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Inference for stochastic volatility models using time change transformations

Kalogeropoulos, Konstantinos, Roberts, Gareth O. and Dellaportas, Petros (2010) Inference for stochastic volatility models using time change transformations. Annals of Statistics, 38 (2). pp. 784-807. ISSN 0090-5364

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Identification Number: 10.1214/09-AOS702

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.

Item Type: Article
Official URL: http://www.imstat.org/aos/
Additional Information: © 2010 IMS
Divisions: Statistics
Subjects: Q Science > QA Mathematics
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C11 - Bayesian Analysis
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C15 - Statistical Simulation Methods; Monte Carlo Methods; Bootstrap Methods
C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models
Sets: Departments > Statistics
Date Deposited: 13 Jan 2011 14:11
Last Modified: 20 Feb 2019 09:31
URI: http://eprints.lse.ac.uk/id/eprint/31421

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