Pregled bibliografske jedinice broj: 816457
Nonlinear Extension of Asymmetric GARCH Model within Neural Network Framework
Nonlinear Extension of Asymmetric GARCH Model within Neural Network Framework // Computer Science & Information Technology (CS & IT), 6 (2016), 6; 101-111 doi:10.5121/csit.2016.60609 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 816457 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Nonlinear Extension of Asymmetric GARCH Model
within Neural Network Framework
Autori
Arnerić, Josip ; Poklepović, Tea
Izvornik
Computer Science & Information Technology (CS & IT) (2231-5403) 6
(2016), 6;
101-111
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
conditional volatility ; GARCH model ; GJR model ; Neural Networks ; emerging markets
Sažetak
The importance of volatility for all market participants has led to the development and application of various econometric models. The most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. Since standard GARCH(1, 1) model usually indicate high persistence in the conditional variance, the empirical researches turned to GJR-GARCH model and reveal its superiority in fitting the asymmetric heteroscedasticity in the data. In order to capture both asymmetry and nonlinearity in data, the goal of this paper is to develop a parsimonious NN model as an extension to GJR- GARCH model and to determine if GJR-GARCH-NN outperforms the GJR-GARCH model.
Izvorni jezik
Engleski
Znanstvena područja
Ekonomija
POVEZANOST RADA
Projekti:
HRZZ-UIP-2013-11-5199 - Mjerenje, modliranje i prognoziranje volatilnosti (Volatility) (Arnerić, Josip, HRZZ - 2013-11) ( CroRIS)
Ustanove:
Ekonomski fakultet, Split,
Ekonomski fakultet, Zagreb
Citiraj ovu publikaciju:
Uključenost u ostale bibliografske baze podataka::
- DOAJ
- EBSCO
