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.

Recommended Citation

Nunzio Cappuccio, Diego Lubian, and Davide Raggi (2004) "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model", Studies in Nonlinear Dynamics & Econometrics: Vol. 8: No. 2, Article 6.
http://www.bepress.com/snde/vol8/iss2/art6

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