Title: INFERENCE FOR THE MULTIVARIATE NORMAL INVERSE GAUSSIAN MODEL
Abstract: Multivariate Normal Inverse Gaussian model is obtained as mean-variance mixture of multivariate Normal distribution. The resulting distribution, while having heavier tails, it also accommodates skewness something not common to many multivariate distributions. In the present paper we propose Maximum likelihood estimation for the multivariate Normal Inverse Gaussian model through an EM type algorithm. Properties of the distribution are also discussed. A financial application is also given to illustrate the proposed methodology.
Publication Year: 2004
Publication Date: 2004-01-01
Language: en
Type: article
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