Simulation of right and left truncated gamma distributions by mixtures
Statistics and Computing
Block sampler and posterior mode estimation for asymmetric stochastic volatility models
Computational Statistics & Data Analysis
Bayesian modeling of financial returns: A relationship between volatility and trading volume
Applied Stochastic Models in Business and Industry
Editorial: Special issue on variable selection and robust procedures
Computational Statistics & Data Analysis
Small area estimation using skew normal models
Computational Statistics & Data Analysis
Hi-index | 0.03 |
A Bayesian analysis of stochastic volatility (SV) models using the class of symmetric scale mixtures of normal (SMN) distributions is considered. In the face of non-normality, this provides an appealing robust alternative to the routine use of the normal distribution. Specific distributions examined include the normal, student-t, slash and the variance gamma distributions. Using a Bayesian paradigm, an efficient Markov chain Monte Carlo (MCMC) algorithm is introduced for parameter estimation. Moreover, the mixing parameters obtained as a by-product of the scale mixture representation can be used to identify outliers. The methods developed are applied to analyze daily stock returns data on S&P500 index. Bayesian model selection criteria as well as out-of-sample forecasting results reveal that the SV models based on heavy-tailed SMN distributions provide significant improvement in model fit as well as prediction to the S&P500 index data over the usual normal model.