Optimization of Asset Allocation and Liquidation Time in Investment Decisions with VaR as a Risk Measure

Chunhui Xu () and Yinyu Ye
Additional contact information
Chunhui Xu: Chiba Institute of Technology
Yinyu Ye: Stanford University

Computational Economics, 2024, vol. 64, issue 1, No 20, 577 pages

Abstract: Abstract Asset allocation and investment times are two correlated aspects of investments; however, most research on investment decisions focuses on asset allocation with investment times fixed. This study aims to provide methods to optimize both asset allocation and liquidation time. We use value at risk (VaR), the most widely used risk indicator in the financial industry, to measure the risk of an investment, and construct an investment decision model for risk-oriented investors, which is continuous-time VaR minimization. We adopt a statistical method to estimate VaR that avoids assumptions that are difficult to verify; the corresponding continuous-time VaR minimization model becomes unsolvable with conventional optimization methods. We first suggest an approach to solve the model by solving a sequence of discrete-time VaR minimization models. This approach leads to the optimal solution under some conditions but yields an approximation to the optimal in general. Solving more discrete-time VaR minimization models produces a better result but has an additional computational burden. To improve the efficiency of the optimality-seeking process, we propose a novel strategy to reduce computing load significantly. We examine the reliability of the strategy by conducting computation experiments with actual data from a stock market.

Keywords: Portfolio selection; Liquidating time; Value at risk; Optimization; Historical simulation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-023-10451-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:64:y:2024:i:1:d:10.1007_s10614-023-10451-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-023-10451-x

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().