Flexible Modelling of Dependence in Volatility Processes

37 Pages Posted: 28 Feb 2011 Last revised: 12 Jul 2013

See all articles by Maria Kalli

Maria Kalli

King's College London

Jim E. Griffin

University College London

Date Written: July 12, 2013

Abstract

This paper proposes a novel volatility model that draws from the existing literature on autoregressive stochastic volatility models, aggregation of autoregressive processes, and Bayesian nonparametric modelling to create a dynamic SV model that can explain 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 process. An efficient algorithm is defined which uses recently proposed adaptive Monte Carlo methods. The proposed model is applied to the daily returns of stock indices.

Keywords: Aggregation, Long-Range Dependence, MCMC, Bayesian nonparametrics, Dirichlet process, Stochastic volatility

JEL Classification: C11, C14, C22

Suggested Citation

Kalli, Maria and Griffin, Jim E., Flexible Modelling of Dependence in Volatility Processes (July 12, 2013). Available at SSRN: https://ssrn.com/abstract=1769655 or http://dx.doi.org/10.2139/ssrn.1769655

Maria Kalli (Contact Author)

King's College London ( email )

United Kingdom

Jim E. Griffin

University College London ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

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