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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
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first_indexed 2022-11-24T10:47:32Z
last_indexed 2023-04-18T03:22:04Z
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spelling 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 MECH 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 Mechanical Engineering COLLEGE CODE MECH 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 2023-03-15T10:47:44Z
_version_ 1767855582580047872
score 11.012678

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