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All Journal International Journal of Computing Science and Applied Mathematics-IJCSAM
Hanna Arini Parhusip
Master Program in Data Science, Universitas Kristen Satya Wacana
Mathematics

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Modeling and Estimating GARCH-X and Realized GARCH Using ARWM and GRG Methods Didit Budi Nugroho; Melina Tito Wijaya; Hanna Arini Parhusip
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 1 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i1.4309

Abstract

This study evaluates the fitting performance of GARCH-X(1,1) and RealGARCH(1,1) models, which are extensions of GARCH(1,1) model by adding the Realized Kernel measure as an exogenous component, on real data, namely the Financial Times Stock Exchange 100 and Hang Seng stock indices over the period from January 2000 to December 2017. The models assume that the return error follows Normal and Student- t distributions. The parameters of models are estimated by using the Adaptive Random Walk Metropolis (ARWM) method implemented in Matlab and the Generalized Reduced Gradient (GRG) method. The comparison of estimation results shows that the GRG method has a good ability to estimate the models because it provides the estimation results that are close to the results of the ARWM method in terms of relative error. On the basis of Akaike Information Criterion, the RealGARCH models perform better than the GARCH-X models, where the RealGARCH model with Student- t distribution provides the best fit.
Co-Authors Didit Budi Nugroho Melina Tito Wijaya