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Multi-period investment decision problem based on time consistent generalized convex risk measure and extremum scenarios

机译:基于时间一致广义凸风险测度和极值情景的多周期投资决策问题

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Purpose-To help investors find an investment policy with strong competitiveness,the purpose of this paper is to construct a multi-period investment decision model with practicality and superior performance.Design/methodology/approach-The paper uses a suitable multi-period risk measure to construct a multi-period portfolio selection model,where target returns at intermediate periods and market frictions are taken into account simultaneously.An efficient scenario tree generation approach is proposed in order to transform the complex multi-period portfolio selection problem into a tractable one.Findings-Numerical results show the new scenario tree generation algorithms are stable and can further reduce the tree size.With the scenario tree generated by the new scenario tree generation approach,the optimal investment strategy obtained under the multi-period investment decision model has more superior performance and robustness than the corresponding optimal investment strategy obtained under the single period investment model or the multi-period investment model only paying attention to the terminal cash flow.Research limitations/implications-The new risk measure and multi-period investment decision models can stimulate readers to find even better models and to efficiently solve realistic multi-period portfolio selection problems.Practical implications-The empirical results show the superior performance and robustness of optimal investment strategy obtained with the new models.What's more important,the empirical analyses tell readers how different market frictions affect the performance of optimal portfolios,which can guide them to efficiently solve real multi-period investment decision problems in practice.Originality/value-The paper first derives the concrete structure of the time consistent generalized convex multi-period risk measure,then constructs a multi-period portfolio selection model based on the new multi-period risk measure,and proposes a new extremum scenario tree generation algorithm.The authors construct a realistic multi-period investment decision model.Furthermore,using the proposed scenario tree generation algorithm,the authors transform the established stochastic investment decision model into a deterministic optimization problem,which can provide optimal investment decisions with robustness and superior performance.
机译:目的-为了帮助投资者找到具有较强竞争力的投资政策,本文的目的是构建具有实用性和卓越性能的多期投资决策模型。设计/方法/方法-本文采用了一种合适的多期风险测度提出了一种同时考虑中期目标收益和市场摩擦的多时期投资组合选择模型。为了将复杂的多时期投资组合选择问题转化为易于处理的问题,提出了一种有效的情景树生成方法。结果表明,新的场景树生成算法是稳定的,并且可以进一步减小树的大小。通过使用新的场景树生成方法生成的场景树,在多周期投资决策模型下获得的最优投资策略更加优越。性能和稳健性要比相应的最佳投资策略获得的不确定性高r单期投资模型或多期投资模型仅关注终端现金流量。研究局限/含意-新的风险度量和多期投资决策模型可以激发读者寻找更好的模型并有效解决现实的多周期投资组合选择问题。实践意义-实证结果表明新模型所获得的最优投资策略具有出色的性能和鲁棒性。更重要的是,经验分析告诉读者不同的市场摩擦如何影响最优投资组合的绩效,原创性/价值-本文首先推导了时间一致广义凸多周期风险测度的具体结构,然后构造了基于时间的广义多周期风险选择模型。关于新的多期风险度量,并提出了新的极值情景树生成算法。作者构建了一个现实的多周期投资决策模型。此外,使用提出的场景树生成算法,作者将建立的随机投资决策模型转化为确定性优化问题,可以提供具有鲁棒性的最优投资决策。和卓越的性能。

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  • 来源
    《中国金融评论(英文版)》 |2014年第4期|360-384|共25页
  • 作者单位

    Department of Computing Science, School of Mathematics and Statistics,Xi'an Jiaotong University, Xi'an, China;

    School of Economics & Management,Guangxi Normal University, Guilin, China;

    Department of Computing Science, School of Mathematics and Statistics,Xi'an Jiaotong University, Xi'an, China;

    Department of Computing Science, School of Mathematics and Statistics,Xi'an Jiaotong University, Xi'an, China;

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