MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model

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

Studies in Nonlinear Dynamics & Econometrics, 2004, vol. 8, issue 2, 31

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.

Date: 2004
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DOI: 10.2202/1558-3708.1211

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