Inference for stochastic volatility models using time change transformations

Konstantinos Kalogeropoulos (), Gareth O. Roberts and Petros Dellaportas

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates.

Keywords: imputation; Markov chain Monte Carlo; diffusion processes (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (7)

Published in Annals of Statistics, 2010, 38(2), pp. 784-807. ISSN: 0090-5364

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http://eprints.lse.ac.uk/31421/ Open access version. (application/pdf)

Related works:
Working Paper: Inference for stochastic volatility models using time change transformations (2007)
Working Paper: Inference for stochastic volatility model using time change transformations (2007)
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