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April 2010 Inference for stochastic volatility models using time change transformations
Konstantinos Kalogeropoulos, Gareth O. Roberts, Petros Dellaportas
Ann. Statist. 38(2): 784-807 (April 2010). DOI: 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 reparametrization 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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Konstantinos Kalogeropoulos. Gareth O. Roberts. Petros Dellaportas. "Inference for stochastic volatility models using time change transformations." Ann. Statist. 38 (2) 784 - 807, April 2010. https://doi.org/10.1214/09-AOS702

Information

Published: April 2010
First available in Project Euclid: 19 February 2010

zbMATH: 1189.91220
MathSciNet: MR2604696
Digital Object Identifier: 10.1214/09-AOS702

Subjects:
Primary: 60J60 , 65C60 , 91B84
Secondary: 62F15 , 62M99

Keywords: Diffusion processes , imputation , Markov chain Monte Carlo

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.38 • No. 2 • April 2010
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