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Simulation techniques for generalized Gaussian densities

Author

Listed:
  • Martina Nardon

    (Department of Applied Mathematics, University of Venice)

  • Paolo Pianca

    (Department of Applied Mathematics, University of Venice)

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.

Suggested Citation

  • Martina Nardon & Paolo Pianca, 2006. "Simulation techniques for generalized Gaussian densities," Working Papers 145, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    • Handle: RePEc:vnm:wpaper:145
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    File URL: http://virgo.unive.it/wpideas/storage/2006wp145.pdf
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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.
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    Cited by:

    1. Sebastiano Michele Zema, 2020. "Directed Acyclic Graph based Information Shares for Price Discovery," LEM Papers Series 2020/28, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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    More about this item

    Keywords

    Generalized Gaussian density; heavy tails; transformations of rendom variables; Monte Carlo simulation; Lambert W function;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

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