Flexible Modelling of Dependence in Volatility Processes

Maria Kalli, Jim Griffin

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


This paper proposes a novel stochastic volatility model that draws from the existing literature on autoregressive stochastic volatility models, aggregation of autoregressive 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.
Original languageEnglish
Pages (from-to)102-113
Number of pages12
JournalJournal of Business and Economic Statistics
Issue number1
Publication statusPublished - 1 Jan 2015

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