Dynamic Modeling and Analysis of Some Energy Companies of Indonesia Over the Year 2018 to 2022 By Using VAR(p)-CCC GARCH(r,s) Model: -

Mustofa Usman, M. Komarudin, Nurhanurawati Nurhanurawati, Edwin Russel, Ahmad Sidiq, Warsono Warsono and F. A.m Elfaki
Additional contact information
Mustofa Usman: Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia,
M. Komarudin: Deparment of Technical Information, Faculty of Engineering, Universitas Lampung, Indonesia,
Nurhanurawati Nurhanurawati: Department of Mathematics Education, Faculty of Education, Universitas Lampung, Indonesia,
Edwin Russel: Department of Management, Faculty of Economics and Business, Universitas Lampung, Indonesia,
Ahmad Sidiq: Department of Industrial Engineering, Faculty of Engineering, Universitas Malahayati, Indonesia,
Warsono Warsono: Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia,
F. A.m Elfaki: Statistics Program, Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Qatar.

International Journal of Energy Economics and Policy, 2023, vol. 13, issue 4, 542-554

Abstract: The stock price plays an important role in a financial market. In this research, the natural relationship of the daily stock prices of two energy companies, namely the daily stock price of ADRO and PTBA, from January 2018 to December 2022 will be discussed. The purpose of this research is to obtain the best model that fits the data of the daily share price of the two companies, ADRO and PTBA. The analysis used to model the data is the multivariate time series method. From the results of the analysis, it was found that the best model is VAR(3)-CCC-GARCH)(1,1). Based on this VAR(3)-CCC-GARCH(1,1) model, further analysis: impulse response function (IRF), granger causality, the proportion of prediction error covariances, and forecasting for the next 30 days are discussed. The granger causality test found that the ADRO and PTBA have mutual granger causality (bidirectional). The results of the IRF analysis explain: If there is a shock of one standard deviation in ADRO, ADRO and PTBA have a response. ADRO's response is positive for the next 24 days with a downward trend, while PTBA's response is positive with an upward trend; If there is a shock of one standard deviation in PTBA, PTBA itself and ADRO respond. ADRO's response is negative and weak and has a downward trend in the next 24 days, while PTBA's response is quite large and positive with a downward trend. From the forecasting results for the next 30 days (period), ADRO's closing price has decreased, and PTBA data has a downward trend for the next 30 days.

Keywords: VAR(p) model; CCC GARCH(r s) model; Impulse Response Function; Granger causality; varaiance decomposition; forecasting (search for similar items in EconPapers)
JEL-codes: C53 Q4 Q47 (search for similar items in EconPapers)
Date: 2023
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