Garch Ve Yapay Sinir Ağları Modelleri Yardımıyla Volatilite Tahmini: Türk Borsası Örneği

Yıl 2023, Cilt: 25 Sayı: 2, 572 - 595, 31.12.2023

Cumhur Şahin

Öz

Küreselleşme olgusunun 1990’lı yıllarla birlikte baskın hale gelmesiyle birlikte uluslararası ekonomik düzende ülkelerin birbiriyle etkileşim ve entegrasyonunun çarpıcı bir biçimde artması ve iktisadi bağların kuvvetlenmesi, finansal piyasalarda yaşanan hızlı değişimler, pazarlar arasındaki ilişkilerin ve risklerin artmasına yol açmaktadır. Finansal piyasalar hali hazırda ortaya çıkan gelişmelere karşı çok daha hassas hale gelmektedir. Finansla ilgili akademik araştırmalarda özellikle finansal zaman serileri ve bunların öngörüsüne yönelik çalışmalar oldukça önemli bir yer tutmaktadır. Dalgalanma veya oynaklık olarak da ifade edilebilen volatilite kavramı finansal piyasalarda vazgeçilmez bir yere sahiptir. Bundan dolayı, volatilitenin en yüksek duyarlılıkla tahmin edilmesi son derece yararlıdır. Son yıllarda finansal endekslerin oynaklığını tahmin etmek için GARCH (Generalized Autoregressive Conditional Heteroskedasticity-Genelleştirilmiş Otoregresif Koşullu Değişen Varyans) tipi modellerin yanı sıra ANN (Artificial Neural Network-yapay sinir ağları) modelleri de yaygın olarak kullanılmaktadır. Bu araştırmanın amacı, farklı model türlerinin birleştirilmesinin menkul kıymet borsa endeks volatilitesi tahminlerini iyileştirip iyileştiremeyeceğine karar verilmesidir. Bu nedenle, BIST-100 Endeksi volatilitesini tahmin etme yetenekleri açısından iki hibrit model kullanılarak, Asimetrik GARCH modeli ve bir yapay sinir ağı modeli karşılaştırılmıştır. Sonuçlar, bir EGARCH modeli tarafından elde edilen şartlı volatilite tahminlerinin yanı sıra, getirileri ve tarihsel değerleri girdi olarak kabul eden bir yapay sinir ağına dayanan hibrit modelin en iyi tahmin gücünü sağladığını ortaya koymaktadır. Ayrıca, bu hibrit modelin baskınlığı, tahminin geri kalan modelleri de kapsayacak şekilde olmasıdır. Son olarak, Türk borsasında önemli kaldıraç etkileri bulunduğu gösterilmiştir.

Anahtar Kelimeler

Yapay Sinir Ağı, GARCH Modelleri, Volatilite Tahmini

Kaynakça

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