J. Yu and R. Meyer, “Multivariate Stochastic Volatility models: Bayesian Estimation and Model Comparison,” Econometric Reviews, Vol. 25, No. 2-3, 2006, pp. 361-384. doi:10.1080/07474930600713465

has been cited by the following article:

• TITLE: A Gibbs Sampling Algorithm to Estimate the Parameters of a Volatility Model: An Application to Ozone Data

  AUTHORS: Verónica De Jesús Romo, Eliane R. Rodrigues, Guadalupe Tzintzun

  KEYWORDS: MCMC Algorithms; Bayesian Inference; Volatility Models; Ozone Air Pollution; Mexico City

  JOURNAL NAME: Applied Mathematics, Vol.3 No.12A, December 31, 2012

  ABSTRACT: In this work we consider a stochastic volatility model, commonly used in financial time series studies, to analyse ozone data. The model considered depends on some parameters and in order to estimate them a Markov chain Monte Carlo algorithm is proposed. The algorithm considered here is the so-called Gibbs sampling algorithm which is programmed using the language R. Its code is also given. The model and the algorithm are applied to the weekly ozone averaged measurements obtained from the monitoring network of Mexico City.