Inference for stochastic volatility model using time change transformations

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

MPRA Paper from University Library of Munich, Germany

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; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 G12 (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ict
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Inference for stochastic volatility models using time change transformations (2010)
Working Paper: Inference for stochastic volatility models using time change transformations (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:5697

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