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

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Author Info

  • 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.

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File URL: http://www.ncer.edu.au/papers/documents/WPNo3.pdf
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Bibliographic Info

Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 3.

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Date of creation: 15 Aug 2006
Date of revision:
Handle: RePEc:qut:auncer:2006-3

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Fax: 07 3138 1500
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Related research

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

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References

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  16. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Wiley Blackwell, vol. 61(2), pages 247-64, April.
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Cited by:
  1. Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.

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