BibTex Citation Data :
@article{J.Gauss14720, author = {Deby Fakhriyana and Abdul Hoyyi and Tatik Widiharih}, title = {PERBANDINGAN MODEL ARCH/GARCH MODEL ARIMA DAN MODEL FUNGSI TRANSFER (Studi Kasus Indeks Harga Saham Gabngan dan Harga Minyak Mentah Dunia Tahun 2013 sampai 2015)}, journal = {Jurnal Gaussian}, volume = {5}, number = {4}, year = {2016}, keywords = {Indonesian Composite Index, crude oil prices, ARIMA, transfer function, ARCH/GARCH}, abstract = { Indonesian Composite Index is a value that used to measure the combined performance of shares listed in stock market. Price of crude oil is one of the factors that affect Indonesian Composite Index. If the prices of crude oil is increasing, it will be responsed by Indonesian goverment directly with also increasing the fuel prices, that will have an impact on Indonesian Composite Index. ARIMA and transfer function are methods of modeling time series data and it have assumption that the residual models have to be homogen. To overcome violations of those assumption, this study continue to modelling ARCH/GARCH with ARIMA and transfer function approach. The data used in this study are daily of Indonesian Composite Index and West Texas Intermediate (WTI) crude oil prices data from 2013 to 2015. This study gained two models, the first is ARIMA (1,1,[3]) which variance model of ARCH(1), it’s AIC value is equal to 7707,4287. The second is transfer fuction model (1,0,0) which noise model ARMA(0,[1,3) as well as variance model ARCH(1), it’s AIC value equal to 7689,18984. The best model is the one that has smallest AIC value. From this study can be concluded that the best of ARCH/GARCH model is ARCH/GARCH model with transfer function approach. Keywords : Indonesian Composite Index, crude oil prices, ARIMA, transfer function, ARCH/GARCH }, issn = {2339-2541}, pages = {633--640} doi = {10.14710/j.gauss.5.4.633-640}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/14720} }
Refworks Citation Data :
Indonesian Composite Index is a value that used to measure the combined performance of shares listed in stock market. Price of crude oil is one of the factors that affect Indonesian Composite Index. If the prices of crude oil is increasing, it will be responsed by Indonesian goverment directly with also increasing the fuel prices, that will have an impact on Indonesian Composite Index. ARIMA and transfer function are methods of modeling time series data and it have assumption that the residual models have to be homogen. To overcome violations of those assumption, this study continue to modelling ARCH/GARCH with ARIMA and transfer function approach. The data used in this study are daily of Indonesian Composite Index and West Texas Intermediate (WTI) crude oil prices data from 2013 to 2015. This study gained two models, the first is ARIMA (1,1,[3]) which variance model of ARCH(1), it’s AIC value is equal to 7707,4287. The second is transfer fuction model (1,0,0) which noise model ARMA(0,[1,3) as well as variance model ARCH(1), it’s AIC value equal to 7689,18984. The best model is the one that has smallest AIC value. From this study can be concluded that the best of ARCH/GARCH model is ARCH/GARCH model with transfer function approach.
Keywords : Indonesian Composite Index, crude oil prices, ARIMA, transfer function, ARCH/GARCH
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