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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

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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...

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Published in: Applied Mathematics and Computation
ISSN: 0096-3003
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa61962
first_indexed 2022-11-24T10:47:32Z
last_indexed 2024-11-14T12:20:04Z
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spelling 2023-06-05T10:47:45.1628370 v2 61962 2022-11-21 Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2022-11-21 ACEM 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. Journal Article Applied Mathematics and Computation 441 127700 Elsevier BV 0096-3003 Neural networks; Fuzzy logic system; Portfolio insurance; Time-varying linear programming; Portfolio optimization 15 3 2023 2023-03-15 10.1016/j.amc.2022.127700 http://dx.doi.org/10.1016/j.amc.2022.127700 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 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). 2023-06-05T10:47:45.1628370 2022-11-21T09:41:16.0969315 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Vasilios N. Katsikis 0000-0002-8208-9656 1 Spyridon D. Mourtas 0000-0002-8299-9916 2 Predrag S. Stanimirović 3 Shuai Li 0000-0001-8316-5289 4 Xinwei Cao 5
title Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN
spellingShingle Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN
Shuai Li
title_short Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN
title_full Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN
title_fullStr Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN
title_full_unstemmed Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN
title_sort Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Vasilios N. Katsikis
Spyridon D. Mourtas
Predrag S. Stanimirović
Shuai Li
Xinwei Cao
format Journal article
container_title Applied Mathematics and Computation
container_volume 441
container_start_page 127700
publishDate 2023
institution Swansea University
issn 0096-3003
doi_str_mv 10.1016/j.amc.2022.127700
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
url http://dx.doi.org/10.1016/j.amc.2022.127700
document_store_str 0
active_str 0
description 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.
published_date 2023-03-15T05:29:18Z
_version_ 1822288100765728768
score 11.048453

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