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Inverse Gaussian quadrature and finite normal-mixture approximation of the generalized hyperbolic distribution

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  • Jaehyuk Choi
  • Yeda Du
  • Qingshuo Song

Abstract

In this study, a numerical quadrature for the generalized inverse Gaussian distribution is derived from the Gauss-Hermite quadrature by exploiting its relationship with the normal distribution. The proposed quadrature is not Gaussian, but it exactly integrates the polynomials of both positive and negative orders. Using the quadrature, the generalized hyperbolic distribution is efficiently approximated as a finite normal variance-mean mixture. Therefore, the expectations under the distribution, such as cumulative distribution function and European option price, are accurately computed as weighted sums of those under normal distributions. The generalized hyperbolic random variates are also sampled in a straightforward manner. The accuracy of the methods is illustrated with numerical examples.

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  • Jaehyuk Choi & Yeda Du & Qingshuo Song, 2018. "Inverse Gaussian quadrature and finite normal-mixture approximation of the generalized hyperbolic distribution," Papers 1810.01116, arXiv.org, revised Dec 2020.
    • Handle: RePEc:arx:papers:1810.01116
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    References listed on IDEAS

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    1. Karlis, Dimitris, 2002. "An EM type algorithm for maximum likelihood estimation of the normal-inverse Gaussian distribution," Statistics & Probability Letters, Elsevier, vol. 57(1), pages 43-52, March.
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