Optimal Dynamic Portfolio Selection: Multiperiod Mean‐Variance Formulation

By Duan Li, W. Ng. 2000

1,060 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e678144fc7741a1a46f7aa85e9adb10f75aa93b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Peter A. Forsyth
2. Xun Yu Zhou
3. Xiang Lin
4. Yiping Qian
5. George Yin
6. Jian Wang
7. Kenneth R. Vetzal
8. Xun Li

In Management Science and Operations Research

1. Peter A. Forsyth
2. Xiang Lin
3. Yiping Qian
4. Xun Yu Zhou
5. Jian Wang
6. George Yin
7. Xun Li
8. Jiaqin Wei

Citations in the corpus

• Mean-Variance Policy for Discrete-time Cone Constrained Markets: The Consistency in Efficiency and Minimum-Variance Signed Supermartingale Measure (2014, positive): “The derived dynamic optimal investment policy in Li and Ng (2000) and Zhou and Li (2000) is termed by Basak and Chabakauri (2010) as pre-committed dynamic optimal investment policy, as the (adaptive) optimal policy is fixed at time 0 to achieve overall optimality for the entire investment horizon.”
• Time-consistent mean–variance asset–liability management with random coefficients (2017, methodology): “More recently, by embedding the original problem into a stochastic linear-quadratic (LQ) control problem, Li and Ng (2000) and Zhou and Li (2000) extend Markowitz’s work to a multi-period model and a continuoustime model, respectively.”
• BEHAVIORAL PORTFOLIO SELECTION IN CONTINUOUS TIME (2007, background): “The equity premium puzzle [Mehra and Prescott (1985)] refers to the phenomenon that observed average annual returns on stocks over the past century are higher by large margin (approximately 6 percentage points) than returns on government bonds, whereas standard asset allocation theories (such as tha”
• MEAN–VARIANCE PORTFOLIO OPTIMIZATION WITH STATE‐DEPENDENT RISK AVERSION (2014, methodology): “In a discrete-time setting, [11] developed an embedding technique to change the originally time-inconsistent MV problem into a stochastic LQ control problem.”
• BETTER THAN DYNAMIC MEAN‐VARIANCE: TIME INCONSISTENCY AND FREE CASH FLOW STREAM (2012, methodology): “Zhu, Li, and Wang (2003) later investigated the wealth reduction phenomena associated with the optimal multiperiod mean-variance policy derived by Li and Ng (2000).”
• Dynamic Mean-Variance Portfolio Selection with No-Shorting Constraints (2001, methodology): “Only recently have Li and Ng [20] faithfully extended Markowitz’s mean-variance model to the multiperiod setting by using the idea of embedding the problem in a tractable auxiliary problem.”
• Markowitz's Mean-Variance Portfolio Selection with Regime Switching: A Continuous-Time Model (2003, methodology): “By embedding the original (not readily solvable) problem into a tractable auxiliary problem, following a similar embedding technique introduced in Li and Ng [19] for the multiperiod model, it was shown that this auxiliary problem in fact is a stochastic optimal LQ problem and can be solved explicitl”
• Optimal time-consistent investment and reinsurance strategies for mean–variance insurers with state dependent risk aversion (2013, background): “Among others, Li and Ng (2000) developed an embedding technique to change the originally timeinconsistent mean–variance problem into a stochastic LQ control problem in a discrete-time setting.”
• A Geometric Approach To Multiperiod Mean Variance Optimization of Assets and Liabilities (2004, positive): “As a special case, the solution of the mean variance problem for the asset only case in Li and Ng (2000) follows directly and can be represented in terms of simple products of some single period orthogonal returns.”
• Continuous-time mean-variance efficiency: the 80% rule (2006, background): “) The dynamic extension of the Markowitz model, especially in continuous time, has been studied extensively in recent years; see, for example, [2, 6, 7, 8, 11, 12].”
• Numerical Methods for Nonlinear PDEs in Finance (2012, background): “This can be avoided with the help of the results in [18, 28]: Theorem 1 (Equivalent Linear Quadratic (LQ) problem).”
• Mean–variance, mean–VaR, and mean–CVaR models for portfolio selection with background risk (2018, background): “Li and Ng (2000) extend the single-period mean-variance portfolio selection model to multi-period and Li, et al. (2010) develop the mean-variance-skewness model for portfolio selection with fuzzy returns.”