MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model

Nunzio Cappuccio (), Diego Lubian and Davide Raggi
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Nunzio Cappuccio: Department of Economics (University of Padova)

No 07/2003, Working Papers from University of Verona, Department of Economics

Abstract: In this paper we present a stochastic volatility model assuming that the return shock has a Skew-GED distribution. This allows a parsimonious yet flexible treatment of asymmetry and heavy tails in the conditional distribution of returns. The Skew-GED distribution nests both the GED, the Skew-normal and the normal densities as special cases so that specification tests are easily performed. Inference is conducted under a Bayesian framework using Markov Chain MonteCarlo methods for computing the posterior distributions of the parameters. More precisely, our Gibbs-MH updating scheme makes use of the Delayed Rejection Metropolis-Hastings methodology as proposed by Tierney and Mira (1999), and of Adaptive-Rejection Metropolis sampling. We apply this methodology to a data set of daily and weekly exchange rates. Our results suggest that daily returns are mostly symmetric with fat-tailed distributions while weekly returns exhibit both significant asymmetry and fat tails.

Keywords: Stochastic volatility; Markov Chain MonteCarlo; Skewness; Heavy tails; Bayesian inference; Metropolis-Hastings sampling (search for similar items in EconPapers)
JEL-codes: C11 C15 G1 (search for similar items in EconPapers)
Pages: 35
Date: 2003-09
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Journal Article: MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model (2004)
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