A mean-field formulation for multi-period asset–liability mean–variance portfolio selection with an uncertain exit time

2018 ◽  
Vol 69 (4) ◽  
pp. 487-499 ◽  
Author(s):  
Xiangyu Cui ◽  
Xun Li ◽  
Xianping Wu ◽  
Lan Yi
Keyword(s):  
Portfolio Selection ◽  
Exit Time ◽  
Mean Field ◽  
Mean Variance Portfolio ◽  
Mean Variance

Mean-variance portfolio selection with an uncertain exit-time in a regime-switching market

2019 ◽  
Vol 53 (4) ◽  
pp. 1171-1186
Author(s):  
Reza Keykhaei
Keyword(s):  
Portfolio Selection ◽  
Regime Switching ◽  
Exit Time ◽  
Efficient Frontier ◽  
Optimal Investment ◽  
Homogeneous Markov Chain ◽  
Investment Strategies ◽  
Special Cases ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, we deal with multi-period mean-variance portfolio selection problems with an exogenous uncertain exit-time in a regime-switching market. The market is modelled by a non-homogeneous Markov chain in which the random returns of assets depend on the states of the market and investment time periods. Applying the Lagrange duality method, we derive explicit closed-form expressions for the optimal investment strategies and the efficient frontier. Also, we show that some known results in the literature can be obtained as special cases of our results. A numerical example is provided to illustrate the results.

scholarly journals Multi-Period Mean-Variance Portfolio Selection with Uncertain Time Horizon When Returns Are Serially Correlated

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Ling Zhang ◽  
Zhongfei Li
Keyword(s):  
Portfolio Selection ◽  
Optimal Strategy ◽  
Time Horizon ◽  
Optimization Problem ◽  
Exit Time ◽  
Efficient Frontier ◽  
Serial Correlations ◽  
Mean Variance Portfolio ◽  
Uncertain Time ◽  
Mean Variance

We study a multi-period mean-variance portfolio selection problem with an uncertain time horizon and serial correlations. Firstly, we embed the nonseparable multi-period optimization problem into a separable quadratic optimization problem with uncertain exit time by employing the embedding technique of Li and Ng (2000). Then we convert the later into an optimization problem with deterministic exit time. Finally, using the dynamic programming approach, we explicitly derive the optimal strategy and the efficient frontier for the dynamic mean-variance optimization problem. A numerical example with AR(1) return process is also presented, which shows that both the uncertainty of exit time and the serial correlations of returns have significant impacts on the optimal strategy and the efficient frontier.

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