Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes

Jim Griffin and Mark Steel

MPRA Paper from University Library of Munich, Germany

Abstract: This paper discusses Bayesian inference for stochastic volatility models based on continuous superpositions of Ornstein-Uhlenbeck processes. These processes represent an alternative to the previously considered discrete superpositions. An interesting class of continuous superpositions is defined by a Gamma mixing distribution which can define long memory processes. We develop efficient Markov chain Monte Carlo methods which allow the estimation of such models with leverage effects. This model is compared with a two-component superposition on the daily Standard and Poor's 500 index from 1980 to 2000.

Keywords: Leverage effect; Levy process; Long memory; Markov chain Monte Carlo; Stock price (search for similar items in EconPapers)
JEL-codes: C11 C32 G10 (search for similar items in EconPapers)
Date: 2008-10-13
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:11071

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