BibTex Citation Data :
@article{J.Gauss26654, author = {Rizki Widyantomo and Abdul Hoyyi and Tatik Widiharih}, title = {PEMODELAN VOLATILITAS RETURN PORTOFOLIO SAHAM MENGGUNAKAN FEED FORWARD NERURAL NETWORK (Studi Kasus :PT Bumi Serpong Damai Tbk. Dan PT H.M Sampoerna Tbk.)}, journal = {Jurnal Gaussian}, volume = {7}, number = {2}, year = {2018}, keywords = {Time series Analysis, ARCH / GARCH, Neural Network, Return.}, abstract = { Time series analysis is an analysis used to predict a time-observed data, one of which is the ARIMA model. ARIMA model assumes a constant residual variance (homogeneous). While financial data usually produce ARIMA model with variance error that is not constant. If the assumption of homogeneity of the residual variance is not met, then the method that can be used is ARCH or GARCH model. Another method that can be used on the data assuming the homogeneity of the variance error is not met is the Neural Network model. In this model we use Neural Network model with variance and residual as the input variables that obtained from ARCH / GARCH model. The data used are BSDE and HMSP asset portfolio returns from November 14, 2016 to January 18, 2018. In this study the selected input variables are from ARIMA (1.0.1) GARCH (1,1) model. The best Neural Network model obtained is Neural Network model with 10 hidden layers with MSE value 6.58 x10-10 with model train evaluation which is MAPE value 1.14441%. Keywords : Time series Analysis, ARCH / GARCH, Neural Network, Return. }, issn = {2339-2541}, pages = {200--211} doi = {10.14710/j.gauss.7.2.200-211}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/26654} }
Refworks Citation Data :
Time series analysis is an analysis used to predict a time-observed data, one of which is the ARIMA model. ARIMA model assumes a constant residual variance (homogeneous). While financial data usually produce ARIMA model with variance error that is not constant. If the assumption of homogeneity of the residual variance is not met, then the method that can be used is ARCH or GARCH model. Another method that can be used on the data assuming the homogeneity of the variance error is not met is the Neural Network model. In this model we use Neural Network model with variance and residual as the input variables that obtained from ARCH / GARCH model. The data used are BSDE and HMSP asset portfolio returns from November 14, 2016 to January 18, 2018. In this study the selected input variables are from ARIMA (1.0.1) GARCH (1,1) model. The best Neural Network model obtained is Neural Network model with 10 hidden layers with MSE value 6.58 x10-10 with model train evaluation which is MAPE value 1.14441%.
Keywords: Time series Analysis, ARCH / GARCH, Neural Network, Return.
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