Forecasting Stocks of Government Owned Companies (GOCS):Volatility Modeling
AbstractThe 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.
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Bibliographic InfoPaper provided by Department of Economics, Padjadjaran University in its series Working Papers in Economics and Development Studies (WoPEDS) with number 200908.
Length: 17 pages
Date of creation: Sep 2009
Date of revision: Sep 2009
Forecasting; Volatility Model; Risk and Return;
Find related papers by JEL classification:
- G0 - Financial Economics - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-09-19 (All new papers)
- NEP-FMK-2009-09-19 (Financial Markets)
- NEP-FOR-2009-09-19 (Forecasting)
- NEP-RMG-2009-09-19 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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