Why does the Standard GARCH(1,1) model work well?

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Jafari, G R, Bahraminasab, A, Norouzzadeh, P
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.02.2007
Subjects
Arches
Autoregressive models
Criteria
Markov chains
Parameters
Regression analysis
Stochastic models
Time series
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Summary: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 results support the known idea that GARCH(1,1) model works well.
ISSN:2331-8422