Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation
Joint with: Javier Mencia, Bank of Spain
We show that the distribution of any portfolio whose components jointly follow a location-scale mixture of normals can be characterized solely by its mean, variance
and skewness. Under this distributional assumption, we derive the mean-variance skewness frontier in closed form, and show that it can be spanned by three funds.
For practical purposes, we derive a standardized distribution, provide analytical expressions for the log-likelihood score and explain how to evaluate the information matrix. Finally, we present an empirical application in which we obtain the mean variance-
skewness frontier generated by the ten Datastream US sectoral indices,and conduct spanning tests.
Keywords: Generalized Hyperbolic Distribution, Maximum Likelihood, Portfolio
Frontiers, Spanning Tests, Tail Dependence.
JEL: C52, C32, G11
For more information, contact Frank Schorfheide.