Forecasting Stocks of Government Owned Companies (GOCS):Volatility Modeling

Erie Febrian () and Aldrin Herwany ()
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Erie Febrian: Finance & Risk Management Study Group (FRMSG) FE UNPAD

No 201002, Working Papers in Business, Management and Finance from Department of Management and Business, Padjadjaran University

Abstract: The development in forecasting techniques has been quite significant, which is indicated by the evolution on how researchers perceive characteristics of financial data. The researchers used to employ mean in their prediction model, but nowadays they tend to employ variance in developing the model. In addition, they also move from the static approaches (e.g., Autoregreesive (AR), Moving Average (MA), ARMA and ARIMA) to the dynamic ones (especially estimation model employing volatility change that just won Nobel prize in 2004). In this research, we try to develop the best prediction model by using volatility model, such as ARCH, GARCH, TARCH and EGARCH, and employing listed stocks of government-owned companies (GOCs) as the sample. The result proves that the employed volatility model and its derivatives are fairly accurate in predicting fluctuation of GOCs stock prices, which are reflected by the associated returns. In addition, the resulted model is capable to measure risk of the observed stock, as well as appropriate price of an asset.

Keywords: Forecasting; Volatility Model; Risk and Return (search for similar items in EconPapers)
JEL-codes: G0 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2010-02, Revised 2010-02
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Working Paper: Forecasting Stocks of Government Owned Companies (GOCS):Volatility Modeling (2009)
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