Avrupa Yenilenebilir Enerji Stoklarının Volatilite Karakteri: ERIX Endeksi Üzerine Bir Araştırma

Year 2024, , 75 - 92, 28.01.2024

Şahnaz Koçoğlu

https://doi.org/10.25295/fsecon.1362815

Cited By: 1

Abstract

Yenilenebilir enerji iklim değişikliği ile mücadele ve enerji güvenliğini sağlamak için stratejik öneme sahip bir sektördür. Avrupa 2020’li yıllara gelindiğinde toplam enerji tüketiminin %20’sinden fazlasını yenilenebilir enerjiden sağlamaktadır ve Avrupa Birliğinin nihai amacı tam olarak fosil yakıtlardan arınmaktır. Bu amaç doğrultusunda yenilenebilir enerji firmaları kilit rol oynamaktadır ve yenilenebilir enerji şirketlerinin finansal performansı mutlaka iyi anlaşılmalıdır. Bu çalışmanın amacı da Avrupa yenilenebilir enerji şirketlerinin hisse senedi performansının volatilite karakterini ortaya koymaktır. Avrupa yenilenebilir enerji şirketlerini analiz etmek amacıyla en büyük yenilenebilir enerji firmalarından oluşan ERIX (European Renewable Energy Index) kullanılmıştır. Endeksin volatilite karakterini incelemek için GARCH (1,1), TGARCH ve EGARCH modelleri kullanılmıştır. Çalışma sonucunda Avrupa yenilenebilir enerji şirketlerinin finansal performansını tahmin etmede geçmiş verilerin kullanılabileceği bulunmuştur. GARCH(1,1) modeli sonucunda bugün meydana gelen şokların gelecek dönem varyans tahminlerinde uzun süre etkili olduğu sonucuna varılmıştır. TAGRCH modeli ERIX endeksi üstünde kötü haberlerin oynaklık etkisinin daha fazla, iyi haberlerin ise daha az olduğunu göstermiştir. EGARCH modeli de iyi haberle ile kötü haberlerin yol açtığı şokların asimetrik olduğu sonucunu ortaya koymuştur. Bu çalışma yenilenebilir enerji endeksine finansal bir veri olarak yaklaşan ve volatilite analizini yapan ilk çalışmadır.

Keywords

Yenilenebilir Enerji, Hisse Senedi Piyasası, ERIX, GARCH Modelleri

The Volatility Character of European Renewable Energy Stocks: A Study on the ERIX Index

Abstract

Renewable energy is a key sector in combating climate change and ensuring energy security. By the 2020s, more than 20% of the total energy consumption is from renewable sources and the European Union plans to phase out fossil-based energy production completely. For this purpose, renewable energy companies play a key role and the financial performance of renewable energies must be well understood. The aim of this study is to reveal the volatile character of the functioning of renewable energy companies in the European Union. ERIX (European Renewable Energy Index), which consists of the largest renewable energy companies, was used to analyze renewable energy in Europe. GARCH (1,1), TGARCH, and EGARCH models were used to examine the volatile character of the index. As a result of the study, it has been found that historical data can be used to predict the financial performance of European renewable energy companies. As a result of the GARCH(1,1) model, It was concluded that shocks occurring today have a long-term impact on future variance estimates. TGARCH model reveals that bad news has a greater volatility effect on the ERIX index and good news has less impact. The EGARCH model also shows that the shocks caused by good news and bad news are asymmetric. This study was the first to approach the renewable energy index as financial data and perform volatility analysis.

Keywords

Renewable Energy, Stock Market, ERIX, GARCH Models

References

• Bollerslev, T. (2008). Glossary to ARCH (GARCH). CREATES Research Paper, 49.
• Bondia, R., Ghosh, S. & 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.
• 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(366a), 427-431.
• Dutta, A., Bouri, E. & Noor, M. H. (2018). Return and Volatility Linkages Between CO2 Emission and Clean Energy Stock Prices. Energy, 164, 803-810.
• Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of The Variance of United Kingdom Inflation. Econometrica: Journal of the Econometric Society, 987-1007.
• Ferrer, R., Shahzad, S. J. H., López, R. & Jareño, F. (2018). Time and Frequency Dynamics of Connectedness Between Renewable Energy Stocks and Crude Oil Prices. Energy Economics, 76, 1-20.
• Glosten, L., Jaganathan, R. & Runkle, D. (1993). Relations Between the Expected Nominal Stock Excess Return, the Volatility of the Nominal Excess Return and the Interest Rate. Journal of Finance, 48(5), 1779-1801.
• Greenmatch. (2023). What Are the Advantages and Disadvantages of Renewable Energy?. https://www.greenmatch.co.uk/blog/2021/09/advantages-and-disadvantages-of-renewable-energy
• Henriques, I. & Sadorsky, P. (2008). Oil Prices and the Stock Prices of Alternative Energy Companies. Energy Economics, 30(3), 998-1010.
• Jiang, Y., Wang, J., Lie, J. & Mo, B. (2021). Dynamic Dependence Nexus and Causality of the Renewable Energy Stock Markets on the Fossil Energy Markets. Energy, 233, 121191.
• Kazemilari, M., Mardani, A., Streimikiene, D. & Zavadskas, E. K. (2017). An Overview of Renewable Energy Companies in Stock Exchange: Evidence from Minimal Spanning Tree Approach. Renewable Energy, 102, 107-117.
• Kumar, S., Managi, S. & Matsuda, A. (2012). Stock Prices of Clean Energy Firms, Oil and Carbon Markets: A Vector Autoregressive Analysis. Energy Economics, 34(1), 215-226.
• Liu, T. & Hamori, S. (2020). Spillovers to Renewable Energy Stocks in the US and Europe: Are They Different?. Energies, 13(12), 3162.
• Liu, T., Nakajima, T. & Hamori, S. (2021). The Impact of Economic Uncertainty Caused by COVID-19 on Renewable Energy Stocks. Empirical Economics, 1-21.
• MacKinnon, J. G. (1996). Numerical Distribution Functions for Unit Root and Cointegration Tests. Journal of Applied Econometrics, 11(6), 601-618.
• Maghyereh, A. I., Awartani, B. & Abdoh, H. (2019). The Co-Movement Between Oil and Clean Energy Stocks: A Wavelet-Based Analysis of Horizon Associations. Energy, 169, 895-913.
• Mohammed, K. S., Usman, M., Ahmad, P. & Bulgamaa, U. (2023). Do All Renewable Energy Stocks React to the War in Ukraine? Russo-Ukrainian Conflict Perspective. Environmental Science and Pollution Research, 30(13), 36782-36793.
• Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica: Journal of the Econometric Society, 347-370.
• Paramati, S. R., Mo, D. & Gupta, R. (2017). The Effects of Stock Market Growth and Renewable Energy Use on CO2 Emissions: Evidence from G20 Countries. Energy Economics, 66, 360-371.
• Phillips, P. C. & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335-346,
• Qiu, L., Chu, L., Zhou, R., Xu, H. & Yuan, S. (2023). How Do Carbon, Stock, and Renewable Energy Markets Interact: Evidence from Europe. Journal of Cleaner Production, 407, 137106.
• Razmi, S. F., Bajgiran, B. R., Behname, M., Salari, T. E. & Razmi, S. M. J. (2020). The Relationship of Renewable Energy Consumption to Stock Market Development and Economic Growth in Iran. Renewable Energy, 145, 2019-2024.
• Reboredo, J. C. (2015). Is There Dependence and Systemic Risk Between Oil and Renewable Energy Stock Prices?. Energy Economics, 48, 32-45.
• Song, Y., Ji, Q., Du, Y. J., & Geng, J. B. (2019). The Dynamic Dependence of Fossil Energy, Investor Sentiment and Renewable Energy Stock Markets. Energy Economics, 84, 104564.
• Tiwari, A. K., Abakah, E. J. A., Gabauer, D. & Dwumfour, R. A. (2022). Dynamic Spillover Effects Among Green Bond, Renewable Energy Stocks and Carbon Markets During COVID-19 Pandemic: Implications for Hedging and Investments Strategies. Global Finance Journal, 51, 100692.
• United Nations Climate Action. (2023). What is Renewable Energy?. https://www.un.org/en/climatechange/what-is-renewable-energy#:~:text=Renewable%20energy%20is%20energy%20derived,plentiful%20and%20all%20around%20us
• Wei, Y., Zhang, J., Chen, Y. & Wang, Y. (2022). The Impacts of El Niño-Southern Oscillation on Renewable Energy Stock Markets: Evidence from Quantile Perspective. Energy, 260, 124949.
• Xi, Y., Zeng, Q., Lu, X. & Huynh, T. L. (2022). Oil and Renewable Energy Stock Markets: Unique Role of Extreme Shocks. Energy Economics, 109, 105995.
• Xia, T., Ji, Q., Zhang, D. & Han, J. (2019). Asymmetric and Extreme Influence of Energy Price Changes on Renewable Energy Stock Performance. Journal of Cleaner Production, 241, 118338.
• 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.
• Zakoian, J. M. (1994). Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18(5), 931-955.
• Zeqiraj, V., Sohag, K. & Soytas, U. (2020). Stock Market Development and Low-Carbon Economy: The Role of Innovation and Renewable Energy. Energy Economics, 91, 104908.