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Journals / Ege Akademik Bakış Dergisi / 2026 / Cilt: 26 Sayı: 1

Forecasting The Volatility of Bist 100 Index Return with Linear and Nonlinear Time Series Models

Erkan Işığıçok(BURSA ULUDAĞ ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, EKONOMETRİ BÖLÜMÜ)0000-0003-4037-0869
Hakan Öndes(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, EKONOMETRİ BÖLÜMÜ)0000-0002-0618-7705
Pages
43–62
Publication Date
2026-01-03
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AI Summary & Key Takeaways

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

It can be said that hybrid models are superior in the risk analysis of volatile financial instruments and in the estimation of macroeconomic variables in general.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The successful modeling and forecasting of volatility, which is the most important element of risk indicators, minimizes financial uncertainties.
📊
Key Findings & Data:In fact, with the recent increase in the number of artificial neural network studies, hybrid models with the combined advantages of multiple model structures have gained importance.
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Conclusion & Impact:It can be said that hybrid models are superior in the risk analysis of volatile financial instruments and in the estimation of macroeconomic variables in general.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Volatilite · Yapay Sinir Ağları ve Hibrit Modeller · BIST 100 Endeks Getirisi

Abstract

The successful modeling and forecasting of volatility, which is the most important element of risk indicators, minimizes financial uncertainties. Classical volatility models are insufficient to forecast structural changes in economic variables. In fact, with the recent increase in the number of artificial neural network studies, hybrid models with the combined advantages of multiple model structures have gained importance. The aim of this study is to demonstrate that hybrid models are more reliable and consistent models in forecasting volatility in variables. For this purpose, the return volatility of the Borsa Istanbul 100 index was modeled, and forecasting performance results were compared with hybrid models. According to the findings, the best forecasting performance was achieved with hybrid structures containing the exponential GARCH-Artificial Neural Networks (MSEGARCH-ANN) combination. It can be said that hybrid models are superior in the risk analysis of volatile financial instruments and in the estimation of macroeconomic variables in general.

Özet

Risk göstergelerinin en önemli unsuru olan volatilitenin başarılı bir şekilde modellenmesi ve öngörülmesi finansal belirsizlikleri en aza indirmektedir. Klasik volatilite modelleri ekonomik değişkenlerdeki yapısal değişimleri öngörmede yetersiz kalmaktadır. Nitekim son yıllarda yapay sinir ağı çalışmalarının artmasıyla birlikte çoklu model yapılarının avantajlarını bir arada bulunduran hibrit modeller önem kazanmıştır. Bu çalışmanın amacı hibrit modellerin değişkenlerdeki volatiliteyi öngörmede daha güvenilir ve tutarlı modeller olduğunu ortaya koymaktır. Bu amaçla Borsa İstanbul 100 endeksinin getiri volatilitesi modellenmiş ve öngörü performans sonuçları hibrit modellerle karşılaştırılmıştır. Bulgulara göre en iyi öngörü performansı üstel GARCH-Yapay Sinir Ağları (MSEGARCH-ANN) kombinasyonunu içeren hibrit yapılarla elde edilmiştir. Hibrit modellerin volatil finansal araçların risk analizinde ve genel olarak makroekonomik değişkenlerin tahmininde üstün olduğu söylenebilir.

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Cite This Article

Işığıçok, E., Öndes, H. (2026). Forecasting The Volatility of Bist 100 Index Return with Linear and Nonlinear Time Series Models. Ege Akademik Bakış Dergisi, Cilt: 26 Sayı: 1, 43–62. https://doi.org/10.21121/eab.20260104

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