Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation

Abstract

a b s t r a c tWe propose a simulation-based approach for solving the constrained dynamic meanvariance portfolio management problem. For this dynamic optimization problem, we first consider a sub-optimal strategy, called the multi-stage strategy, which can be utilized in a forward fashion. Then, based on this fast yet sub-optimal strategy, we propose a backward recursive programming approach to improve it. We design the backward recursion algorithm such that the result is guaranteed to converge to a solution, which is at least as good as the one generated by the multi-stage strategy. In our numerical tests, highly satisfactory asset allocations are obtained for dynamic portfolio management problems with realistic constraints on the control variables.

On pre-commitment aspects of a time-consistent strategy for a mean-variance investor

“…We also extended the numerical simulation-based algorithm, which was proposed in Cong and Oosterlee (2016b) for the pre-commitment mean-variance problem, to solving time-consistent mean-variance problems. With some modifications of the algorithm, we could utilize it to achieve highly satisfactory results for the time-consistent problem as well as for the hybrid problem.…”

Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem

“…In Cong and Oosterlee (2016), SGBM is implemented for solving the dynamic mean-variance portfolio management problem in a robust and efficient way. In this paper, we implement SGBM for the dynamic utility-based portfolio management problem.…”

Providing an Evolutionary Approach for Multi-objective Portfolio Optimization Problem by Using Evolutionary Algorithm Multi-objective NSGAⅡ

“…To solve the problem of portfolio optimization, various tools and algorithms proposed and can use and also includes classical optimization algorithms [1][2][3] as well as the smart optimization algorithms (meta-heuristic). Stock portfolio problem in recent decades has been favorable issue for many researchers in industrial engineering [4][5], computer [6] financial [7][8][9], operations research and almost solved as a classic problem with met heuristic algorithms such as genetic [10][11][12], particle swarm [13,14], colonies of bees [15], ant colony [16,17] and Memetic [18].…”

“…In this algorithm we created Pool mating of the parent population with the Choose the best solution random, Then we compared this problem with famous algorithm such as NRGA, -SPEAⅡ . The required parameters for the proposed algorithm and other algorithms are visible in the table (1)(2)(3) and The results of the comparisons provided in Table ( …”