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All Journal Jurnal Sains dan Teknologi BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Sains dan Edukasi Sains Prosiding University Research Colloquium
Faldy Tita
Universitas Kristen Satya Wacana
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Other

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Journal : Prosiding University Research Colloquium

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Study on The Continuous-Jump Behavior of Asset Return Volatility Through The GJR Model Alfagustina, Yumita Cristin; Nugroho, Didit Budi; Tita, Faldy
Prosiding University Research Colloquium Proceeding of The 17th University Research Colloquium 2023: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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Abstract

Generalized Auto-Regressive Conditional Heteroskeasticity (GARCH) is a model used to predict the volatility of returns. Volatility is a statistical measure of the movement of returns for securities (financial instruments that can only be traded through markets or securities companies) or certain market indices. Then the GARCH model was further developed into an asymmetric form, namely conditional volatility and returns have a relationship, namely the GJR model which is an abbreviation of the name (Glosten- Jagannathan-Runkle). This research focuses on the GJR-X by adding high-frequency exogenous variables in volatility process and on the GARCH-CJ which is a decomposition of the exogenous variable X, namely the continuous component C (Continuous) and the jump J (Jump). TOPIX data (Tokyo Stock Price Index) is the real data used in this study. To estimate the model parameters, the ARWM (Adaptive Random Walk Metropolis) method will be used with the MCMC (Markov Chain Monte Carlo) algorithm. First, it was found that the ARWM method is good at estimating parameters. Second, the AIC value of GJR-CJ was smaller than that of GJR-X, which means that GJR-CJ had better data fitting.
Co-Authors Adi Setiawan Alfagustina, Yumita Cristin Bambang Susanto Didit Budi Nugroho Didit Nugroho, Didit Dimitrio, Obed Hanafi, Fika Maula Hanna Arini Parhusip Kurniawan, Titus Antonius David Lennox Larwuy Puspitasari, Agnes Dhika Wulandari, Nadya Putri