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Inference for stochastic volatility model using time change transformations

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Author Info
Kalogeropoulos, Konstantinos
Roberts, Gareth O.
Dellaportas, Petros

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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.

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File URL: http://mpra.ub.uni-muenchen.de/5697/
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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 5697.

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Date of creation: 2007
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Handle: RePEc:pra:mprapa:5697

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Related research
Keywords: Imputation Markov chain Monte Carlo Stochastic volatility

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis

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