SavedAdd your journal

Journals / Uluslararası Ekonomi İşletme ve Politika Dergisi / 2025 / Cilt: 9 Sayı: 2

Do Frequency Differences Overshadow Sustainability Impact? The GARCH-MIDAS Example

Frekans Farklılıkları Sürdürülebilirlik Etkisini Gölgeliyor mu? GARCH-MIDAS Örneği

Gözde Bozkurt(BEYKENT ÜNİVERSİTESİ)0000-0001-8413-1099
Pages
651–664
Publication Date
2025-10-06
🏛️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)

The results reveal that frequency differences can significantly affect the sustainability-finance relationship and that the correct frequency selection plays a critical role in the analysis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study analyzes the effect of carbon dioxide (〖CO〗_2) emissions per capita, which is an important indicator of environmental sustainability, on the volatility of Borsa Istanbul 100 (BIST100) index through financial data at different frequencies (daily, weekly and monthly).
📊
Key Findings & Data:Since environmental data are at annual frequency and financial returns are at high frequency, GARCH-MIDAS(1,1) model was preferred.
💡
Conclusion & Impact:The results reveal that frequency differences can significantly affect the sustainability-finance relationship and that the correct frequency selection plays a critical role in the analysis.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:GARCH-MIDAS · Çevresel Sürdürülebilirlik · Veri Frekansı Karşılaştırması

Abstract

This study analyzes the effect of carbon dioxide (〖CO〗_2) emissions per capita, which is an important indicator of environmental sustainability, on the volatility of Borsa Istanbul 100 (BIST100) index through financial data at different frequencies (daily, weekly and monthly). Since environmental data are at annual frequency and financial returns are at high frequency, GARCH-MIDAS(1,1) model was preferred. This model enables analysis by integrating the low-frequency structure of macro variables with the high-frequency nature of financial market variables. The findings of the study show that the effect of environmental variables on volatility varies depending on the data frequency. Especially in the weekly data frequency, both short-term and long-term parameters were found to be statistically significant and the performance of the model reached the highest level. The results reveal that frequency differences can significantly affect the sustainability-finance relationship and that the correct frequency selection plays a critical role in the analysis.

Özet

Bitcoin’in 2008'de oluşturulmasından beri güvenli liman, riskten korunma ve risk varlıklarının çeşitlendirilmesi gibi kripto para birimleri ve değerli metallerin paylaştığı çeşitli ortak özellikler geniş çapta tartışma konusu olmuştur. Bitcoin getirileri ile değerli metaller olan altın, bakır, gümüş ve platin getirileri arasındaki dinamik koşullu korelasyonları ve volatilite yayılımlarını analiz eden bu çalışmada DCC- GARCH modeli kullanılmıştır. Bitcoin getirileri ile değerli metaller olan altın, bakır, gümüş ve platin getirilerinin tüm modellerde volatilitelerinin kalıcı olduğu, incelenen tüm getiri serilerinde volatilite kümelenmesinin oluştuğu gözlemlenmiştir. Altın piyasasından Bitcoin piyasasına doğru tek yönlü volatilite aktarımına karşın, Bitcoin piyasasından bakır, gümüş ve platin piyasalarına doğru tek yönlü volatilite aktarımı bulunmuştur. Dinamik koşullu korelasyonlarda Bitcoin ile altın piyasalarında altın piyasası için, Bitcoin ve bakır piyasalarında Bitcoin ve bakır için anlamlı sonuçlar çıkmıştır. Bitcoin ve gümüş ile Bitcoin ve platin piyasaları arasında dinamik koşullu korelasyonlara rastlanmamıştır.

🔖

Cite This Article

Bozkurt, G. (2025). Do Frequency Differences Overshadow Sustainability Impact? The GARCH-MIDAS Example. Uluslararası Ekonomi İşletme ve Politika Dergisi, Cilt: 9 Sayı: 2, 651–664. https://doi.org/10.29216/ueip.1687052

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (22)

  1. Amendola, A., Candila, V., & Scognamillo, A. (2017). On the influence of US monetary policy on crude oil price volatility. Empirical Economics, 52(1), 155–178. https://doi.org/10.1007/s00181-016-1060-4
  2. Bansal, P., & DesJardine, M. R. (2014). Business sustainability: It is about time. Strategic Organization, 12(1), 70–78. https://doi.org/10.1177/1476127013520265
  3. Block, S., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A., et al. (2024). 2024 Environmental Performance Index. Yale Center for Environmental Law & Policy. https://epi.yale.edu
  4. Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644
  5. Clark, G. L., Feiner, A., & Viehs, M. (2015). From the stockholder to the stakeholder: How sustainability can drive financial outperformance [Research report]. University of Oxford, Arabesque Partners. https://arabesque.com/research/From_the_stockholder_to_the_stakeholder_web.pdf
  6. Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776–797. https://doi.org/10.1162/REST_a_00264
  7. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS touch: Mixed data sampling regression models. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.559961
  8. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90. https://doi.org/10.1080/07474930600972467
  9. Güneş, H., & Kaya, M. (2022). BİST endeksleri ile Brent petrol fiyatları arasındaki ilişkinin analizi. Uluslararası Finansal Ekonomi ve Bankacılık Uygulamaları Dergisi, 3(2), 71–95.
  10. Investing.com. (n.d.). BIST 100 Index. Investing.com. Retrieved February 15, 2025, from https://tr.investing.com/indices/ise-100
  11. Liu, J., Ma, F., Tang, Y., & Zhang, Y. (2019). Geopolitical risk and oil volatility: A new insight. Energy Economics, 84, 104548. https://doi.org/10.1016/j.eneco.2019.104548
  12. Maraqa, B., & Bein, M. (2020). Dynamic interrelationship and volatility spillover among sustainability stock markets, major European conventional indices, and international crude oil. Sustainability, 12(9), 3908. https://doi.org/10.3390/su12093908
  13. Oloko, T. F., Adediran, I. A., & Fadiya, O. T. (2022). Climate change and Asian stock markets: A GARCH-MIDAS approach. Asian Economics Letters, 3, 001c.37142. https://doi.org/10.46557/001c.37142
  14. Özçim, H. (2022). BİST Sürdürülebilirlik Endeksi ve makroekonomik veriler arasındaki ilişkinin GARCH modelleri çerçevesinde incelenmesi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (50), 115–126. https://doi.org/10.30794/pausbed.1074446
  15. Salisu, A. A., Gupta, R., & Demirer, R. (2022). Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model. Energy Economics, 108, 105934. https://doi.org/10.1016/j.eneco.2022.105934
  16. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
  17. Umar, M., Farid, S., & Naeem, M. A. (2022). Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial, oil and pandemic crisis. Energy, 240, 122702. https://doi.org/10.1016/j.energy.2021.122702
  18. Walther, T., Klein, T., & Bouri, E. (2019). Exogenous drivers of Bitcoin and cryptocurrency volatility – A mixed data sampling approach to forecasting. Journal of International Financial Markets, Institutions and Money, 63, 101133. https://doi.org/10.1016/j.intfin.2019.101133
  19. Wang, J., & Li, L. (2023). Climate risk and Chinese stock volatility forecasting: Evidence from ESG index. Finance Research Letters, 55(Part A), 103898. https://doi.org/10.1016/j.frl.2023.103898
  20. World Bank. (n.d.). World Development Indicators. World Bank DataBank. Retrieved February 15, 2025, from https://databank.worldbank.org/source/world-development-indicators
  21. Xu, Q., Bo, Z., Jiang, J., & Liu, Y. (2019). Does Google Search Index really help predicting stock market volatility? Evidence from the modified mixed sampling model on volatility. Knowledge-Based Systems, 166, 170–185. https://doi.org/10.1016/j.knosys.2018.12.009
  22. You, Y., & Liu, X. (2020). Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach. Journal of Banking & Finance, 116, 105849. https://doi.org/10.1016/j.jbankfin.2020.105849