Flexible modeling of dependence in volatility processes

Journal article


Kalli, M. and Griffin, J. 2015. Flexible modeling of dependence in volatility processes. Journal of Business and Economic Statistics. 33 (1), pp. 102-113. https://doi.org/10.1080/07350015.2014.925457
AuthorsKalli, M. and Griffin, J.
Abstract

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.

KeywordsAggregation; Long-Range Dependence; MCMC; Bayesian nonparametrics; Dirichlet process; Stochastic volatility
Year2015
JournalJournal of Business and Economic Statistics
Journal citation33 (1), pp. 102-113
PublisherTaylor & Francis
ISSN0735-0015
Digital Object Identifier (DOI)https://doi.org/10.1080/07350015.2014.925457
Publication dates
Print2015
Publication process dates
Deposited23 Aug 2013
Accepted12 Jun 2014
Output statusPublished
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