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

  • Zhiping Chen,
  • Bingbing Ji,
  • Jia Liu,
  • Yu Mei

摘要

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.