Stochastic volatility systems

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Stochastic volatility models aie a well-known framework for the analysis of financial time series data, together with the other important class of ARCH-type models. The main difference between them, at least from a statistical point of view, relies on the possibility of obtaining exact inference, in particular with regard to the estimation issue. Whereas for ARCH-type models the standard results apply, in the sense that maximum likelihood estimates for the parameters of interest can be computed, for stochastic volatility models there are more complications and usually only approximate results can be obtained, unless two particular estimation strategies are employed: exact non-Gaussian filtering methods or simulation techniques. This paper stresses the importance of "only" approximate and therefore suboptimal estimation methods for special models whose complexity makes it difficult to find exact solutions. The setup where the analysis is conducted is the state-space formulation and this suggests enclosing the cases here considered in a class of so-called stochastic volatility systems.

Original languageEnglish (US)
Pages (from-to)137-142
Number of pages6
JournalInternational Journal of Modelling and Simulation
Issue number2
StatePublished - 1997
Externally publishedYes


  • Bilinear stochastic processes
  • Estimation algorithms
  • Linear
  • Nonlinear and nongaussian state space formulations
  • Stochastic volatility systems

ASJC Scopus subject areas

  • Modeling and Simulation
  • Mechanics of Materials
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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