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Why Does The Standard Garch(1, 1) Model Work Well?

Author

Listed:
  • G. R. JAFARI

    (Department of Physics, Shahid Beheshti University, Evin, Tehran 19839, Iran;
    Department of Nano-Science, IPM, P. O. Box 19395-5531, Tehran, Iran)

  • A. BAHRAMINASAB

    (Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom;
    The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, I-34100 Trieste, Italy)

  • P. NOROUZZADEH

    (Department of Physics, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerpen, Belgium)

Abstract
The AutoRegressive Conditional Heteroskedasticity (ARCH) and its generalized version (GARCH) family of models have grown to encompass a wide range of specifications, each of them is designed to enhance the ability of the model to capture the characteristics of stochastic data, such as financial time series. The existing literature provides little guidance on how to select optimal parameters, which are critical in efficiency of the model, among the infinite range of available parameters. We introduce a new criterion to find suitable parameters in GARCH models by using Markov length, which is the minimum time interval over which the data can be considered as constituting a Markov process. This criterion is applied to various time series and its results support the known idea that GARCH(1, 1) model works well.

Suggested Citation

  • G. R. Jafari & A. Bahraminasab & P. Norouzzadeh, 2007. "Why Does The Standard Garch(1, 1) Model Work Well?," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1223-1230.
    • Handle: RePEc:wsi:ijmpcx:v:18:y:2007:i:07:n:s0129183107011261
    DOI: 10.1142/S0129183107011261
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    References listed on IDEAS

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