This paper addresses a continuous-time mean-variance portfolio selection model, formulated as a bicriteria optimization problem to maximize expected terminal return and minimize terminal wealth variance. The authors show that this non-standard problem can be embedded into a class of auxiliary stochastic linear-quadratic (LQ) problems, providing an effective framework for studying the mean-variance problem. This approach allows for the derivation of the efficient frontier in a closed form, addressing the lack of analytical solutions for multiperiod mean-variance efficient frontiers. The paper highlights the challenges in applying dynamic programming to objective functions involving nonlinear utility functions and discusses related studies on mean-variance hedging problems.This paper addresses a continuous-time mean-variance portfolio selection model, formulated as a bicriteria optimization problem to maximize expected terminal return and minimize terminal wealth variance. The authors show that this non-standard problem can be embedded into a class of auxiliary stochastic linear-quadratic (LQ) problems, providing an effective framework for studying the mean-variance problem. This approach allows for the derivation of the efficient frontier in a closed form, addressing the lack of analytical solutions for multiperiod mean-variance efficient frontiers. The paper highlights the challenges in applying dynamic programming to objective functions involving nonlinear utility functions and discusses related studies on mean-variance hedging problems.