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A Bayesian semiparametric model for volatility with a leverage effect

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  • Delatola, E.-I.
  • Griffin, J.E.

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.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 60 (2013)
Issue (Month): C ()
Pages: 97-110

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Handle: RePEc:eee:csdana:v:60:y:2013:i:c:p:97-110

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Web page: http://www.elsevier.com/locate/csda

Related research

Keywords: Dirichlet process; Asset return; Stock index; Off-set mixture representation; Mixture model; Centred representation;

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Cited by:
  1. Jensen, Mark J & Maheu, John M, 2013. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," MPRA Paper 52132, University Library of Munich, Germany.

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