Journals / Ekonomi Politika ve Finans Araştırmaları Dergisi / 2021 / Cilt: 6 - Sayı: 1

Spillover Between Clean Energy Sector , Crude Oil and Technology Sector

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

Pages
60–81
Publication Date
2021-04-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is believed that the result obtained from the study, provide information to researches, policymakers and investors.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Climate change resulting from global warming has increased the interest in renewable energy (clean energy).
📊
Key Findings & Data:The consumption of renewable energy has accelerated as a result of increasing efficiency and decreasing costs due to the developing technology.
💡
Conclusion & Impact:It is believed that the result obtained from the study, provide information to researches, policymakers and investors.
🔬Design:Research Article
📍Scope:2001 Verileri
⚙️Variables:Yenilenebilir Enerji · Petrol Piyasası · Teknoloji Sektörü

Abstract

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.

Özet

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.

🔖

Cite This Article

GENÇYÜREK, A. G., EKİNCİ, R. (2021). Spillover Between Clean Energy Sector , Crude Oil and Technology Sector. Ekonomi Politika ve Finans Araştırmaları Dergisi, Cilt: 6 - Sayı: 1, 60–81. https://ajindex.com/articles/141680

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. 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
  2. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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/
  8. 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
  9. 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
  10. EIA. (2019). International Energy Outlook with projections to 2050. Retrieved from https://www.eia.gov/outlooks/ieo/
  11. 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
  12. Ferrer, R., Shahzad, S. J. H.,