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HİSSE SENEDİ GETİRİLERİNDEKİ VOLATİLİTENİN TAHMİNLENMESİNDE DESTEK VEKTÖR MAKİNELERİNE DAYALI GARCH MODELLERİNİN KULLANIMI

VOLATILITY FORECASTING IN STOCK RETURNS USING SUPPORT VECTOR MACHINES BASED GARCH MODELS

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Abstract (2. Language): 
Volatility, as a spread of all likely outcomes of an uncertain variable, is crucial phenomenon for the investors who must consider the spread of asset returns in finacial markets. Therefore, volatility modelling and forecasting plays an important role in financial risk management. Support Vector Machine (SVM) is an efficient learning technique for classification and regression problems, including financial time series forecasting. In this study, we aimed to compare the forecasting performance of SVM based GARCH(1,1), EGARCH(1,1) and GJR-GARCH(1,1) models with their corresponding classical models using daily returns in Istanbul Stock Exchange for the period 04.01.2007 – 30.12.2012. The results confirmed the remarkable generalization performance of SVM, as shown in the SVM literature.
Abstract (Original Language): 
Belirsiz bir değişkenin alabileceği olası tüm değerlerin dağılımının ifadesi olarak volatilite, finansal piyasalardaki varlıkların getirilerinin sergilediği değişkenliği dikkate alması gereken bir yatırımcı için hayati bir olgudur. Sonuç olarak volatilitenin modellenmesi ve tahminlenmesi finansal risk yönetiminde önemli rol oynar. Bu çalışmada GARCH tipi modellerden GARCH(1,1), EGARCH(1,1) ve GJR-GARCH(1,1) modellerine, son yıllarda gittikçe popülaritesi artan güçlü bir makine öğrenmesi metodu olan Destek Vektör Makineleri (DVM) ile yaklaşılmıştır. Bu amaçla 04.01.2007 – 31.12.2012 dönemine ait günlük İMKB ulusal 100 endeksi-kapanış fiyatları kullanılmış ve modellerin klasik çözümü ile DVM çözümlerinin tahminleme performansları kıyaslanmıştır. Elde edilen sonuçlara göre, DVM’ye dayalı karma GARCH modellerinin daha iyi performans gösterdiği gözlenmiştir.
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REFERENCES

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