BibTex Citation Data :
@article{JM1363, author = {Susi Wahyuni and Nur Iriawan and Dwi AW}, title = {PERAMALAN VOLATILITAS INDEKS HARGA SAHAM MENGGUNAKAN MODEL ASIMETRIK GARCH (GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY) DENGAN DISTRIBUSI SKEWED STUDENT-t}, journal = {MATEMATIKA}, volume = {8}, number = {1}, year = {2012}, keywords = {}, abstract = { The condition of Indonesian economic in the last fiew years fluctuatics following konjungtur cycle. Besides in economic problem, non economic problem such as social and politic are also influence that fluctuation. It means, the instability had influenced of stock exchange practition in analyzing and predicting return. Financial data, such as stock exchange price indices often has heteroscedasticity. One of modeling technique to analyze the condition is using GARCH models. Unfortunately, GARCH models often do not fully capture the thick tails property of high frequency financial time series. To cope this weakness, we’ll an Asymmetric GARCH model will be used. Using Box-Jenkins methods, the Composite Price Indices have mean model ARIMA (10 17 69,1,0). With the same data we can having GARCH (2,1) model. The Asymmetric GARCH (AGARCH) model in this research was not properly proper to model the Composite Price Indices Volatility. }, url = {https://ejournal.undip.ac.id/index.php/matematika/article/view/1363} }
Refworks Citation Data :
The condition of Indonesian economic in the last fiew years fluctuatics following konjungtur cycle. Besides in economic problem, non economic problem such as social and politic are also influence that fluctuation. It means, the instability had influenced of stock exchange practition in analyzing and predicting return. Financial data, such as stock exchange price indices often has heteroscedasticity. One of modeling technique to analyze the condition is using GARCH models. Unfortunately, GARCH models often do not fully capture the thick tails property of high frequency financial time series. To cope this weakness, we’ll an Asymmetric GARCH model will be used. Using Box-Jenkins methods, the Composite Price Indices have mean model ARIMA (10 17 69,1,0). With the same data we can having GARCH (2,1) model. The Asymmetric GARCH (AGARCH) model in this research was not properly proper to model the Composite Price Indices Volatility.
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