Threshold Stochastic Volatility Models with Heavy Tails: A Bayesian Approach
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2019-09-16
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Pontificia Universidad Católica del Perú. Fondo Editorial
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This 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.
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MMarkov chain Monte Carlo, Non linear state space models, Scale mixtures of normal distributions, Stochastic volatility, Threshold, Value-at-Risk, Expected shortfall, Modelos de volatilidad
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Excepto se indique lo contrario, la licencia de este artículo se describe como info:eu-repo/semantics/openAccess