Volatility Modelling Using Hybrid Autoregressive Conditional Heteroskedasticity (ARCH) - Support Vector Regression (SVR)

Yasin, Hasbi and Tarno, Tarno and Hoyyi, Abdul (2015) Volatility Modelling Using Hybrid Autoregressive Conditional Heteroskedasticity (ARCH) - Support Vector Regression (SVR). Prosiding Seminar Internasional 5th ISNPINSA 2015 . pp. 183-187. ISSN ISBN: 978-602-71169-7-9

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Abstract

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%. Keywords: ARCH; ARIMA; SVR; Volatility

Item Type:Article
Subjects:H Social Sciences > HA Statistics
Divisions:Faculty of Science and Mathematics > Department of Statistics
ID Code:52877
Deposited By:Mr Hasbi Yasin
Deposited On:05 Apr 2017 16:20
Last Modified:05 Apr 2017 16:28

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