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Simulation techniques for generalized Gaussian densities

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Author Info
Martina Nardon () (Department of Applied Mathematics, University of Venice)
Paolo Pianca () (Department of Applied Mathematics, University of Venice)

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Abstract

This contribution deals with Monte Carlo simulation of 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 also useful in modeling economic and financial data. For values of the shape parameter a within a certain range, the distribution presents heavy tails. In particular, the cases a=1/3 and a=1/2 are considered. For such values of the shape parameter, different simulation methods are assessed.

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File URL: http://www.dma.unive.it/wpdma/2006wp145.pdf
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Publisher Info
Paper provided by Department of Applied Mathematics, University of Venice in its series Working Papers with number 145.

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Length: 18 pages
Date of creation: Nov 2006
Date of revision:
Handle: RePEc:vnm:wpaper:145

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Related research
Keywords: Generalized Gaussian density; heavy tails; transformations of rendom variables; Monte Carlo simulation; Lambert W function;

Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Econometric and Statistical Methods; Specific Distributions

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References listed on IDEAS
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  1. Olivier V. Pictet & Michel M. Dacorogna & Ulrich A. Muller, 1996. "Heavy tails in high-frequency financial data," Working Papers 1996-12-11, Olsen and Associates. [Downloadable!]
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This page was last updated on 2009-11-18.

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