Flexible Modelling of Dependence in Volatility Processes

Kalli, Maria and Griffin, Jim E. (2015) Flexible Modelling of Dependence in Volatility Processes. Journal of Business and Economic Statistics, 33 (1). pp. 102-113. ISSN 0735-0015. E-ISSN 1537-2707. (doi:https://doi.org/10.1080/07350015.2014.925457) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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http://amstat.tandfonline.com/doi/abs/10.1080/0735...

Abstract

This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and Bayesian nonparametric modeling to create a SV 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 modeled 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
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 18 Feb 2015 15:47 UTC
Last Modified: 01 Dec 2017 09:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/47183 (The current URI for this page, for reference purposes)
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