Arasındaki Yayılım İlişkisi. Ekonomi, Politika&Finans Araştırmaları Dergisi, 6(1), s.60-81. https://doi.org/10.30784/epfad.798974
• Henriques, I. ve Sadorsky, P. (2008). Oil Prices and the Stock Prices of Alternative Energy Companies. Energy Economics, 30, s.998-1010.
• Hong, Y. (2001). A Test for Volatility Spillover with Application to Exchange Rates. Journal of Econometrics, 103, s.183-224. https://doi.org/10.1016/S0304-4076(01)00043-4
• Karagöl, E. T. ve Kavaz, İ. (2017). Dünyada ve Türkiye’de Yenilenebilir Enerji (Analiz). Siyaset, Ekonomi ve Toplum Araştırmaları Vakfı, 197, s.18-28.
• Kocaarslan, B. ve Soytaş, U. (2019a). Dynamic Correlations between Oil Prices and the Stock Prices of Clean Energy and Technology Firms: The Role of Reserve Currency (US dollar). Energy Economics, 84(104502), s.1-11. https://doi.org/10.1016/j.eneco.2019.104502
• Kocaarslan, B. ve Soytaş, U. (2019b). Asymmetric Pass-through between Oil Prices and the Stock Prices of Clean Energy Firms: new evidence from a nonlinear analysis. Energy Reports, 5, s.117-125. https://doi.org/10.1016/j.egyr.2019.01.002
• Koop, G., Pesaran, M. H. ve Potter, S. M. (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74, s.119-47. https://doi.org/10.1016/0304-4076(95)01753-4
• Kumar, S., Managi, S. ve Matsuda, A. (2012). Stock Prices of Clean Energy Firms, Oil and Carbon Markets: a Vector Autoregressive Analysis. Energy Economics, 34, s.215-226. https://doi.org/10.1016/j.eneco.2011.03.002
• Levine, R. (1997). Financial Development and Economic Growth: Views and Agenda. Journal of Economic Literature, 35, s.688-726.
• Lopez, R. (2018). The Behaviour of Energy-related Volatility Indices around Scheduled News Announcements: Implications for variance swap investments. Energy Economics, 72, s.356-364. https://doi.org/10.1016/j.eneco.2018.04.040
• Managi, S. ve Okimoto, T. (2013). Does the Price of Oil Interact with Clean Energy Prices in the Stock Market? Japan and the World Economy, 27, s.1-9. http://dx.doi.org/10.1016/j.japwor.2013.03.003
• Mucuk, M. ve Uysal, D. (2009). Türkiye Ekonomisinde Enerji Tüketimi ve Ekonomik Büyüme. Maliye Dergisi, 157, Temmuz-Aralık 2009, s.105-115.
• Pesaran, H. H. ve Shin, Y. (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics Letters, 58, s.17-29. https://doi.org/10.1016/S0165-1765(97)00214-0
• Reboredo, J. C. (2015). Is There Dependence and Systemic Risk between Oil and Renewable Energy Stock Prices? Energy Economics, 48, s.32-45. http://dx.doi.org/10.1016/j.eneco.2014.12.009
• Saeed, T., Bouri, E. ve Alsulami, H. (2021). Extreme Return Connectedness and its Determinants between Clean/green and Dirty Energy Investments. Energy Economics, 96(105017), s.1-14. https://doi.org/10.1016/j.eneco.2020.105017
• Telçeken, N., Kıyılar, M. ve Kadıoğlu, E. (2019). Volatilite Endeksleri: Gelişimi, Türleri, Uygulamaları ve TRVIX Önerisi, Ekonomi, Politika & Finans Araştırmaları Dergisi, 4(2), s.204-228.
• Uluslararası Enerji Ajansı Dünya Enerji Yatırımları 2022 Raporu, https://www.iea.org/ (Erişim Tarihi: 10.07.2022)
• Xia, T., Ji, Q., Zhang, D. ve Han, J. (2019). Asymmetric and Extreme Influence of Energy Price Changes on Renewable Energy Stock Performance. Journal of Cleaner Production, 241, s.1-10. https://doi.org/10.1016/j.jclepro.2019.118338
• Yahoo Finance, https://finance.yahoo.com/ (Erişim Tarihi: 12.02.2022)