Inference for stochastic volatility models using time change transformations

TL;DR

This paper introduces a new MCMC-based approach for parameter estimation in stochastic volatility models using time change transformations, improving computational efficiency and addressing degeneracy issues.

Contribution

It presents an innovative reparametrisation via time scale transformations and a novel MCMC scheme tailored for stochastic volatility models.

Findings

The proposed method effectively estimates parameters in simulated data.

Application to US treasury bill rates demonstrates practical utility.

Algorithm is fast and robust for models with stochastic volatility.

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.