Bayesian analysis of stochastic volatility models with flexible tails

Mark Steel

Econometric Reviews, 1998, vol. 17, issue 2, 109-143

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.

Keywords: financial time series; leptokurtic distributions; Markov Chain Monte Carlo; Skewed Exponential Power distribution (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1080/07474939808800408

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