Abanto-Valle, Carlos A.Garrafa-Aragón, Hernán B.2019-09-16http://revistas.pucp.edu.pe/index.php/economia/article/view/21103/20850This paper extends the threshold stochastic volatility (THSV) model specification proposed in So et al. (2002) and Chen et al. (2008) by incorporating thick-tails in the mean equation innovation using the scale mixture of normal distributions (SMN). A Bayesian Markov Chain Monte Carlo algorithm is developed to estimate all the parameters and latent variables. Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting via a computational Bayesian framework are considered. The MCMC-based method exploits a mixture representation of the SMN distributions. The proposed methodology is applied to daily returns of indexes from BM&F BOVESPA (BOVESPA), Buenos Aires Stock Exchange (MERVAL), Mexican Stock Exchange (MXX) and the Standar & Poors 500 (SP500). Bayesian model selection criteria reveals that there is a significant improvement in model fit for the returns of the data considered here, by using the THSV model with slash distribution over the usual normal and Student-t models. Empirical results show that the skewness can improve VaR and ES forecasting in comparison with the normal and Student-t models.application/pdfenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0MMarkov chain Monte CarloNon linear state space modelsScale mixtures of normal distributionsStochastic volatilityThresholdValue-at-RiskExpected shortfallModelos de volatilidadinfo:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#5.02.01https://doi.org/10.18800/economia.201901.002