TY - JOUR
T1 - Survey on Multi-period Mean–Variance Portfolio Selection Model
AU - Cui, Xiang Yu
AU - Gao, Jian Jun
AU - Li, Xun
AU - Shi, Yun
N1 - Funding Information:
This work is partially supported by the National Natural Science Foundation of China (Nos. 71971132, 61573244, 71671106, 71971083 and 72171138), by the Key Program of National Natural Science Foundation of China (No. 71931004), by Shanghai Institute of International Finance and Economics, by Program for Innovative Research Team of Shanghai University of Finance and Economics and by the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE.
Publisher Copyright:
© 2022, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/4/5
Y1 - 2022/4/5
N2 - Due to the non-separability of the variance term, the dynamic mean–variance (MV) portfolio optimization problem is inherently difficult to solve by dynamic programming. Li and Ng (Math Finance 10(3):387–406, 2000) and Zhou and Li (Appl Math Optim 42(1):19–33, 2000) develop the pre-committed optimal policy for such a problem using the embedding method. Following this line of research, researchers have extensively studied the MV portfolio selection model through the inclusion of more practical investment constraints, realistic market assumptions and various financial applications. As the principle of optimality no longer holds, the pre-committed policy suffers from the time-inconsistent issue, i.e., the optimal policy computed at the intermediate time t is not consistent with the optimal policy calculated at any time before time t. The time inconsistency of the dynamic MV model has become an important yet challenging research topic. This paper mainly focuses on the multi-period mean–variance (MMV) portfolio optimization problem, reviews the essential extensions and highlights the critical development of time-consistent policies.
AB - Due to the non-separability of the variance term, the dynamic mean–variance (MV) portfolio optimization problem is inherently difficult to solve by dynamic programming. Li and Ng (Math Finance 10(3):387–406, 2000) and Zhou and Li (Appl Math Optim 42(1):19–33, 2000) develop the pre-committed optimal policy for such a problem using the embedding method. Following this line of research, researchers have extensively studied the MV portfolio selection model through the inclusion of more practical investment constraints, realistic market assumptions and various financial applications. As the principle of optimality no longer holds, the pre-committed policy suffers from the time-inconsistent issue, i.e., the optimal policy computed at the intermediate time t is not consistent with the optimal policy calculated at any time before time t. The time inconsistency of the dynamic MV model has become an important yet challenging research topic. This paper mainly focuses on the multi-period mean–variance (MMV) portfolio optimization problem, reviews the essential extensions and highlights the critical development of time-consistent policies.
KW - Investment constraints
KW - Multi-period mean–variance
KW - Time inconsistency
UR - http://www.scopus.com/inward/record.url?scp=85127620142&partnerID=8YFLogxK
U2 - 10.1007/s40305-022-00397-6
DO - 10.1007/s40305-022-00397-6
M3 - Journal article
AN - SCOPUS:85127620142
SN - 2194-668X
VL - 10
SP - 599
EP - 622
JO - Journal of the Operations Research Society of China
JF - Journal of the Operations Research Society of China
IS - 3
ER -