Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form

Charles Bos and Neil Shephard ()

No 2004-W02, Economics Papers from Economics Group, Nuffield College, University of Oxford

Abstract: In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algoritms for this type of model are ineffective, but that this problem can be removed by reparameterising the model. We illustrate our results on an example from financial economics and one from the nonparametric regression literature. We also develop an effective particle filter for this model which is useful to assess the fit of the model.

Keywords: Markov chain Monte Carlo; particle filter; cubic spline; state space form; stochastic volatility. (search for similar items in EconPapers)
Pages: 30 pages
Date: 2004-02-25
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form (2006)
Working Paper: Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space form (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:nuf:econwp:042

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