Forecasting Volatility İn Oil Prices With A Class Of Nonlinear Volatility Models: Smooth Transition Rbf And Mlp Neural Networks Augmented Garch Approach

Ersin, Özgür; Bildirici, Melike

Forecasting Volatility İn Oil Prices With A Class Of Nonlinear Volatility Models: Smooth Transition Rbf And Mlp Neural Networks Augmented Garch Approach

dc.contributor.author	Ersin, Özgür
dc.contributor.author	Bildirici, Melike
dc.date.accessioned	2019-07-17T13:17:18Z
dc.date.available	2019-07-17T13:17:18Z
dc.date.issued	2015
dc.department	İstanbul Beykent Üniversitesi	en_US
dc.description.abstract	In this study, the forecasting capabilities of a new class of nonlinear econometric models, namely, the LSTAR-LST-GARCH-RBF and MLP models are evaluated. The models are utilized to model and to forecast the daily returns of crude oil prices. Many financial time series are subjected to leptokurtic distribution, heavy tails, and nonlinear conditional volatility. This characteristic feature leads to deterioration in the forecast capabilities of traditional models such as the ARCH and GARCH models. According to the empirical findings, the oil prices and their daily returns could be classified as possessing nonlinearity in the conditional mean and conditional variance processes. Several model groups are evaluated: (i) the models proposed in the first group are the LSTAR-LST-GARCH models that are augmented with fractional integration and asymmetric power terms (FIGARCH, APGARCH, and FIAPGARCH); (ii) the models proposed in the second group are the LSTAR-LST-GARCH models further augmented with MLP and RBF type neural networks. The models are compared in terms of MSE, RMSE, and MAE criteria for in-sample and out-of-sample forecast capabilities. The results show that the LSTAR based and neural network augmented models provide important gains over the single-regime baseline GARCH models, followed by the LSTAR-LST-GARCH type models in terms of modeling and forecasting volatility in crude oil prices.	en_US
dc.identifier.doi	10.1007/s12182-015-0035-8
dc.identifier.issn	1995-8226
dc.identifier.scopus	2-s2.0-84938982513	en_US
dc.identifier.scopusquality	Q1	en_US
dc.identifier.uri	https://doi.org/10.1007/s12182-015-0035-8
dc.identifier.wos	WOS:000363726400015	en_US
dc.identifier.wosquality	Q2	en_US
dc.indekslendigikaynak	Web of Science	en_US
dc.indekslendigikaynak	Scopus	en_US
dc.language.iso	en	en_US
dc.publisher	Springer Link	tr_TR
dc.relation.publicationcategory	Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı	en_US
dc.subject	Volatility	tr_TR
dc.subject	Petrol prices	tr_TR
dc.subject	ARCH	tr_TR
dc.subject	STAR	tr_TR
dc.subject	Neural networks	tr_TR
dc.subject	LSTAR-LST-GARCH family	tr_TR
dc.title	Forecasting Volatility İn Oil Prices With A Class Of Nonlinear Volatility Models: Smooth Transition Rbf And Mlp Neural Networks Augmented Garch Approach	en_US
dc.type	Article	en_US