A Bayesian semiparametric model for volatility with a leverage effect

Griffin, Jim E., Delatola, Eleni-Ioanna (2013) A Bayesian semiparametric model for volatility with a leverage effect. Computational Statistics and Data Analysis, 60 (1). pp. 97-110. ISSN 0167-9473. (doi:10.1016/j.csda.2012.10.023) (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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Abstract

A Bayesian semiparametric stochastic volatility model for financial data is developed. This nonparametrically estimates the return distribution from the data allowing for stylized facts such as heavy tails of the distribution of returns whilst also allowing for correlation between the returns and changes in volatility, which is usually termed the leverage effect. An efficient MCMC algorithm is described for inference. The model is applied to simulated data and two real data sets. The results of fitting the model to these data show that choosing a parametric return distribution can have a substantial effect on inference about the leverage effect.

Item Type: Article
DOI/Identification number: 10.1016/j.csda.2012.10.023
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: 29 May 2014 15:32 UTC
Last Modified: 29 May 2019 12:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41220 (The current URI for this page, for reference purposes)
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