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Estimating Stochastic Volatility Models Using a Discrete Non-linear Filter. Working paper #3

Author

Listed:
  • Adam Clements
  • Stan Hurn
  • Scott White

    (National Centre for Econometric Research)

Abstract

Many approaches have been proposed for estimating stochastic volatility (SV) models, a number of which are filtering methods. While non-linear filtering methods are superior to linear approaches, non-linear filtering methods have not gained a wide acceptance in the econometrics literature due to their computational cost. This paper proposes a discretised non-linear filtering (DNF) algorithm for the estimation of latent variable models. It is shown that the DNF approach leads to significant computational gains relative to other procedures in the context of SV estimation without any associated loss in accuracy. It is also shown how a number of extensions to standard SV models can be accommodated within the DNF algorithm.

Suggested Citation

  • Adam Clements & Stan Hurn & Scott White, 2006. "Estimating Stochastic Volatility Models Using a Discrete Non-linear Filter. Working paper #3," NCER Working Paper Series 3, National Centre for Econometric Research.
    • Handle: RePEc:qut:auncer:2006-3
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    File URL: http://www.ncer.edu.au/papers/documents/WPNo3.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    non-linear filtering; stochastic volatility; state-space models; asymmetries; latent factors; two factor volatility models;
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