Many investment models in discrete or continuous-time settings boil down to
maximizing an objective of the quantile function of the decision variable. This
quantile optimization problem is known as the quantile formulation of the
original investment problem. Under certain monotonicity assumptions, several
schemes to solve such quantile optimization problems have been proposed in the
literature. In this paper, we propose a change-of-variable and relaxation
method to solve the quantile optimization problems without using the calculus
of variations or making any monotonicity assumptions. The method is
demonstrated through a portfolio choice problem under rank-dependent utility
theory (RDUT). We show that this problem is equivalent to a classical Merton's
portfolio choice problem under expected utility theory with the same utility
function but a different pricing kernel explicitly determined by the given
pricing kernel and probability weighting function. With this result, the
feasibility, well-posedness, attainability and uniqueness issues for the
portfolio choice problem under RDUT are solved. It is also shown that solving
functional optimization problems may reduce to solving probabilistic
optimization problems. The method is applicable to general models with
law-invariant preference measures including portfolio choice models under
cumulative prospect theory (CPT) or RDUT, Yaari's dual model, Lopes' SP/A
model, and optimal stopping models under CPT or RDUT.Comment: to appear in Mathematical Financ
