No Cover Image

Journal article 527 views

Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN

Vasilios N. Katsikis Orcid Logo, Spyridon D. Mourtas Orcid Logo, Predrag S. Stanimirović, Shuai Li Orcid Logo, Xinwei Cao

Applied Mathematics and Computation, Volume: 441, Start page: 127700

Swansea University Author: Shuai Li Orcid Logo

Full text not available from this repository: check for access using links below.

Check full text

DOI (Published version): 10.1016/j.amc.2022.127700

Abstract

It is well known that minimum-cost portfolio insurance (MPI) is an essential investment strategy. This article presents a time-varying version of the original static MPI problem, which is thus more realistic. Then, to solve it efficiently, we propose a powerful recurrent neural network called the li...

Full description

Published in: Applied Mathematics and Computation
ISSN: 0096-3003
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa61962
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: It is well known that minimum-cost portfolio insurance (MPI) is an essential investment strategy. This article presents a time-varying version of the original static MPI problem, which is thus more realistic. Then, to solve it efficiently, we propose a powerful recurrent neural network called the linear-variational-inequality primal-dual neural network (LVI-PDNN). By doing so, we overcome the drawbacks of the static approach and propose an online solution. In order to improve the performance of the standard LVI-PDNN model, an adaptive fuzzy-power LVI-PDNN (F-LVI-PDNN) model is also introduced and studied. This model combines the fuzzy control technique with LVI-PDNN. Numerical experiments and computer simulations confirm the F-LVI-PDNN model’s superiority over the LVI-PDNN model and show that our approach is a splendid option to accustomed MATLAB procedures.
Keywords: Neural networks; Fuzzy logic system; Portfolio insurance; Time-varying linear programming; Portfolio optimization
College: Faculty of Science and Engineering
Funders: Predrag Stanimirović is supported by Ministry of Education, Science and Technological Development, Republic of Serbia, Contract No. 451-03-68/2020-14/200124. Predrag Stanimirović is supported by the Science Fund of the Republic of Serbia, #GRANT No 7750185, Quantitative Automata Models: Fundamental Problems and Applications - QUAM. This work was supported by the Ministry of Science and Higher Education of the Russian Federation (Grant No. 075-15-2022-1121).
Start Page: 127700

Similar Items