Discussion Papers, Department of Business and Management Science, Norwegian School of Economics (NHH)
Some new bivariate IG and NIG-distributions for modelling covariate nancial returns
Abstract: The univariate Normal Inverse Gaussian (NIG) distribution
is found useful for modelling financial return data exhibiting skewness and
fat tails. Multivariate versions exists, but may be impractical to
implement in finance. This work explores some possibilities with links to
the mixing representation of the NIG distribution by the IG-distribution.
We present two approaches for constructing bivariate NIG distribution that
take advantage of the correlation between the univariate latent
IG-variables that characterizes the marginal NIG-distribution. These are
readily available from the marginal estimation, either by maximum
likelihood via the EM-algorithm or by Bayesian estimation via Markov chain
Monte Carlo methods. A context for implementation in finance is given.
Keywords: Financial returns; bivariate distribution; NIG distribution; mixture representation; inverse Gaussian distribution; bivariate simulation; (follow links to similar papers)
JEL-Codes: C10; C11; C13; C15; C16; (follow links to similar papers)
29 pages, January 8, 2007
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