SavedAdd your journal

Journals / Ahi Evran Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Cilt: 10 Sayı: 2

Sectoral Volatility in Borsa Istanbul: A GARCH-based Comparative Analysis

Borsa İstanbul'da Sektörel Volatilite: GARCH Tabanlı Karşılaştırmalı Bir Analiz

Emre Bulut(FIRAT ÜNİVERSİTESİ)0000-0002-2884-1405
Pages
507–522
Publication Date
2024-07-31
🏛️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 findings revealed distinct volatility behaviors among sectors, with the BIST Financial Leasing Index (FINK) displaying heightened vulnerability to external shocks, while the BIST Banking Index (BNK) and BIST Financial Index (MALI) exhibited comparatively milder volatility responses.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study delved into the complex landscape of sectoral volatility dynamics within Borsa Istanbul, a dynamic emerging market, using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model.
📊
Key Findings & Data:By analyzing a dataset spanning from March 1, 2013, to August 16, 2023, the research examined how different sectors respond to market shocks and how these responses vary across sectors.
💡
Conclusion & Impact:The findings revealed distinct volatility behaviors among sectors, with the BIST Financial Leasing Index (FINK) displaying heightened vulnerability to external shocks, while the BIST Banking Index (BNK) and BIST Financial Index (MALI) exhibited comparatively milder volatility responses.
🔬Design:Modeling & Simulation
📍Scope:2013 Verileri
⚙️Variables:risk · BIST · volatilite

Abstract

This study delved into the complex landscape of sectoral volatility dynamics within Borsa Istanbul, a dynamic emerging market, using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. By analyzing a dataset spanning from March 1, 2013, to August 16, 2023, the research examined how different sectors respond to market shocks and how these responses vary across sectors. The findings revealed distinct volatility behaviors among sectors, with the BIST Financial Leasing Index (FINK) displaying heightened vulnerability to external shocks, while the BIST Banking Index (BNK) and BIST Financial Index (MALI) exhibited comparatively milder volatility responses. Policymakers, regulators, and investors can utilize these insights to tailor risk management strategies, enhance market stability, and construct portfolios that align with risk preferences. This research enriches the understanding of sectoral dynamics in emerging markets, offering a foundation for future investigations into the intricate interplay between sectors, shocks, and volatility patterns.

Özet

Bu çalışma, dinamik bir gelişmekte olan piyasa olan Borsa İstanbul'daki sektörel volatilite dinamiklerinin karmaşık yapısını Genelleştirilmiş Otoregresif Koşullu Değişen Varyans (GARCH) modelini kullanarak incelemiştir. Araştırma, 1 Mart 2013'ten 16 Ağustos 2023'e kadar uzanan bir veri setini analiz ederek, farklı sektörlerin piyasadaki şoklara nasıl tepki verdiğini ve bu tepkilerin sektörler arasında nasıl değiştiğini incelemiştir. Bulgular, sektörler arasında farklı volatilite davranışları olduğunu göstermiştir. BIST Finansal Kiralama Endeksi (FINK) şoklara karşı daha yüksek kırılganlık gösterirken, BIST Bankacılık Endeksi (BNK) ve BIST Mali Endeksi (MALI) nispeten daha ılımlı volatilite tepkileri sergilemiştir. Politika yapıcılar, düzenleyiciler ve yatırımcılar, risk yönetimi stratejilerini uyarlamak, piyasa istikrarını artırmak ve risk tercihlerine uygun portföyler oluşturmak için bu içgörüleri kullanabilirler. Bu araştırma, gelişmekte olan piyasalardaki sektörel dinamiklerin anlaşılmasına yardımcı olmayı hedeflemekte ve böylelikle sektörler, şoklar ve oynaklık modelleri arasındaki karmaşık etkileşime ilişkin gelecekteki araştırmalar için bir temel sunmaktadır.

🔖

Cite This Article

Bulut, E. (2024). Sectoral Volatility in Borsa Istanbul: A GARCH-based Comparative Analysis. Ahi Evran Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 10 Sayı: 2, 507–522. https://doi.org/10.31592/aeusbed.1355079

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (34)

  1. Alagidede, P., and Panagiotidis, T. (2009). Modelling stock returns in Africa's emerging equity markets. International Review of Financial Analysis, 18(1), 1-11.
  2. Al-Najjar, D. (2016). Modelling and estimation of volatility using arch/garch models in Jordan’s stock market. Asian Journal of Finance & Accounting, 8(1), 152. https://doi.org/10.5296/ajfa.v8i1.9129
  3. Bhowmik, R. and Wang, S. (2020). Stock market volatility and return analysis: a systematic literature review. Entropy, 22(5), 522. https://doi.org/10.3390/e22050522
  4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  5. Bordignon, S., Caporin, M., and Lisi, F. (2007). Generalised long-memory GARCH models for intra-daily volatility. Comput. Stat. Data Anal., 51, 5900-5912.
  6. Brooks, C. (2008). Introductory econometrics for finance (2nd Edition). Cambridge: Cambridge University Press.
  7. Bulut, E. ve Karabulut, R. (2023). Hisse senedi getiri volatilerinin doğrusal olmayan metotlarla incelenmesi ve piyasa etkinliğinin araştırılması: BRICS-T ülkeleri ile karşılaştırmalı bir analiz. Çanakkale: Paradigma Akademi.
  8. Chaudhary, R., Bakhshi, P., & Gupta, H. (2020). Volatility in international stock markets: an empirical study during covid-19. Journal of Risk and Financial Management, 13(9), 208. https://doi.org/10.3390/jrfm13090208
  9. Cheteni, P. (2017). Stock market volatility using garch models: evidence from south africa and china stock markets. Journal of Economics and Behavioral Studies, 8(6(J)), 237-245. https://doi.org/10.22610/jebs.v8i6(j).1497
  10. Claudiu, A. L., Maria-Cristina, Z., Suhan, M., Attila, G., Simona, E., and Jatin, T. (2022). Financial market interconnections analyzed using garch univariate and multivariate models. Economic Computation and Economic Cybernetics Studies and Research, 56(3/2022), 101-118. https://doi.org/10.24818/18423264/56.3.22.07
  11. Demir, İ., and Çene, E. (2012). İMKB-100 Endeksindeki kaldıraç etkisinin ARCH modelleriyle iki alt dönemde incelenmesi. Istanbul University Journal of the School of Business Administration, 41(2), 214-226.
  12. Ekinci, C., Akyildirim, E., and Corbet, S. (2019). Analysing the dynamic influence of US macroeconomic news releases on Turkish stock markets. Finance Research Letters, 31, 155-164.
  13. Endri, E., Aipama, W., Razak, A. A., Sari, L., and Septiano, R. (2021). Stock price volatility during the covid-19 pandemic: the garch model. Investment Management and Financial Innovations, 18(4), 12-20. https://doi.org/10.21511/imfi.18(4).2021.02
  14. Engle, F. R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
  15. Er, Ş., and Fidan, N. (2013). Modeling Istanbul Stock Exchange-100 daily stock returns: a nonparametric GARCH approach. Journal of Business Economics and Finance, 2(1), 36-50.
  16. Filis, G., Degiannakis, S., and Floros, C. (2011). Dynamic correlation between stock market and oil prices: the case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20(3), 152-164. https://doi.org/10.1016/j.irfa.2011.02.014
  17. Gökçe, A. (2001). İstanbul Menkul Kıymetler Borsası getirilerindeki volatilitenin ARCH teknikleri ile ölçülmesi. Gazi İktisadi ve İdari Bilimler Fakültesi Dergisi, 3(1), 1-23.
  18. Hansen, P., and Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of Applied Econometrics, 20(07), 839-889.
  19. İltaş, Y., and Demirgüneş, K. (2020). Asset tangibility and financial performance: a time series evidence. Ahi Evran Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(2), 345-364.
  20. Jorion, P. (2005). Financial risk manager-handbook. Wiley Finance (3nd Edition). Canada: GARP (Global Association of Risk Professionals).
  21. Kang, S. H. (2008). Empirical analyses of long memory in the Korean Stock Market. Doctoral dissertation, School of Commerce Division of Business and Enterprise University of South Australia, Adelaid.
  22. Kapusuzoglu, A., and Ceylan, N. B. (2018). Trading volume, volatility and GARCH effects in Borsa Istanbul. Strategic design and innovative thinking in business operations: the role of business culture and risk management, 333-347.
  23. Köksal, B. (2009). A comparison of conditional volatility estimators for the ISE National 100 Index returns. Journal of Economic and Social Research, 11(2), 1-29.
  24. Kirchgässner, G., and Wolters, J. (2007). Introduction to modern time series analysis. Berlin: Springer.
  25. Kumar, A., and Biswal, S. K. (2019). Impulsive clustering and leverage effect of emerging stock market with special reference to Brazil, India, Indonesia, and Pakistan. Journal of Advanced Research in Dynamic Control System, 11, 33-37.
  26. Nguyen, C., and Nguyen, H. M. (2019). Modeling stock price volatility: empirical evidence from the ho chi minh city stock exchange in vietnam. The Journal of Asian Finance, Economics and Business, 6(3), 19-26. https://doi.org/10.13106/jafeb.2019.vol6.no3.19
  27. Ova, A. (2023). BIST Gıda ve İçecek Endeksi’nde yer alan şirketlerin finansal durum analizi. Ahi Evran Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(3), 670-682.
  28. Poon, S. H., and Granger, C. W. (2003). Forecasting volatility in financial markets: a review. Journal of Economic Literature, 41, 478–539.
  29. Sharma, S., Aggarwal, V., and Yadav, M. P. (2021). Comparison of linear and non-linear garch models for forecasting volatility of select emerging countries. Journal of Advances in Management Research, 18(4), 526-547. https://doi.org/10.1108/jamr-07-2020-0152
  30. Spulbar, C., Trivedi, J., and Birau, R. (2020). Investigating abnormal volatility transmission patterns between emerging and developed stock markets: a case study. Journal of Business Economics and Management, 21(6), 1561-1592. https://doi.org/10.3846/jbem.2020.13507
  31. Taylor, S. (1986). Modelling financial time series. New York: Wiley and Sons.
  32. Tsay, R. S. (2005). Analysis of financial time series. New York: John Wiley and Sons.
  33. Viljoen, H., Conradie, W.J., and Britz, M. (2022). The influence of different financial market regimes on the dynamic estimation of GARCH volatility model parameters and volatility forecasting. Studies in Economics and Econometrics, 46, 169 - 184.
  34. Zhang, J., Lai, Y., and Lin, J. (2017). The day-of-the-week effects of stock markets in different countries. Finance Research Letters, 20, 47-62.