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Bayesian analysis of stochastic volatility models with flexible tails

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  • Mark Steel

Abstract

An alternative distributional assumption is proposed for the stochastic volatility model. This results in extremely flexible tail behaviour of the sampling distribution for the observables, as well as in the availability of a simple Markov Chain Monte Carlo strategy for posterior analysis. By allowing the tail behaviour to be determined by a separate parameter, we reserve the parameters of the volatility process to dictate the degree of volatility clustering. Treatment of a mean function is formally integrated in the analysis. Some empirical examples on both stock prices and exchange rates clearly indicate the presence of fat tails, in combination with high levels of volatility clustering. In addition, predictive distributions indicate a good fit with these typical financial data sets.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 17 (1998)
Issue (Month): 2 ()
Pages: 109-143

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Handle: RePEc:taf:emetrv:v:17:y:1998:i:2:p:109-143

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Related research

Keywords: financial time series; leptokurtic distributions; Markov Chain Monte Carlo; Skewed Exponential Power distribution;

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Cited by:
  1. Carmen Broto & Esther Ruiz, 2002. "Estimation Methods For Stochastic Volatility Models: A Survey," Statistics and Econometrics Working Papers ws025414, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2003. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model," Working Papers 7, University of Verona, Department of Economics.

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