Flexible modeling of dependence in volatility processes

This paper proposes a novel stochastic volatility model that draws from the exist- ing literature on autoregressive stochastic volatility models, aggregation of autoregres- sive processes, and Bayesian nonparametric modelling to create a stochastic volatility 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 modelled 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.