Examining sustainable stock indices with the DCC-GARCH model and the impact of oil prices on this relationship

Year 2024, Volume: 26 Issue: 1, 48 - 58, 01.06.2024

Ozge Dinc Cavlak

https://doi.org/10.33707/akuiibfd.1335551

Abstract

This study aims to examine the time-varying conditional correlations and volatility between sustainable stock indices across countries and reveal the dynamics of the relationship between energy markets and sustainable stock performances. Firstly, the interaction between sustainable stock indices for the US and Turkish markets is examined using the DCC-GARCH model that considers the time-varying correlation and volatility. Then, the effects of oil prices on the obtained dynamic conditional correlations are revealed using Granger causality analysis. The results of the research reveal that the dynamic conditional correlation model between the US and Turkish sustainable stock indices is stable. The model parameters show that the effect of the lagged shock on the current dynamic conditional correlations and the effect of the lagged dynamic conditional correlations on the current dynamic conditional correlations are significant, thus demonstrating the existence of the dynamic conditional correlation between the US and Turkish sustainability indices. In addition, oil prices have a significant causal effect on the dynamic conditional correlations between the US and Turkish sustainable stock indices. Finally, the impulse response analysis reveals that in response to a shock in oil prices, the dynamic correlation between the markets responds inversely to the shock, suggesting that the US and Turkish stock markets diverge in terms of sustainability indices.

Keywords

Dynamic Conditional Correlation (DCC) Model, GARCH Model, Granger Causality, Sustainable Stock

Sürdürülebilir hisse senedi endekslerinin DCC-GARCH modeli ile incelenmesi ve petrol fiyatlarının bu ilişkiye etkisi

Abstract

Bu çalışma, ülkeler arası sürdürülebilir hisse senedi endeksleri arasındaki zamana bağlı değişen koşullu korelasyonları ve volatiliteyi incelemeyi amaçlamakta; aynı zamanda enerji piyasaları ile sürdürülebilir hisse senedi performansları arasındaki ilişki dinamiklerini ortaya koymayı hedeflemektedir. İlk olarak, ABD ve Türkiye piyasaları için sürdürülebilir hisse senedi endeksleri arasındaki etkileşim, zamana bağlı değişen korelasyonu ve volatiliteyi dikkate alan DCC-GARCH modeli ile incelenmektedir. Ardından, petrol fiyatlarının, elde edilen dinamik koşullu korelasyonlar üzerindeki etkileri Granger nedensellik analizi kullanılarak ortaya konmaktadır. Araştırmanın sonuçları, ABD ve Türkiye sürdürülebilir hisse senedi endeksleri arasındaki dinamik koşullu korelasyon modelinin stabil olduğunu göstermektedir. Model parametreleri, gecikmeli şokun mevcut dinamik koşullu korelasyonlar üzerindeki etkisinin ve gecikmeli dinamik koşullu korelasyonların mevcut dinamik koşullu korelasyonlar üzerindeki etkisinin anlamlı olduğunu göstermekte; böylelikle ABD ve Türkiye sürdürülebilirlik endeksleri arasında dinamik koşullu korelasyonun varlığı ortaya konmaktadır. Bunun yanı sıra, petrol fiyatlarının, ABD ve Türkiye sürdürülebilir hisse senedi endeksleri arasındaki dinamik koşullu korelasyonlar üzerinde anlamlı bir nedensel etkisi olduğu bulunmaktadır. Son olarak, Etki-Tepki analizi sonucu, petrol fiyatlarında meydana gelen bir şoka karşılık, piyasalar arasındaki dinamik korelasyonun şok ile ters yönde bir tepki verdiği görülmekte; bu da ABD ve Türkiye hisse senedi piyasalarının sürdürülebilirlik endeksleri bakımından ayrıştığına işaret etmektedir.

Keywords

Dinamik Koşullu Korelasyon (DCC) Modeli; GARCH Modeli, Granger Nedensellik, Sürdürülebilir Hisse Senedi

References

• Belasri, Y., & Ellaia, R. (2017). Estimation of volatility and correlation with multivariate generalized autoregressive conditional heteroskedasticity models: an application to Moroccan stock markets. International Journal of Economics and Financial Issues, 7(2), 384-396.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
• Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. The review of economics and statistics, 498-505.
• BORSA İstanbul (2014). Sürdürülebilirlik Endeksleri. https://www.borsaistanbul.com/tr/sayfa/165/bist-surdurulebilirlik-endeksleri. Erişim tarihi: 30.05.2023.
• Chen, Y., Li, W., & Jin, X. (2018). Volatility spillovers between crude oil prices and new energy stock price in China. Romanian Journal of Economic Forecasting, 21(2), 43-62.
• Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.
• Engle R. F. (2002). Dynamic Conditional Correlation – A Simple Class of Multivariate GARCH Models. Journal of Business and Economic Statistics, 20, 339-350.
• Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157-168.
• 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.
• Gok, I. Y., Duranay, S., & Unlu, H. U. (2019). Co-movement dynamics of sustainability indices: investigating the diversification opportunities through FTSE4Good index family and Borsa Istanbul sustainability index. Social Responsibility Journal, 16(8), 1475-1487.
• Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438.
• Guesmi, K., & Fattoum, S. (2014). Return and volatility transmission between oil prices and oil-exporting and oil-importing countries. Economic Modelling, 38, 305-310.
• Henriques, I., & Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics, 30(3), 998-1010.
• International Monetary Fund (2003). Effects of Financial Globalization on Developing Countries Some Empirical Evidence. Eswar S. Prasad, Kenneth Rogoff, Shang-Jin Wei, and M. Ayan Kose. Erişim Tarihi: 30.05.2023. https://www.imf.org/external/pubs/nft/op/220/index.htm.
• Kocaarslan, B., & Soytas, U. (2019). Asymmetric pass-through between oil prices and the stock prices of clean energy firms: New evidence from a nonlinear analysis. Energy Reports, 5, 117-125.
• Kocaarslan, B., Sari, R., Gormus, A., & Soytas, U. (2017). Dynamic correlations between BRIC and US stock markets: The asymmetric impact of volatility expectations in oil, gold and financial markets. Journal of Commodity Markets, 7, 41-56.
• 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.
• Naifar, N. (2018). Exploring the dynamic links between GCC sukuk and commodity market volatility. International Journal of Financial Studies, 6(3), 72.
• Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
• Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy stock prices?. Energy Economics, 48, 32-45.
• Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241-252. S&P Dow Jones Indices, (2023). https://www.spglobal.com/spdji/en/indices/esg/sp-500-esg- index/#overview. Erişim tarihi: 30.05.2023.
• Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34(1), 248-255.
• Sadorsky, P. (2014). Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Energy Economics, 43, 72-81.
• Silvennoinen, A., & Teräsvirta, T. (2009). Multivariate GARCH models. In Handbook of financial time series (pp. 201-229). Berlin, Heidelberg: Springer Berlin Heidelberg.
• Smyth, R., & Narayan, P. K. (2018). What do we know about oil prices and stock returns?. International Review of Financial Analysis, 57, 148-156.
• Tang, C., Aruga, K., & Hu, Y. (2023). The Dynamic Correlation and Volatility Spillover among Green Bonds, Clean Energy Stock, and Fossil Fuel Market. Sustainability, 15(8), 6586.
• Uğurlu Yıldırım, E. (2021). Covid-19 Pandemi ve 2008 Ekonomik Kriz Dönemlerinde Riskten Kaçınma Düzeyinin ABD ve BRIC Piyasa Entegrasyonu Üzerindeki Değişen Etkisi. Muhasebe ve Finansman Dergisi, (90), 185-208.
• United Nations (2015). Transforming our World: The 2030 Agenda for Sustainable Development. Erişim Tarihi: 28.05.2023. https://sustainabledevelopment.un.org/post2015/transformingourworld.
• World Commission on Environment and Development. (1987). Our Common Future (The Brundtland Report). Erişim Tarihi: 28.05.2023. http://www.un-documents.net/our-common-future.pdf.
• 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.
• Yadav, M. P., Sharma, S., & Bhardwaj, I. (2023). Volatility spillover between Chinese stock market and selected emerging economies: A dynamic conditional correlation and portfolio optimization perspective. Asia-Pacific Financial Markets, 30(2), 427-444.