We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The ...We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function.Crucially,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level.This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential.Our proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal strategy.We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior.Finally,we discuss applications of this framework to robo-advisors.展开更多
基金supported by the National Natural Science Foundation of China(Nos.71671106 and 72171138)by the Shanghai Institute of International Finance and Economics,and by the Program for Innovative Research Team of Shanghai University of Finance and Economics(No.2020110930)+1 种基金partially supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(No.CityU 11200219)partially supported by the National Natural Science Foundation of China(No.72050410356).
文摘We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function.Crucially,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level.This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential.Our proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal strategy.We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior.Finally,we discuss applications of this framework to robo-advisors.
