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Repetitive Stochastic Guesstimation for Estimating Parameters in a GARCH(1,1) Model


  • Agapie, Adriana

    () (UNESCO Chair for Business Administration, Academy of Economic Studies, Bucharest, Romania)

  • Bratianu, Constantin

    () (UNESCO Chair for Business Administration, Academy of Economic Studies, Bucharest, Romania)


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.

Suggested Citation

  • Agapie, Adriana & Bratianu, Constantin, 2010. "Repetitive Stochastic Guesstimation for Estimating Parameters in a GARCH(1,1) Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 213-222, July.
  • Handle: RePEc:rjr:romjef:v::y:2010:i:2:p:213-222

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    References listed on IDEAS

    1. 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.
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    Cited by:

    1. Acatrinei, Marius & Gorun, Adrian & Marcu, Nicu, 2013. "A DCC-GARCH Model To Estimate the Risk to the Capital Market in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 136-148, March.

    More about this item


    RSG; GARCH Model; financial markets;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)


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