Flexible Modeling of Dependence in Volatility Processes
提出一种新的随机波动率模型，通过聚合自回归过程并采用贝叶斯非参数方法对系数分布建模，能够捕捉波动率的长期依赖特征，并应用于股票日收益率数据。
This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and Bayesian nonparametric modeling to create a SV model that can capture long-range dependence. The volatility process is assumed to be the aggregate of autoregressive processes, where the distribution of the autoregressive coefficients is modeled using a flexible Bayesian approach. The model provides insight into the dynamic properties of the volatility. An efficient algorithm is defined which uses recently proposed adaptive Monte Carlo methods. The proposed model is applied to the daily returns of stocks.