Flexible modeling of dependence in volatility processes

Kalli, M. and Griffin, J. (2015) Flexible modeling of dependence in volatility processes. Journal of Business and Economic Statistics, 33 (1). pp. 102-113. ISSN 0735-0015.


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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.

Item Type: Article
Uncontrolled Keywords: Aggregation; Long-Range Dependence; MCMC; Bayesian nonparametrics; Dirichlet process; Stochastic volatility
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
Divisions: pre Nov-2014 > Faculty of Business and Management > Department of Accounting, Finance & Information Management
Depositing User: Maria Kalli
Date Deposited: 23 Aug 2013 10:09
Last Modified: 26 Feb 2016 04:23
URI: https://create.canterbury.ac.uk/id/eprint/12157

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Last edited: 29/06/2016 12:23:00