Repetitive Stochastic Guesstimation for Estimating Parameters in a GARCH(1,1) Model
A behavioral algorithm for optimization - Repetitive Stochastic Guesstimation (RSG) - is adapted, with complete proofs for its global convergence, for estimating parameters in a GARCH(1,1) model, based on a very small number of observations. Estimators delivered by this algorithm for the example of a GARCH(1,1) model are dependent on some computational capabilities - namely number of iterations and replications performed. In this context, the Large Numbers Law might be applied in a completely different dimension. An alternative toward waiting until the historical data series are recorded (while the underling process may change several times) is to use computers for correctly extracting information from the most recent data. Given the existent computational support, it is also possible to determine estimates for the rates of convergence. As a result, potential benefits of this econometric technique can be gained in case of very young financial markets from Eastern European countries. Also, prediction and political decisions based on these estimations are properly grounded.
Volume (Year): (2010)
Issue (Month): 2 (July)
|Contact details of provider:|| Postal: Casa Academiei, Calea 13, Septembrie nr.13, sector 5, Bucureşti 761172|
Phone: 004 021 3188148
Fax: 004 021 3188148
Web page: http://www.ipe.ro/
More information through EDIRC
References listed on IDEAS
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.:
- Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
When requesting a correction, please mention this item's handle: RePEc:rjr:romjef:v::y:2010:i:2:p:213-222. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Corina Saman)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.