Inference for stochastic volatility models using time change transformations

Konstantinos Kalogeropoulos, Gareth O. Roberts, Petros Dellaportas

The Annals of Statistics · 2010 · Volume 38 · Issue 2

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.

Keywords

60J60, 62F15, 62M99, 65C60, 91B84, Diffusion processes, imputation, Markov chain Monte Carlo

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