Temiz Enerji Sektörü, Teknoloji Sektörü ve Ham Petrol Arasındaki Yayılım İlişkisi

Yıl 2021, Cilt: 6 Sayı: 1, 60 - 81, 30.04.2021

Ahmet Galip Gençyürek , Ramazan Ekinci

https://doi.org/10.30784/epfad.798974

Cited By: 2

Öz

Küresel ısınmanın sonucu olarak ortaya çıkan iklim değişikliği yenilenebilir enerjiye diğer bir ifadeyle temiz enerjiye olan ilgiyi artırmıştır. Gelişen teknolojiyle birlikte verimliliğin artması ve maliyetlerin azalması sonucunda da yenilenebilir enerji tüketimi hızlanmıştır. Petrol piyasasının yenilenebilir enerjinin ikamesi olması, teknolojinin de yenilenebilir enerjinin önemli bir girdisi olması nedeniyle teoride yenilenebilir enerjinin her iki değişkenden etkilendiği düşünülmektedir. Yapılan çalışma ile teoride ileri sürülen bu görüş hem ortalamada hem de varyansta nedensellik ilişkisinin tespitine olanak sağlayan Hong (2001) yöntemiyle incelenmek istenmektedir. Temiz enerji sektörü, teknoloji sektörü ve petrol piyasası sırasıyla Willderhill Endeksi (ECO), ArcaTech Endeksi ve WTI tarafından temsil edilmektedir. 2004-2019 döneminin analiz edildiği çalışma sonucunda ortalamada temiz enerji sektöründen petrol piyasasına doğru, varyansta ise; petrol piyasasından temiz enerji sektörüne doğru Granger nedenselliğin olduğu tespit edilmiştir. Kappa-1 yöntemiyle belirlenen varyans kırılma tarihlerinin dikkate alınması sonrasında nedensellik ilişkilerinin varlığında herhangi bir değişim gözlemlenmemiştir. Elde edilen sonuçların araştırmacılara, politika yapıcılara ve yatırımcılara önemli bilgiler sunacağı düşünülmektedir.

Anahtar Kelimeler

Yenilenebilir Enerji, Petrol Piyasası, Teknoloji Sektörü

Spillover Between Clean Energy Sector , Crude Oil and Technology Sector

Öz

Climate change resulting from global warming has increased the interest in renewable energy (clean energy). The consumption of renewable energy has accelerated as a result of increasing efficiency and decreasing costs due to the developing technology. In theory, renewable energy is thought to be affected by the oil market and technology sector, since the oil market is a substitute for renewable energy and technology sector is an important input of renewable energy. This view which is claimed in theory is aimed to be analyzed by the Hong (2001) method that allows the determination of causality both mean and variance. The clean energy sector, the technology sector, and the oil market are represented by the Willderhill Index (ECO), ArcaTech Index, and WTI respectively. As a result of the study that is span from 2004-2019, it has been determined that there is causality in mean from the oil market to the clean energy sector; and there is causality in variance from the clean energy sector to the oil market. After considering the variance breaking dates determined by the Kappa-1 method, no change was observed in the presence of causality. It is believed that the result obtained from the study, provide information to researches, policymakers and investors.

Anahtar Kelimeler

Renewable Energy, Crude Oil, Technology Sector

Kaynakça

• Ahmad, W. (2017). An analysis of directional spillover between crude oilprices and stock prices of clean energy and technology companies. Research in International Business and Finance, 47, 376-389. https://doi.org/10.1016/j.ribaf.2017.07.140
• Baruník, J. and Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. https://doi.org/10.1093/jjfinec/nby001
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
• Bondia, R., Ghosh, S. and 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, 101,558-565. https://doi.org/10.1016/j.energy.2016.02.031
• BP. (2018). BP Statistical Review of World Energy 67th Edition. Retrieved from https://www.bp.com/content/dam/bp/en/corporate/pdf/energy-economics/statistical-review/bp-stats-review-2018-oil.pdf
• Cheung, Y-W. and Ng, L. K. (1996). A causality-in-variance test and its application to financial market. Prices. Journal of Econometrics, 72(1-2), 33-48. https://doi.org/10.1016/0304-4076(94)01714-X
• Dawar, I., Dutta, A., Bouri, E. and Saeed, T. (2020). Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression. Renewable Energy, 163, 288-299. https://doi.org/10.1016/j.renene.2020.08.162
• De Pooter, M. and Van Dijk, D. (2004). Testing for changes in volatility in heteroskedastic time series-a further examination (No. EI 2004-38). Retrieved from https://repub.eur.nl/pub/1627/
• Ding, Z., Granger, C. W. J. and Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Emprical Finanace, 1, 83-106. https://doi.org/10.1016/0927-5398(93)90006-D
• Dutta, A. (2017). Oil price uncertainty and clean energy stock returns: New evidence from crude oil volatility index. Journal of Cleaner Production, 164, 1157-1166. https://doi.org/10.1016/j.jclepro.2017.07.050
• EIA. (2019). International Energy Outlook with projections to 2050. Retrieved from https://www.eia.gov/outlooks/ieo/
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007. https://doi.org/10.2307/1912773
• Ferrer, R., Shahzad, S. J. H., Lopez, R. and Jareno, F. (2018). Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76, 1-20. https://doi.org/10.1016/j.eneco.2018.09.022
• Henriques, I. and Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics, 30, 998-1010. https://doi.org/10.1016/j.eneco.2007.11.001
• Hong, Y. (2001). A test for volatility spillover with application to exchange rates. Journal of Econometrics, 103, 183-224. https://doi.org/10.1016/S0304-4076(01)00043-4
• Kocaarslan, B. and Soytaş, U. (2019). 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, 1-11. https://doi.org/10.1016/j.eneco.2019.104502
• Korkmaz, T. ve Çevik, E. İ. (2009). Zımni volatilite endeksinden gelişmekte olan piyasalara yönelik volatilite yayılma etkisi. BDDK Bankacılık ve Finansal Piyasalar, 3(2), 87-105. Erişim adresi: https://dergipark.org.tr/tr/pub/bddkdergisi
• Kumar, S., Managi, S. and Matsuda, A. (2012). Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis. Energy Economics, 34, 215-226. https://doi.org/10.1016/j.eneco.2011.03.002
• Maghyereh, A. I., Awartani, B. and Abdoh, H. (2019). The co-movement between oil and clean energy stocks: A wavelet based analysis of horizon associations. Energy, 169, 895-913. https://doi.org/10.1016/j.energy.2018.12.039
• Managi, S. and Okimoto, T. (2013). Does the price of oil interact with clean energy prices in the stock market?. Japan and the World Economy, 27, 1-9. https://doi.org/10.1016/j.japwor.2013.03.003
• Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business, 36(4), 394-419. doi: 10.1086/294632
• Nasreen, S., Tiwari, A. K., Eizaguirre, J. C. and Wohar, M. E. (2020). Dynamic connectedness between oil prices and stock returns of clean energy and technology companies. Journal of Cleaner Production, 260, 1-21. https://doi.org/10.1016/j.jclepro.2020.121015
• Nelson, D. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260
• Painuly, P. I. and Wohlgemuth, N. (2020). Economics of renewable energy. U. Soytaş and R. Sarı (Eds.), Handbook of Energy Economics (pp .68-84). NY: Routledge.
• Pham, L. (2019). Do all clean energy stocks respond homogeneously to oil price?. Energy Economics, 81, 355-379. https://doi.org/10.1016/j.eneco.2019.04.010
• Reboredo, J. C. and Ugolini, A. (2018). The impact of energy prices on clean energy stock prices. A multivariate quantile dependence approach. Energy Economics, 76, 136-152. https://doi.org/10.1016/j.eneco.2018.10.012
• Reboredo, J. C., Rivera-Castro, M. A. and Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241-252. https://doi.org/10.1016/j.eneco.2016.10.015
• Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34, 248-255. https://doi.org/10.1016/j.eneco.2011.03.006
• Sanso, A., Arago, V. and Silvestre, J. (2004). Testing for changes in the unconditional variance of financial time series. Revista de Economia Financiera, 4(1), 32-53. Retrieved from https://dspace.uib.es/
• Song, Y., Ji, Q., Du, Y-J. and Geng, J-B. (2019). The dynamic dependence of fossil energy, investor sentiment andrenewable energy stock markets. Energy Economics, 84, 1-15. https://doi.org/10.1016/j.eneco.2019.104564
• Terasvirta, T. (2009). An introduction to univariate Garch models. In T. G. Andersen, R A. Davis, J-P. Kreib and T. Mikosch (Eds.), Handbook of Financial Time Series (pp. 17-42). Berlin: Springer.
• Yıldırım, D. Ç., Çevik, E. İ. and Esen, Ö. (2020). Time-varying volatility spillovers between oil prices and precious metal prices. Resources Policy, 68, 1-14. https://doi.org/10.1016/j.resourpol.2020.101783