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


  • Erie Febrian

    () (Finance & Risk Management Study Group (FRMSG) FE UNPAD)

  • Aldrin Herwany

    () (Research Division, Laboratory of Management FE UNPAD)


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.

Suggested Citation

  • Erie Febrian & Aldrin Herwany, 2009. "Forecasting Stocks of Government Owned Companies (GOCS):Volatility Modeling," Working Papers in Economics and Development Studies (WoPEDS) 200908, Department of Economics, Padjadjaran University, revised Sep 2009.
  • Handle: RePEc:unp:wpaper:200908

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    References listed on IDEAS

    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Aggarwal, Reena & Inclan, Carla & Leal, Ricardo, 1999. "Volatility in Emerging Stock Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(01), pages 33-55, March.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    More about this item


    Forecasting; Volatility Model; Risk and Return;

    JEL classification:

    • G0 - Financial Economics - - General

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