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

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Author Info
Griffin, Jim
Steel, Mark F.J.

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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.

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File URL: http://mpra.ub.uni-muenchen.de/11071/
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11071.

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Date of creation: 13 Oct 2008
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Handle: RePEc:pra:mprapa:11071

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Related research
Keywords: Leverage effect; Levy process; Long memory; Markov chain Monte Carlo; Stock price;

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. S. P. Brooks & P. Giudici & G. O. Roberts, 2003. "Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 3-39. [Downloadable!] (restricted)
  2. Gareth O. Roberts & Omiros Papaspiliopoulos & Petros Dellaportas, 2004. "Bayesian inference for non-Gaussian Ornstein-Uhlenbeck stochastic volatility processes," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 369-393. [Downloadable!] (restricted)
  3. Griffin, J.E. & Steel, M.F.J., 2006. "Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility," Journal of Econometrics, Elsevier, vol. 134(2), pages 605-644, October. [Downloadable!] (restricted)
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