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\iteman{ZMATH 1209.62029}
\itemau{Abanto-Valle, C.A.; Migon, H.S.; Lachos, V.H.}
\itemti{Stochastic volatility in mean models with scale mixtures of normal distributions and correlated errors: a Bayesian approach.}
\itemso{J. Stat. Plann. Inference 141, No. 5, 1875-1887 (2011).}
\itemab
Summary: A stochastic volatility in mean model with correlated errors using the symmetrical class of scale mixtures of normal distributions is introduced. The scale mixture of normal distributions is an attractive class of symmetric distributions that includes the normal, Student-t, slash and contaminated normal distributions as special cases, providing a robust alternative to estimation in stochastic volatility in mean models in the absence of normality. Using a Bayesian paradigm, an efficient method based on Markov chain Monte Carlo (MCMC) is developed for parameter estimation. The methods developed are applied to analyze daily stock return data from the S\~ao Paulo Stock, Mercantile \& Futures Exchange index (IBOVESPA). The Bayesian predictive information criteria (BPIC) and the logarithm of the marginal likelihood are used as model selection criteria. The results reveal that the stochastic volatility in mean model with correlated errors and slash distribution provides a significant improvement in model fit for the IBOVESPA data over the usual normal model.
\itemrv{~}
\itemcc{62F15 62P05 65C40}
\itemut{feedback and leverage effect; Markov chain Monte Carlo; non-Gaussian and nonlinear state-space models; scale mixture of normal distributions; stochastic volatility in mean}
\itemli{doi:10.1016/j.jspi.2010.11.039}
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