Semimartingale theory of monotone mean--variance portfolio allocation
We study dynamic optimal portfolio allocation for monotone mean--variance preferences in a general semimartingale model. Armed with new results in this area we revisit the work of Cui, Li, Wang and Zhu (2012, MAFI) and fully characterize the circumstances under which one can set aside a non-negative cash flow while simultaneously improving the mean--variance efficiency of the left-over wealth. The paper analyzes, for the first time, the monotone hull of the Sharpe ratio and highlights its relevance to the problem at hand.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The Hansen ratio in mean--variance portfolio theory
It is shown that the ratio between the mean and the $L^2$-norm leads to a particularly parsimonious description of the mean-variance efficient frontier and the dual pricing kernel restrictions known as the Hansen-Jaganna…
Power Forward Performance in Semimartingale Markets with Stochastic Integrated Factors
We study the forward investment performance process (FIPP) in an incomplete semimartingale market model with closed and convex portfolio constraints, when the investor's risk preferences are of the power form. We provide…
TripletBeating the Market with Generalized Generating Portfolios
Stochastic portfolio theory aims at finding relative arbitrages, i.e. trading strategies which outperform the market with probability one. Functionally generated portfolios, which are deterministic functions of the marke…
Signature Methods in Stochastic Portfolio Theory
In the context of stochastic portfolio theory we introduce a novel class of portfolios which we call linear path-functional portfolios. These are portfolios which are determined by certain transformations of linear funct…
Dynamically optimal portfolios for monotone mean--variance preferences
Monotone mean-variance (MMV) utility is the minimal modification of the classical Markowitz utility that respects rational ordering of investment opportunities. This paper provides, for the first time, a complete charact…