caa a22 4500 605519595 CHVBK 20210128100731.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s11009-013-9389-9 doi (NATIONALLICENCE)springer-10.1007/s11009-013-9389-9 Bayesian Estimation of a Skew-Student-t Stochastic Volatility Model [Elektronische Daten] [C. Abanto-Valle, V. Lachos, Dipak Dey] In this paper we present a stochastic volatility (SV) model assuming that the return shock has a skew-Student-t distribution. This allows a parsimonious, flexible treatment of skewness and heavy tails in the conditional distribution of returns. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed and used for parameter estimation and forecasting. The MCMC method exploits a skew-normal mixture representation of the error distribution with a gamma distribution as the mixing distribution. The developed methodology is applied to the NASDAQ daily index returns. Bayesian model selection criteria as well as out-of-sample forecasting in a value-at-risk (VaR) study reveal that the SV model based on skew-Student-t distribution provides significant improvement in model fit as well as prediction to the NASDAQ index data over the usual normal model. Springer Science+Business Media New York, 2013 Markov chain Monte Carlo nationallicence Non-Gaussian and nonlinear state space models nationallicence Skew-Student-t nationallicence Stochastic volatility nationallicence Value-at-risk nationallicence Abanto-Valle C. Department of Statistics, Federal University of Rio de Janeiro, CP 68530, CEP 21945-970, Rio de Janeiro, RJ, Brazil aut Lachos V. Department of Statistics, Campinas State University, CP 6065, CEP 13083-859, Campinas, SP, Brazil aut Dey Dipak Department of Statistics, University of Connecticut, Storrs, CT, USA aut Methodology and Computing in Applied Probability Springer US; http://www.springer-ny.com 17/3(2015-09-01), 721-738 1387-5841 17:3<721 2015 17 11009 https://doi.org/10.1007/s11009-013-9389-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11009-013-9389-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Abanto-Valle C. Department of Statistics, Federal University of Rio de Janeiro, CP 68530, CEP 21945-970, Rio de Janeiro, RJ, Brazil aut NATIONALLICENCE 700 1- Lachos V. Department of Statistics, Campinas State University, CP 6065, CEP 13083-859, Campinas, SP, Brazil aut NATIONALLICENCE 700 1- Dey Dipak Department of Statistics, University of Connecticut, Storrs, CT, USA aut NATIONALLICENCE 773 0- Methodology and Computing in Applied Probability Springer US; http://www.springer-ny.com 17/3(2015-09-01), 721-738 1387-5841 17:3<721 2015 17 11009