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Our estimation method is based on the fact that the powerful hidden Markov model (HMM) machinery can be applied in order to evaluate an arbitrarily accurate approximation of the likelihood of an SVM model with SMN distributions. Likelihood\u2010based estimation of the parameters of stochastic volatility models, in general, and SVM models with SMN distributions, in particular, is usually regarded as challenging as the likelihood is a high\u2010dimensional multiple integral. However, the HMM approximation, which is very easy to implement, makes numerical maximum of the likelihood feasible and leads to simple formulae for forecast distributions, for computing appropriately defined residuals, and for decoding, that is, estimating the volatility of the process. Copyright \u00a9 2017 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/asmb.2246","type":"journal-article","created":{"date-parts":[[2017,3,14]],"date-time":"2017-03-14T15:30:39Z","timestamp":1489505439000},"page":"394-408","source":"Crossref","is-referenced-by-count":8,"title":["Maximum likelihood estimation for stochastic volatility in mean models with heavy\u2010tailed distributions"],"prefix":"10.1002","volume":"33","author":[{"given":"Carlos A.","family":"Abanto\u2010Valle","sequence":"first","affiliation":[{"name":"Department of Statistics Federal University of Rio de Janeiro  Caixa Postal 68530 Rio de Janeiro CEP: 21945\u2010970 Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Langrock","sequence":"additional","affiliation":[{"name":"Department of Business Administration and Economics Bielefeld University  Postfach 10 01 31 Bielefeld 33501 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