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Journals / Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Cilt: 43 Sayı: 3

Volatility Forecasting in Borsa Istanbul: A Comparative Analysis of Participation and Conventional Indices

Borsa İstanbul’da Volatilite Öngörümlemesi: Katılım ve Konvansiyonel Endekslerin Karşılaştırmalı Analizi

Erdi Bayram(MANİSA CELÂL BAYAR ÜNİVERSİTESİ)0000-0003-4478-7231
Rabia Aktaş(MANİSA CELÂL BAYAR ÜNİVERSİTESİ)0000-0002-7006-5235
Koray Kayalıdere(MANİSA CELÂL BAYAR ÜNİVERSİTESİ)0000-0003-4073-1644
Pages
449–468
Publication Date
2025-09-26
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AI Summary & Key Takeaways

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

The results reveal that participation indices exhibit distinct volatility characteristics and that asymmetric models—especially EGARCH and TGARCH—produce satisfactory predictions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Volatility is a key input for investment decisions and portfolio management.
📊
Key Findings & Data:First, we estimate the model parameters using 809 days of data and explain the volatility features of the index return series.
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Conclusion & Impact:The results reveal that participation indices exhibit distinct volatility characteristics and that asymmetric models—especially EGARCH and TGARCH—produce satisfactory predictions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Borsa Istanbul · Volatilite tahmini · GARCH modelleri

Abstract

Volatility is a key input for investment decisions and portfolio management. Volatility modeling and forecasting are essential for risk analysis and risk management in financial markets. In this study, we investigate volatility characteristics of the four indices in Borsa Istanbul (Istanbul Stock Exchange) using GARCH family models. The primary motivation of this research is to model and forecast the volatility of participation and conventional indices. First, we estimate the model parameters using 809 days of data and explain the volatility features of the index return series. Second, we generate 57 days of forecast series and calculate realized volatility in the out-of-sample period. Finally, we analyze the predictive power of the forecast series of the models in relation to realized volatility, calculated using intraday returns. We use measures such as RMSE (Root Mean Squared Error), QLIKE (Quasi-Likelihood Loss), and R-squared (R²) to evaluate the predictability of GARCH models. The results reveal that participation indices exhibit distinct volatility characteristics and that asymmetric models—especially EGARCH and TGARCH—produce satisfactory predictions.

Özet

Volatilite yatırım kararları ve portföy yönetimi için temel girdilerden biridir. Volatilite modellemesi ve öngörümlemesi finansal piyasalarda risk analizi ve yönetimi açısından oldukça önemlidir. Bu çalışmada, Borsa İstanbul’daki dört farklı endeksin volatilite özelikleri GARCH tipi modellerle incelenmiştir. Araştırmanın temel motivasyonu katılım ve konvansiyonel endekslerin volatilitesini modellemek ve ilgili endekslerde öngörümleme yapmaktır. Öncelikle 809 günlük getiri verisiyle GARCH modellerinin parametreleri tahmin edilmiş ve endeks getiri serilerinin volatilite özelikleri incelenmiştir. Daha sonra örneklem dışı 57 günlük tahmin serileri elde edilmiş ve günlük bazda gerçekleşmiş volatilite hesaplanmıştır. Son olarak modellerin tahmin serilerinin gün içi getirilerle hesaplanan gerçekleşmiş volatiliteyi öngörümleme gücü analiz edilmiştir. GARCH modellerinin öngörümleme gücünü incelemek üzere RMSE (Root Mean Squared Error), QLIKE (Quasi-Likelihood Loss) ve R-squared (R2) ölçütleri hesaplanmıştır. Sonuçlar katılım endekslerinin, özelikle Katılım 30 endeksinin geleneksel endekslere kıyasla volatilite karakteristiğinin farklı olduğunu göstermektedir. Ayrıca EGARCH ve TGARCH asimetrik modellerin kullanılan diğer modellere göre gerçekleşen volatiliteyi öngörümleme açısından daha başarılı tahminler ürettiği izlenmektedir.

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Cite This Article

Bayram, E., Aktaş, R., Kayalıdere, K. (2025). Volatility Forecasting in Borsa Istanbul: A Comparative Analysis of Participation and Conventional Indices. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 43 Sayı: 3, 449–468. https://doi.org/10.17065/huniibf.1570979

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