Threshold Stochastic Volatility Models with Heavy Tails: A Bayesian Approach
| Autor: | Carlos A. Abanto-Valle, Hernán B. Garrafa-Aragón |
|---|---|
| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Economía, Vol 42, Iss 83 (2019) |
| Druh dokumentu: | article |
| ISSN: | 0254-4415 2304-4306 |
| DOI: | 10.18800/economia.201901.002 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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