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All Journal dCartesian: Jurnal Matematika dan Aplikasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Kubik Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ekonomi dan Studi Pembangunan (Journal of Economics and Development Studies) Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Indonesian Journal of Data and Science Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Pendidikan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS ADPEBI International Journal of Business and Social Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Akuntansi dan Keuangan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Pendidikan Indonesia (Japendi) Jurnal Kedokteran STM (Sains dan Teknologi Medik) Eduvest - Journal of Universal Studies Multifinance KISA INSTITUE : Journal of Economics, Accounting, Business, Management, Engineering and Society Adpebi International Journal of Multidisciplinary Sciences d'Cartesian: Jurnal Matematika dan Aplikasi SJME (Supremum Journal of Mathematics Education)
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Journal : Media Statistika

SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL Nugroho, Didit Budi; Priyono, Agus; Susanto, Bambang
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.21-32

Abstract

The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.