Some new bivariate IG and NIG-distributions for modelling covariate nancial returns
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.
|Date of creation:||08 Jan 2007|
|Date of revision:|
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- Anders Eriksson & Lars Forsberg & Eric Ghysels, 2004.
"Approximating the Probability Distribution of Functions of Random Variables: A New Approach,"
CIRANO Working Papers
- Eric Ghysels & Anders Eriksson Lars Forsberg, 2004. "Approximating the probability distribution of functions of random variables: A new approach," Econometric Society 2004 Far Eastern Meetings 503, Econometric Society.
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