Comparing stochastic volatility models through Monte Carlo simulations
Computational Statistics & Data Analysis
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models
Computational Statistics & Data Analysis
Mixture periodic autoregressive conditional heteroskedastic models
Computational Statistics & Data Analysis
Asymmetric multivariate normal mixture GARCH
Computational Statistics & Data Analysis
Time-varying joint distribution through copulas
Computational Statistics & Data Analysis
A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood
Computational Statistics & Data Analysis
Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation
Expert Systems with Applications: An International Journal
Hi-index | 0.03 |
Bayesian inference and prediction for a generalized autoregressive conditional heteroskedastic (GARCH) model where the innovations are assumed to follow a mixture of two Gaussian distributions is performed. The mixture GARCH model can capture the patterns usually exhibited by many financial time series such as volatility clustering, large kurtosis and extreme observations. A Griddy-Gibbs sampler implementation is proposed for parameter estimation and volatility prediction. Bayesian prediction of the Value at Risk is also addressed providing point estimates and predictive intervals. The method is illustrated using the Swiss Market Index.