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Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)

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

Applied Mathematics and Computation, Volume: 385, Start page: 125453

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.amc.2020.125453

Abstract

Portfolio insurance is a hedging strategy which is used to limit portfolio losses without having to sell off stock when stocks decline in value. Consequently, the minimization of the costs related to portfolio insurance is a very important investment strategy. On the one hand, a popular option to so...

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Published in: Applied Mathematics and Computation
ISSN: 0096-3003
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa54545
first_indexed 2020-06-25T19:06:47Z
last_indexed 2020-08-17T03:16:06Z
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spelling 2020-08-16T10:00:08.6298082 v2 54545 2020-06-25 Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS) 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-06-25 ACEM Portfolio insurance is a hedging strategy which is used to limit portfolio losses without having to sell off stock when stocks decline in value. Consequently, the minimization of the costs related to portfolio insurance is a very important investment strategy. On the one hand, a popular option to solve the static minimum-cost portfolio insurance problem is based on the use of linear programming (LP) methods. On the other hand, the static portfolio selection under transaction costs (PSTC) problem is usually approached by nonlinear programming (NLP) methods. In this article, we define and study the time-varying minimum-cost portfolio insurance under transaction costs (TV-MCPITC) problem in the form of a time-varying nonlinear programming (TV-NLP) problem. Using the Beetle Antennae Search (BAS) algorithm, we also provide an online solution to the static NLP problem. The online solution to a time-varying financial problem is a great technical analysis tool and along with fundamental analysis will enable the investors to make better decisions. To the best of our knowledge, an approach that incorporates modern meta-heuristic optimization techniques to provide a more realistic online solution to the TV-MCPITC problem is original. In this way, by presenting an online solution to a time-varying financial problem we highlight the limitations of static methods. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods. Journal Article Applied Mathematics and Computation 385 125453 Elsevier BV 0096-3003 Portfolio constrained optimization, Time-varying transaction costs, Time-varying nonlinear programming, Nature-inspired algorithms, Beetle search optimization 15 11 2020 2020-11-15 10.1016/j.amc.2020.125453 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2020-08-16T10:00:08.6298082 2020-06-25T13:20:59.7097892 Vasilios N. Katsikis 1 Spyridon D. Mourtas 2 Predrag S. Stanimirović 3 Shuai Li 0000-0001-8316-5289 4 Xinwei Cao 5 54545__17587__1030c3faaacc43198f0ecff0a1be938c.pdf 54545.pdf 2020-06-26T16:13:00.0588649 Output 9042883 application/pdf Accepted Manuscript true 2021-06-21T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). false eng
title Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)
spellingShingle Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)
Shuai Li
title_short Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)
title_full Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)
title_fullStr Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)
title_full_unstemmed Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)
title_sort Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)
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 385
container_start_page 125453
publishDate 2020
institution Swansea University
issn 0096-3003
doi_str_mv 10.1016/j.amc.2020.125453
publisher Elsevier BV
document_store_str 1
active_str 0
description Portfolio insurance is a hedging strategy which is used to limit portfolio losses without having to sell off stock when stocks decline in value. Consequently, the minimization of the costs related to portfolio insurance is a very important investment strategy. On the one hand, a popular option to solve the static minimum-cost portfolio insurance problem is based on the use of linear programming (LP) methods. On the other hand, the static portfolio selection under transaction costs (PSTC) problem is usually approached by nonlinear programming (NLP) methods. In this article, we define and study the time-varying minimum-cost portfolio insurance under transaction costs (TV-MCPITC) problem in the form of a time-varying nonlinear programming (TV-NLP) problem. Using the Beetle Antennae Search (BAS) algorithm, we also provide an online solution to the static NLP problem. The online solution to a time-varying financial problem is a great technical analysis tool and along with fundamental analysis will enable the investors to make better decisions. To the best of our knowledge, an approach that incorporates modern meta-heuristic optimization techniques to provide a more realistic online solution to the TV-MCPITC problem is original. In this way, by presenting an online solution to a time-varying financial problem we highlight the limitations of static methods. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods.
published_date 2020-11-15T04:58:27Z
_version_ 1821380189673750528
score 11.04748

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