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Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility

Delatola, Eleni-Ioanna, Griffin, Jim E. (2011) Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility. Bayesian Analysis, 6 (4). pp. 901-926. ISSN 1936-0975. (doi:10.1214/11-BA632) (KAR id:29597)

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Official URL:
http://dx.doi.org/10.1214/11-BA632

Abstract

This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility model. The distribution of the logarithm of the squared return is flexibly modelled using an infinite mixture of Normal distributions. This allows efficient Markov chain Monte Carlo methods to be developed. Links between the return distribution and the distribution of the logarithm of the squared returns are discussed. The method is applied to simulated data, one asset return series and one stock index return series. We find that estimates of volatility using the model can differ dramatically from those using a Normal return distribution if there is evidence of a heavy-tailed return distribution.

Item Type: Article
DOI/Identification number: 10.1214/11-BA632
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jim Griffin
Date Deposited: 30 May 2012 12:30 UTC
Last Modified: 16 Nov 2021 10:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/29597 (The current URI for this page, for reference purposes)
Griffin, Jim E.: https://orcid.org/0000-0002-4828-7368
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