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Convergence of Heuristic-based Estimators of the GARCH Model

  • Alexandru MandesEmail author
  • Cristian Gatu
  • Peter Winker
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)

Abstract

The GARCH econometric model is able to describe the volatility of financial data under realistic assumptions and the convergence of its theoretical estimators has been proven. However, when data is “unfriendly” maximum likelihood estimators need to be computed by stochastic optimization algorithms in order to avoid local optima attraction basins, and thus, a new source of uncertainty is introduced. A formal framework for joint convergence analysis of both, the estimators and the heuristic, has been previously described within the context of the GARCH(1,1) model. The aim of this contribution is to adapt and extend this research to asymmetric and multiple lagged GARCH models. Aspects of subset model selection are also investigated.

Keywords

Bayesian Information Criterion GARCH Model Joint Convergence Model Selection Algorithm Smooth Transition Autoregressive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Alexandru Mandes
    • 1
    Email author
  • Cristian Gatu
    • 1
  • Peter Winker
    • 2
    • 3
  1. 1.Alexandru Ioan Cuza UniversityIasiRomania
  2. 2.Justus Liebig University GiessenGiessenGermany
  3. 3.Centre for European Economic ResearchMannheimGermany

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