Abstract
When two variables are bivariate normally distributed, Stein's (1973, 1981) seminal lemma provides a convenient expression for the covariance of the first variable with a function of the second. The lemma has proven to be useful in various disciplines, including statistics, probability, decision theory and finance. In finance, however, asset returns do not always display symmetry but may exhibit skewness. This observation led Adcock (2007, 2010, 2014) to develop Stein's type lemmas for certain multivariate distributions that are consistent with Simaan's (1987, 1993) setting for asset returns. In this paper, we depart from Simaan's setting and develop a new Stein's type lemma in the setting of a mean–variance mixture model for returns. As a particular application, we show that expected utility maximizers select portfolios that are mean–variance–skewness efficient.
Original language | English |
---|---|
Pages (from-to) | 606-612 |
Number of pages | 7 |
Journal | European Journal of Operational Research |
Volume | 261 |
Issue number | 2 |
Early online date | 14 Mar 2017 |
DOIs | |
Publication status | Published - 1 Sep 2017 |
Keywords
- Decision analysis
- Mean–variance optimization
- Multivariate generalized hyperbolic distribution (MGH)
- Stein's lemma
- Utility function
ASJC Scopus subject areas
- Modelling and Simulation
- Management Science and Operations Research
- Information Systems and Management
Fingerprint Dive into the research topics of 'A stein type lemma for the multivariate generalized hyperbolic distribution'. Together they form a unique fingerprint.
Profiles
-
Jing Yao
- School of Mathematical & Computer Sciences - Assistant Professor
- School of Mathematical & Computer Sciences, Actuarial Mathematics & Statistics - Assistant Professor
Person: Academic (Research & Teaching)