A Parameterized Method for Optimal Multi-Period Mean-Variance Portfolio Selection with Liability

Xun Li (), Zhongfei Li (), Xianping Wu () and Haixiang Yao ()
Additional contact information
Xun Li: The Hong Kong Polytechnic University
Zhongfei Li: Sun Yat-Sen Business School, Sun Yat-Sen University
Xianping Wu: South China Normal University
Haixiang Yao: Guangdong University of Foreign Studies

Chapter Chapter 9 in Optimization and Control for Systems in the Big-Data Era, 2017, pp 147-166 from Springer

Abstract: Abstract Big data is being generated by everything around us at all times. The massive amount and corresponding data of assets in the financial market naturally form a big data set. In this paper, we tackle the multi-period mean-variance portfolio of asset-liability management using the parameterized method addressed in Li et al. (SIAM J. Control Optim. 40:1540–1555, 2002) and the state variable transformation technique. By this simple yet efficient method, we derive the analytical optimal strategies and efficient frontiers accurately. A numerical example is presented to shed light on the results established in this work.

Keywords: Multi-period portfolio; Mean-variance formulation; Asset-liability management (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-319-53518-0_9

Ordering information: This item can be ordered from
http://www.springer.com/9783319535180

DOI: 10.1007/978-3-319-53518-0_9

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().