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

2012
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Applied Mathematics
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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

doi:10.4236/am.2012.312a299
fatcat:vehzvhhqmza5xogcuzo5qqgqgm