Full Bayesian Inference for GARCH and EGARCH Models
提出GARCH和EGARCH模型的完全贝叶斯分析方法，涵盖参数估计、模型选择和波动率预测，并用雅典股票交易所指数示例说明。
A full Bayesian analysis of GARCH and EGARCH models is proposed consisting of parameter estimation, model selection, and volatility prediction. The Bayesian paradigm is implemented via Markov-chain Monte Carlo methodologies. We provide implementation details and illustrations using the General Index of the Athens stock exchange.