Griffin, Jim and Steel, Mark F.J. (2008): Bayesian inference with stochastic volatility models using continuous superpositions of nonGaussian OrnsteinUhlenbeck processes.

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Abstract
This paper discusses Bayesian inference for stochastic volatility models based on continuous superpositions of OrnsteinUhlenbeck 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 twocomponent superposition on the daily Standard and Poor's 500 index from 1980 to 2000.
Item Type:  MPRA Paper 

Original Title:  Bayesian inference with stochastic volatility models using continuous superpositions of nonGaussian OrnsteinUhlenbeck processes 
Language:  English 
Keywords:  Leverage effect; Levy process; Long memory; Markov chain Monte Carlo; Stock price 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models G  Financial Economics > G1  General Financial Markets > G10  General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  11071 
Depositing User:  Mark F.J. Steel 
Date Deposited:  14. Oct 2008 05:04 
Last Modified:  16. Feb 2013 03:20 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/11071 