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All Journal Jurnal Ekonomi Kuantitatif Terapan dCartesian: Jurnal Matematika dan Aplikasi Media Statistika Indonesian Journal of Mathematics and Natural Sciences Gadjah Mada International Journal of Business Jurnal Sains Matematika dan Statistika Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications International Journal of Computing Science and Applied Mathematics International Journal on Emerging Mathematics Education Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ComTech: Computer, Mathematics and Engineering Applications Jurnal Linguistik Komputasional Imajiner: Jurnal Matematika dan Pendidikan Matematika KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Abdi Insani SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Journal of Science and Science Education Jurnal Akuntansi dan Keuangan Prosiding University Research Colloquium Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya d'Cartesian: Jurnal Matematika dan Aplikasi Limits: Journal of Mathematics and Its Applications
Didit Budi Nugroho
Universitas Kristen Satya Wacana
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Journal : ComTech: Computer, Mathematics and Engineering Applications

 1 
Volatility Fitting Performance of QGARCH(1,1) Model with Student-t, GED, and SGED Distributions Didit Budi Nugroho; Bintoro Ady Pamungkas; Hanna Arini Parhusip
ComTech: Computer, Mathematics and Engineering Applications Vol. 11 No. 2 (2020): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v11i2.6391

Abstract

The research had two objectives. First, it compared the performance of the Generalized Autoregressive Conditional Heteroscedasticity (1,1) (GARCH) and Quadratic GARCH (1,1) (QGARCH)) models based on the fitting to real data sets. The model assumed that return error follows four different distributions: Normal (Gaussian), Student-t, General Error Distribution (GED), and Skew GED (SGED). Maximum likelihood estimation was usually employed in estimating the GARCH model, but it might not be easily applied to more complicated ones. Second, it provided two ways to evaluate the considered models. The models were estimated using the Generalized Reduced Gradient (GRG) Non-Linear method in Excel’s Solver and the Adaptive Random Walk Metropolis (ARWM) in the Scilab program. The real data in the empirical study were Financial Times Stock Exchange Milano Italia Borsa (FTSEMIB) and Stoxx Europe 600 indices over the daily period from January 2000 to December 2017 to test the conditional variance process and see whether the estimation methods could adapt to the complicated models. The analysis shows that GRG Non-Linear in Excel’s Solver and ARWM methods have close results. It indicates a good estimation ability. Based on the Akaike Information Criterion (AIC), the QGARCH(1,1) model provides a better fitting than the GARCH(1,1) model on each distribution specification. Overall, the QGARCH(1,1) with SGED distribution best fits both data.
Volatility Fitting Performance of QGARCH(1,1) Model with Student-t, GED, and SGED Distributions Didit Budi Nugroho; Bintoro Ady Pamungkas; Hanna Arini Parhusip
ComTech: Computer, Mathematics and Engineering Applications Vol. 11 No. 2 (2020): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v11i2.6391

Abstract

The research had two objectives. First, it compared the performance of the Generalized Autoregressive Conditional Heteroscedasticity (1,1) (GARCH) and Quadratic GARCH (1,1) (QGARCH)) models based on the fitting to real data sets. The model assumed that return error follows four different distributions: Normal (Gaussian), Student-t, General Error Distribution (GED), and Skew GED (SGED). Maximum likelihood estimation was usually employed in estimating the GARCH model, but it might not be easily applied to more complicated ones. Second, it provided two ways to evaluate the considered models. The models were estimated using the Generalized Reduced Gradient (GRG) Non-Linear method in Excel’s Solver and the Adaptive Random Walk Metropolis (ARWM) in the Scilab program. The real data in the empirical study were Financial Times Stock Exchange Milano Italia Borsa (FTSEMIB) and Stoxx Europe 600 indices over the daily period from January 2000 to December 2017 to test the conditional variance process and see whether the estimation methods could adapt to the complicated models. The analysis shows that GRG Non-Linear in Excel’s Solver and ARWM methods have close results. It indicates a good estimation ability. Based on the Akaike Information Criterion (AIC), the QGARCH(1,1) model provides a better fitting than the GARCH(1,1) model on each distribution specification. Overall, the QGARCH(1,1) with SGED distribution best fits both data.
Volatility Fitting Performance of QGARCH(1,1) Model with Student-t, GED, and SGED Distributions Didit Budi Nugroho; Bintoro Ady Pamungkas; Hanna Arini Parhusip
ComTech: Computer, Mathematics and Engineering Applications Vol. 11 No. 2 (2020): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v11i2.6391

Abstract

The research had two objectives. First, it compared the performance of the Generalized Autoregressive Conditional Heteroscedasticity (1,1) (GARCH) and Quadratic GARCH (1,1) (QGARCH)) models based on the fitting to real data sets. The model assumed that return error follows four different distributions: Normal (Gaussian), Student-t, General Error Distribution (GED), and Skew GED (SGED). Maximum likelihood estimation was usually employed in estimating the GARCH model, but it might not be easily applied to more complicated ones. Second, it provided two ways to evaluate the considered models. The models were estimated using the Generalized Reduced Gradient (GRG) Non-Linear method in Excel’s Solver and the Adaptive Random Walk Metropolis (ARWM) in the Scilab program. The real data in the empirical study were Financial Times Stock Exchange Milano Italia Borsa (FTSEMIB) and Stoxx Europe 600 indices over the daily period from January 2000 to December 2017 to test the conditional variance process and see whether the estimation methods could adapt to the complicated models. The analysis shows that GRG Non-Linear in Excel’s Solver and ARWM methods have close results. It indicates a good estimation ability. Based on the Akaike Information Criterion (AIC), the QGARCH(1,1) model provides a better fitting than the GARCH(1,1) model on each distribution specification. Overall, the QGARCH(1,1) with SGED distribution best fits both data.
Co-Authors A. Setiawan A.A. Ketut Agung Cahyawan W Adi Setiawan Agus Priyono Alfagustina, Yumita Cristin ALOYSIUS JOAKIM FERNANDEZ Anggita M Kusumawati Arisandi, Nia Lestari Atyanta Nika Rukmasari Bambang Susanto Beni Utomo Daivi Wardani, Daivi Daniel Eliazar Latumaerissa Dini Kurniawati E. D. Saputri, E. D. Erlina Prihatnani Faldy Tita Hanafi, Fika Maula Hanna Arini Parhusip Hariadi, Adrianus Herry Hariyanto, H Hindriyanto Dwi Purnomo Irfani, Nur Istiarsi Saptuti Sri Kawuryan Istiarsih Saputri Sri Kawuryan Kezia Natalia Putri Prasetia Kholil, Zaini Kristia Anggraeni Kurniawan, Johanes Dian Lea, Lea Lennox Larwuy Leopoldus Ricky Sasongko Leopoldus Ricky Sasongko Lydia Soepriyani Fallo Mince M. M. Rosely Nia Lestari Arisandi Obed Christian Dimitrio Pamungkas, Bintoro Ady Panjaitan, Lam P. Panjaitan, Lam Peter Prasetia, Kezia Natalia Putri Puspitasari, Agnes Dhika Putri, Zefania Sasongko Rahmawanto, Setya Budi Rebecca Rorimpandey Rorimpandey, Rebecca Rudhito, Andy Santosa, Petrus Priyo Sassongko, Leopoldus Ricky Sri Kawuryan, Istiarsi Saptuti Sri Suryasatriya Trihandaru Tundjung Mahatma Veny M Ningtyas Wijaya, Melina Tito Wulandari, Nadya Putri Yulius Pratomo