Normal variance-mean mixtures (II) - a multivariate moment method

Werner Huerlimann

Abstract


A moment method for the multivariate variance-mean mixture model is considered. Besides mean and covariance the method uses the coskewness and cokurtosis tensors. Its algorithmic implementation depends upon the solution of a sextic equation. Explicit formulas for the normal inverse Gaussian, the gamma, the inverse gamma and the classical tempered stable mixing distributions are included. An application to the statistical estimation of bivariate stock market indices is given. The models are successfully fitted to seven bivariate daily data sets over different time periods. The goodness-of-fit of the margins are optimized and compared.


Full Text: PDF

How to Cite this Article:

Werner Huerlimann, Normal variance-mean mixtures (II) - a multivariate moment method, Journal of Mathematical and Computational Science, Vol 4, No 4 (2014), 763-796

Copyright © 2014 Werner Huerlimann. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

J. Math. Comput. Sci.

ISSN: 1927-5307

Editorial Office: jmcs@scik.org

 

Copyright ©2019 JMCS