The Gaussian Mixture Dynamic Conditional Correlation Model: Parameter Estimation, Value at Risk Calculation, and Portfolio Selection
提出一种多元GARCH模型，用高斯混合分布处理标准化新息，通过最大似然和贝叶斯方法估计参数，并用于计算投资组合风险价值和选择最优组合，实证基于道琼斯和纳斯达克指数。
Abstract A multivariate generalized autoregressive conditional heteroscedasticity model with dynamic conditional correlations is proposed, in which the individual conditional volatilities follow exponential generalized autoregressive conditional heteroscedasticity models and the standardized innovations follow a mixture of Gaussian distributions. Inference on the model parameters and prediction of future volatilities are addressed by both maximum likelihood and Bayesian estimation methods. Estimation of the Value at Risk of a given portfolio and selection of optimal portfolios under the proposed specification are addressed. The good performance of the proposed methodology is illustrated via Monte Carlo experiments and the analysis of the daily closing prices of the Dow Jones and NASDAQ indexes. Keywords: : Bayesian inferenceGaussian mixture modelMaximum likelihood estimationMultivariate generalized autoregressive conditional heteroscedasticity modelPortfolio selectionValue at Risk