An EM type algorithm for maximum likelihood estimation of the normal-inverse Gaussian distribution

Dimitris Karlis

Statistics & Probability Letters, 2002, vol. 57, issue 1, 43-52

Abstract: The Normal-Inverse Gaussian distribution arises as a Normal variance-mean mixture with an Inverse Gaussian mixing distribution. This article deals with Maximum Likelihood estimation of the parameters of the Normal-Inverse Gaussian distribution. Due to the complexity of the likelihood, direct maximization is difficult. An EM type algorithm is provided for the Maximum Likelihood estimation of the Normal-Inverse Gaussian distribution. This algorithm overcomes numerical difficulties occurring when standard numerical techniques are used. An application to a data set concerning the general index of the Athens Stock Exchange is given. Some operating characteristics of the algorithm are discussed.

Keywords: Scale; normal; mixtures; Financial; data; Hyperbolic; distributions; Heavy; tailed; distributions (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (36)

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