<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1)"^^ . "Perilaku data runtun waktu finansial multivariat pasti memiliki volatilitas yang tinggi dan ragam yang heterogen, khususnya pada data return. Selain ragam yang heterogen, hal yang tidak dapat dihindari adalah korelasi antar variabel dan waktu. Sehingga untuk mengatasi hal tersebut salah satu model MGARCH yaitu DCC-GARCH (1,1) dianggap paling baik dalam pemodelannya. Hal ini didasarkan ide dasar model DCC-GARCH (1,1) memiliki ide dasar yaitu varians dan korelasi bersyarat sehingga model ini yaitu memodelkan varians dan korelasi bersyarat untuk mengatasi dinamika asimetris volatilitas. Tujuan penelitian ini adalah mendapatkan model DCC-GARCH (1,1) dan untuk meramalkan data return harian tiga saham yaitu PT. Bank Negara Indonesia Tbk., PT. Bank Rakyat Indonesia Tbk., dan Bank Mandiri Tbk. dari Februari 2010 hingga Agustus 2017. Model yang didapat adalah VAR (1) dan DCC-GARCH (1,1). Hasil ramalan untuk 20 periode berikutnya cukup baik dam semua nilai berada didalam interval konfidensi 95%.\r\nKata kunci: heteroskedatisitas, volatilitas, DCC-GARCH (1,1)\r\n\r\n\r\n\r\nabstract\r\n\r\nMultivariate financial time series data usually have high volatility and heterogeneous variation, especially in data of stock return. Besides of heterogeneous variety, the unavoidable thing is the correlation between variables and time. So to overcome this, one of the MGARCH model DCC-GARCH (1,1) is considered the best to overcome it. This model considered the best because the basic idea of DCC-GARCH (1,1) is to model the variance and conditional correlation on time variable so it is posible to overcome the asymmetric dynamics of volatility . The purpose of this research is to get DCC-GARCH (1,1) model and to forecast return daily data return of three stocks namely PT. Bank Negara Indonesia Tbk., PT. Bank Rakyat Indonesia Tbk., and PT. Bank Mandiri Tbk. from February 2010 to August 2017. The best model of the data is VAR (1) and DCC-GARCH (1,1). Forecast results for the next 20 periods are good enough and all values are within the confidential interval 95%. Key words: heteroscedasticity, volatility, DCC-GARCH (1,1)"^^ . "2018-03-07" . . . . . "UNIVERSITAS LAMPUNG"^^ . . . . . . . "1417031039"^^ . "Dea Elizabet Sirait"^^ . "1417031039 Dea Elizabet Sirait"^^ . . . . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (File PDF)"^^ . . . "ABSTRAK.pdf"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (File PDF)"^^ . . . "SKRIPSI TANPA BAB PEMBAHASAN.pdf"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (File PDF)"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "preview.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "medium.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "small.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "preview.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "medium.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "small.jpg"^^ . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . . "ANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC\r\n(DCC-GARCH) (1,1) (Other)"^^ . . . . . "HTML Summary of #30818 \n\nANALISIS DATA RETURN SAHAM MENGGUNAKAN MODEL DYNAMIC CONDITIONAL CORRELATION-GENERALIZED AUTOREGRESSIVE HETEROSCEDATIC \n(DCC-GARCH) (1,1)\n\n" . "text/html" . . . "Q Science (General)"@en . . . "QA Mathematics"@en . .