Author
Listed:
- Zhiping Chen
(Xi’an Jiaotong University
Xi’an International Academy for Mathematics and Mathematical Technology)
- Bingbing Ji
(Xi’an Jiaotong University
Xi’an International Academy for Mathematics and Mathematical Technology)
- Jia Liu
(Xi’an Jiaotong University
Xi’an International Academy for Mathematics and Mathematical Technology)
- Yu Mei
(Xi’an Jiaotong University
Xi’an International Academy for Mathematics and Mathematical Technology)
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.
Suggested Citation
Zhiping Chen & Bingbing Ji & Jia Liu & Yu Mei, 2025.
"Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation,"
Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 35-75, July.
Handle:
RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10699-x
DOI: 10.1007/s10614-024-10699-x
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10699-x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.