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

DOI: 10.4236/am.2012.312A299   PDF   HTML     4,778 Downloads   7,268 Views   Citations


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.

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V. Romo, E. Rodrigues and G. Tzintzun, "A Gibbs Sampling Algorithm to Estimate the Parameters of a Volatility Model: An Application to Ozone Data," Applied Mathematics, Vol. 3 No. 12A, 2012, pp. 2178-2190. doi: 10.4236/am.2012.312A299.

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The authors declare no conflicts of interest.


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