Analytical Optimal Strategy Research Articles

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

This paper is concerned with multi-period asset–liability mean–variance portfolio selection with an uncertain exit time. By employing the mean-field formulation to this problem which involves two-dimensional state variables, we derive the analytical optimal strategy and efficient frontier successfully. The corresponding sensitivity analysis and a real-life example shed light on influences of liability and uncertain exit time to the optimal investment strategy.

A mean-field formulation for multi-period asset-liability mean-variance portfolio selection with probability constraints

This paper is concerned with studying an optimal multi-period asset-liability mean-variance portfolio selection with probability constraints using mean-field formulation without embedding technique. We strictly derive its analytical optimal strategy and efficient frontier. Numerical examples shed light on efficiency and accuracy of our method when dealing with this class of multi-period non-separable mean-variance portfolio selection problems.

A Portfolio Approach for the Forest Asset Dynamic Management under Stochastic Timber Price and Growth

Considering the valuation of forest stands based on revenue from wood sales, concession policy (such as carbon subsidies) and associated costs, the paper focuses on the stochastic control model to study the forest asset dynamic management. The key contribution is to find the optimal dynamic strategy about harvesting quantity in the continual and multiple periods in conditions of stochastic commodity price and timber growth by using portfolio approach. Finally, an analytical optimal strategy is obtained to analyze the quantification relations through which some important conclusions about the optimal forest management can be drawn.

Dynamic portfolio optimization with risk control for absolute deviation model

In this paper, we present a new multiperiod portfolio selection with maximum absolute deviation model. The investor is assumed to seek an investment strategy to maximize his/her terminal wealth and minimize the risk. One typical feature is that the absolute deviation is employed as risk measure instead of classical mean variance method. Furthermore, risk control is considered in every period for the new model. An analytical optimal strategy is obtained in a closed form via dynamic programming method. Algorithm with some examples is also presented to illustrate the application of this model.

An Analytical Optimal Strategy of the Forest Asset Dynamic Management under Stochastic Timber Price and Growth: A Portfolio Approach

Considering the valuation of forest stands based on revenue from wood sales, concession policy (such as carbon sub- sidies) and associated costs, the paper focuses on the stochastic control model to study the forest asset dynamic manage- ment. The key contribution is to find the optimal dynamic strategy about harvesting quantity in the continual and multiple periods in conditions of stochastic commodity price and timber growth by using portfolio approach. Finally, an analytical optimal strategy is obtained to analyze the quantification relations through which some important conclusions about the optimal forest management can be drawn.

Multi-period portfolio selection for asset-liability management with uncertain investment horizon

It is often the case that some unexpected event may force an investor to terminate her investment and leave the market. We consider in this paper the mean-variance formulation of multi-period portfolio optimization for asset-liability management with an uncertain investment horizon. Under the assumption that exit time follows a given distribution, the problem under investigation with uncertain investment horizon can be translated into one with deterministic exit time. By making use of the embedding technique of Li and Ng (2000), we derive an analytical optimal strategy and an analytical expression of the mean-variance efficient frontier for the mean-variance formulation of the problem.