ASYMMETRIC POWER AND FRACTIONALLY INTEGRATED SUPPORT VECTOR AND NEURAL NETWORK GARCH MODELS WITH AN APPLICATION TO FORECASTING FINANCIAL RETURNS IN ISE100 STOCK INDEX
Küçük Resim Yok
Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Editura Ase
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The study aims to augment commonly applied volatility models with support vector machines and neural networks. Further, fractional integration and asymmetric powers will be introduced. The proposed modeling strategy benefits from neural network based GARCH models and SVR-GARCH models. Following these approaches, the study proposed fractional integration and asymmetric power GARCH structures to obtain SVR-FIAPGARCH and NN-FIAPGARCH models to be evaluated in terms of learning algorithms. Models are evaluated for in-sample and out-of-sample forecasting of daily returns in Istanbul ISE100 stock index. Results suggest several findings: i. fractional integration and asymmetric power structures could be modeled with learning algorithms. ii. volatility clustering, asymmetry and nonlinearity characteristics are modeled more effectively with SVR-GARCH and MLP-GARCH models compared to the GARCH models. iii. SVR-GARCH models provided the lowest error criteria levels in out-of-sample and are closely followed by the MLP-GARCH models.
Açıklama
Anahtar Kelimeler
Volatility, Stock Returns, ARCH, Fractional Integration, MLP
Kaynak
Economic Computation And Economic Cybernetics Studies And Research
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
48
Sayı
2
