Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation

IF 1.9 4区 经济学 Q2 ECONOMICS
Zhiping Chen, Bingbing Ji, Jia Liu, Yu Mei
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引用次数: 0

Abstract

To comprehensively reflect the heteroscedasticity, nonlinear dependence and heavy-tailed distributions of stock returns while reducing the huge cost of parameter estimation, we use the Fama-French three-factor model to describe stock returns and then model the factor dynamics by using the ARMA-GARCH and Student-t copula models. A factor-based scenario tree generation algorithm is thus proposed, and the corresponding multi-stage international portfolio selection model is constructed and its reformulation is derived. Different from the current literature, our proposed models can capture the dynamic dependence among international markets and the dynamics of exchange rates, and what’s more important, make it possible for the practical solution of large-scale multi-stage international portfolio selection problems. Considering three different objective functions and international investments in the USA, Japanese and European markets, we carry out a series of empirical studies to demonstrate the practicality and efficiency of the proposed factor-based scenario tree generation algorithm and multi-stage international portfolio selection models.

Abstract Image

利用基于因子的情景树生成技术进行多阶段国际投资组合选择
为了全面反映股票收益率的异方差性、非线性依赖性和重尾分布,同时降低参数估计的巨大成本,我们使用 Fama-French 三因子模型来描述股票收益率,然后使用 ARMA-GARCH 和 Student-t copula 模型来建立因子动态模型。因此,我们提出了一种基于因子的情景树生成算法,并构建了相应的多阶段国际投资组合选择模型及其重构推导。与现有文献不同的是,我们提出的模型能够捕捉国际市场间的动态依赖关系和汇率的动态变化,更重要的是,它使大规模多阶段国际投资组合选择问题的实际解决成为可能。考虑到三个不同的目标函数以及美国、日本和欧洲市场的国际投资,我们进行了一系列实证研究,以证明所提出的基于因子的情景树生成算法和多阶段国际投资组合选择模型的实用性和效率。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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