Simulating a Generalized Gaussian Noise with Shape Parameter 1/2

This contribution deals with Monte Carlo simulation techniques for generalized Gaussian random variables. Such a parametric family of distributions has been proposed in many applications in science to describe physical phenomena and in engineering, and it seems interesting also in modeling economic and financial data. For low values of the shape parameter α, the distribution presents heavy tails. In particular, the choice α = 1/2 is considered. For such a value of the shape parameter, different Monte Carlo simulation techniques are assessed