Hisse Senedi Getirilerindeki Volatilitenin Tahminlenmesinde Destek Vektör Makinelerine Dayalı Garch Modellerinin Kullanımı

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 financial markets. Therefore, volatility modeling 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.