Estimating stochastic volatility models using integrated nested laplace approximations

Sara Martino, Kjersti Aas*, Ola Lindqvist, Linda R. Neef, Håvard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Volatility in financial time series is mainly analysed through two classes of models; the generalized autoregressive conditional heteroscedasticity (GARCH) models and the stochastic volatility (SV) ones. GARCH models are straightforward to estimate using maximum-likelihood techniques, while SV models require more complex inferential and computational tools, such as Markov Chain Monte Carlo (MCMC). Hence, although provided with a series of theoretical advantages, SV models are in practice much less popular than GARCH ones. In this paper, we solve the problem of inference for some SV models by applying a new inferential tool, integrated nested Laplace approximations (INLAs). INLA substitutes MCMC simulations with accurate deterministic approximations, making a full Bayesian analysis of many kinds of SV models extremely fast and accurate. Our hope is that the use of INLA will help SV models to become more appealing to the financial industry, where, due to their complexity, they are rarely used in practice.

Original languageEnglish (US)
Pages (from-to)487-503
Number of pages17
JournalEuropean Journal of Finance
Issue number7
StatePublished - Aug 2011
Externally publishedYes


  • Approximate bayesian inference
  • Laplace approximation
  • Latent gaussian models
  • Stochastic volatility model

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (miscellaneous)


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