Yasin, Hasbi and Tarno, T. and Hoyyi, Abdul (2015) Volatility Modelling Using Hybrid Autoregressive Conditional Heteroskedasticity (ARCH) - Support Vector Regression (SVR). In: The 5th International Seminar on New Paradigm and Innovation on Natural Sciences and Its Application (5th ISNPINSA) i , 7-8 Oktober 2015, Semarang.
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High fluctuations in stock returns is one problem that is considered by the investors. Therefore we need a model that is able to predict accurately the volatility of stock returns. One model that can be used is a model Autoregressive Conditional Heteroskedasticity (ARCH). This model can serve as a model input in the Support Vector Regression (SVR) model, known as Hybrid ARCH-SVR. This modeling is one of the alternatives in modeling the volatility of stock returns. This method is able to show a good performance in modeling the volatility of stock returns. The purpose of this study was to determine the stock return volatility models using a Hybrid ARCH-SVR model on stock price data of PT. Indofood Sukses Makmur Tbk. The result shows that the determination of the input variables based on the ARIMA (3,0,3)-ARCH (5), so that the SVR model consists of 5 lags as input vector. Using a this model was obtained that the Mean Absolute Percentage Error (MAPE) of 1,98% and R2 =99,99%.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Q Science > Q Science (General)|
|Divisions:||Faculty of Science and Mathematics > Department of Chemistry|
|Deposited By:||INVALID USER|
|Deposited On:||28 May 2018 09:23|
|Last Modified:||28 May 2018 09:23|
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