基于藤状协程的情景树生成方法,用于优化投资组合

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-03-05 DOI:10.1002/for.3112
Xiaolei He, Weiguo Zhang
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引用次数: 0

摘要

本文提出了一种有效的启发式方法,用于为投资组合选择问题生成多阶段情景树。在两种或两种以上风险资产的情况下,投资者需要考虑不同资产之间复杂的多元依赖关系。这种依赖模式不仅表现为非对称和肥尾,还表现为时变,而且上尾和下尾对投资组合管理有不同的影响。本文设计了一种新的情景生成方法,将 GARCH 型模型和藤蔓 copula 模型相结合,以灵活的方式正确反映多种资产中这些复杂的依赖模式。同时利用模拟和聚类方法,从该模型中依次生成多阶段情景树。情景树的节点概率是通过求解改进的矩匹配模型确定的,该模型的目标是保持原始分布的中心矩和低尾。然后,在多阶段投资组合选择模型中对生成的情景树进行测试。实验结果证明,与其他现有模型或方法相比,我们提出的情景生成方法既高效又有优势,而且矩匹配对我们的方法有积极影响。
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Vine copula-based scenario tree generation approaches for portfolio optimization

This paper presents an efficient heuristic to generate multi-stage scenario trees for portfolio selection problems. In the case of two or more risky assets, investors need to account for the complex multivariate dependence among different assets. The dependence patterns have shown not only asymmetric and fat tails but also time-varying, and the upper and lower tails have different effect on portfolio management. In this paper, we design a new scenario generation method by combining the GARCH-type model and vine copula model to properly reflect these complex dependence patterns in multiple assets in a flexible way. A multi-stage scenario tree is generated sequentially from this model by simultaneously utilizing the simulation and clustering methods. The scenarios' nodal probabilities are determined by solving an improved moment matching model, whose objective is to maintain the central moments and lower tails of the original distribution. The resulting scenario trees are then tested on a multi-stage portfolio selection model. The experimental results prove the efficiency and advantages of our proposed scenario generation method over other existing models or methods and the positive influence of moment matching on our method.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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