Abstract
Hedge funds have return peculiarities not commonly associated with traditional investment vehicles. They are more inclined to produce return distributions with significantly non–normal skewness and kurtosis. Investor preferences may be better represented by bilinear utility functions or S–shaped value functions than by neoclassical utility functions, and mean–variance optimization is thus not appropriate for forming portfolios that include hedge funds. Portfolios of hedge funds formed using both mean–variance and full–scale optimization, given a wide range of assumptions about investor preferences, reveal that higher moments of hedge funds do not meaningfully compromise the efficacy of mean–variance optimization if investors have power utility; mean–variance optimization is not particularly effective for identifying optimal hedge fund allocations if preferences are bilinear or S–shaped; and, contrary to conventional wisdom, investors with S–shaped preferences are attracted to kurtosis as well as negative skewness.
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