Читать книгу Asset Allocation - William Kinlaw, Mark P. Kritzman - Страница 18

 Some investors believe that optimization is hypersensitive to estimation error because, by construction, optimization overweights asset classes for which expected return is overestimated and risk is underestimated, and it underweights asset classes for which the opposite is true.

 We argue that optimization is not hypersensitive to estimation error for reasonably constrained portfolios.

 If asset classes are close substitutes for each other, it is true that their weights are likely to change substantially given small input errors, but because they are close substitutes, the correct and incorrect portfolios will have similar expected returns and risk.

 If asset classes are dissimilar from each other, small input errors will not cause significant changes to the correct allocations; thus, again the correct and incorrect portfolios will have similar expected returns and risk.

 Nevertheless, estimation error is an important challenge to optimization, and investors would be well served to explore ameliorative measures such as Bayesian shrinkage, resampling, and the use of stability-adjusted return distributions.