Analysis of Some Variable Energy Companies by Using VAR(p)-GARCH(r,s) Model : Study From Energy Companies of Qatar over the Years 2015–2022 - Usman, Mustofa; Komarudin, M.; Sarida, Munti; Wamiliana, Wamiliana; Russel, Edwin; Kufepaksi, Mahatma; Ali Alam, Iskandar; A.M. Elfaki, Faiz (International Journal of Energy Economics and Policy, 2022)
In this study, the nature of the weekly stock price relationships of several Qatar energy companies, namely the weekly stock price of Qatar Fuel Company (QFLS), Qatar Gas Transport Company (QGTS), and Qatar Electricity and Water Company (QEWC), will be discussed. The duration of data weekly stock price is from January 2015 to April 2022. This study aimed to obtain the best model for the weekly stock price relationship of the three companies QFLS, QGTS, and QEWC. The multivariate time series analysis method will be used to evaluate the data. From the analysis using multivariate time series modeling, the best model is VAR(3)-GARCH)(1,1). Based on this best model, further analysis is carried out, namely Granger causality, impulse response function (IRF), and forecasting for the next 12 periods. The Granger causality test found that the QFLS has Granger causality on the QGTS (unidirectional), while the QGTS and QEWC variables have bidirectional Granger causality. The IRF analysis indicated that if there is a shock of 1 standard deviation in QFLS, then QFLS and QEWC will fluctuate for the first six weeks and move toward equilibrium from the seventh week onwards, while the impact on QGTS can be ignored. Suppose there is a shock of 1 standard deviation in the QGTS. In that case, the QFLS and QEWC will respond by fluctuating for the first six weeks, and at the seventh week and move toward equilibrium, while the impact on QGTS can be ignored; and if there is a shock of 1 standard deviation in QEWC, then QFLS and QEWC will respond negatively and fluctuating for the first six weeks, and at the seventh week toward equilibrium, while the impact on QGTS is negligible. Forecasting for the next 12 periods shows that the farther the forecasting period, the larger the standard error. This indicates that the ffarther the period is, the more unstable it is.