Constrained portfolio strategies in a regime-switching economy.

IF 1.5 Q3 BUSINESS, FINANCE Financial Markets and Portfolio Management Pub Date : 2023-01-01 DOI:10.1007/s11408-022-00414-x
Marcelo Lewin, Carlos Heitor Campani
{"title":"Constrained portfolio strategies in a regime-switching economy.","authors":"Marcelo Lewin,&nbsp;Carlos Heitor Campani","doi":"10.1007/s11408-022-00414-x","DOIUrl":null,"url":null,"abstract":"<p><p>We implement an allocation strategy through a regime-switching model using recursive utility preferences in an out-of-sample exercise accounting for transaction costs. We study portfolios turnover and leverage, proposing two procedures to constrain the allocation strategies: a low-turnover control (LoT) and a maximum leverage control (MaxLev). LoT sets a dynamic threshold to trim minor rebalancing, reducing portfolio turnover, mitigating costs. MaxLev calculates dynamic adjustments to the risk aversion parameter to constrain the portfolio leverage. The MaxLev adjustments depend on the risk aversion and permitted portfolio leverage, which enables optimal strategies considering the leverage constraints. The study uses US equity portfolios, and shows that, first, models with LoT result in superior return-to-risk measures than those without it when transaction costs increase. Second, considering transaction costs, the return-to-risk measures of the models using MaxLev closely match or exceed those from the corresponding unconstrained regime-switching benchmarks. Third, MaxLev returns have lower volatility and higher return-to-risk than conventional numerically constrained benchmarks. Fourth, the certainty equivalent returns indicate that models using MaxLev and LoT outperform both single-state models and unconstrained regime-switching models with statistical significance.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11408-022-00414-x.</p>","PeriodicalId":44895,"journal":{"name":"Financial Markets and Portfolio Management","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243879/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Financial Markets and Portfolio Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11408-022-00414-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0

Abstract

We implement an allocation strategy through a regime-switching model using recursive utility preferences in an out-of-sample exercise accounting for transaction costs. We study portfolios turnover and leverage, proposing two procedures to constrain the allocation strategies: a low-turnover control (LoT) and a maximum leverage control (MaxLev). LoT sets a dynamic threshold to trim minor rebalancing, reducing portfolio turnover, mitigating costs. MaxLev calculates dynamic adjustments to the risk aversion parameter to constrain the portfolio leverage. The MaxLev adjustments depend on the risk aversion and permitted portfolio leverage, which enables optimal strategies considering the leverage constraints. The study uses US equity portfolios, and shows that, first, models with LoT result in superior return-to-risk measures than those without it when transaction costs increase. Second, considering transaction costs, the return-to-risk measures of the models using MaxLev closely match or exceed those from the corresponding unconstrained regime-switching benchmarks. Third, MaxLev returns have lower volatility and higher return-to-risk than conventional numerically constrained benchmarks. Fourth, the certainty equivalent returns indicate that models using MaxLev and LoT outperform both single-state models and unconstrained regime-switching models with statistical significance.

Supplementary information: The online version contains supplementary material available at 10.1007/s11408-022-00414-x.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
制度转换经济中的约束投资组合策略。
我们通过使用递归效用偏好的制度切换模型实现分配策略,该模型用于计算交易成本的样本外练习。我们研究了投资组合的周转率和杠杆率,提出了两种约束配置策略的程序:低周转率控制(LoT)和最大杠杆控制(MaxLev)。LoT设置了一个动态阈值,以减少轻微的再平衡,减少投资组合周转率,降低成本。MaxLev计算对风险厌恶参数的动态调整,以约束投资组合杠杆。MaxLev调整取决于风险规避和允许的投资组合杠杆,这使得考虑杠杆约束的最优策略成为可能。该研究使用了美国股票投资组合,结果表明,首先,当交易成本增加时,有LoT的模型比没有LoT的模型产生了更好的风险回报。其次,考虑到交易成本,使用MaxLev的模型的风险回报指标接近或超过了相应的无约束制度切换基准。第三,与传统的数值约束基准相比,MaxLev回报率具有更低的波动性和更高的风险回报。第四,确定性等效回报表明,使用MaxLev和LoT的模型优于单状态模型和无约束状态切换模型,且具有统计显著性。补充信息:在线版本包含补充资料,提供地址为10.1007/s11408-022-00414-x。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.20
自引率
0.00%
发文量
21
期刊介绍: The journal Financial Markets and Portfolio Management invites submissions of original research articles in all areas of finance, especially in – but not limited to – financial markets, portfolio choice and wealth management, asset pricing, risk management, and regulation. Its principal objective is to publish high-quality articles of innovative research and practical application. The readers of Financial Markets and Portfolio Management are academics and professionals in finance and economics, especially in the areas of asset management. FMPM publishes academic and applied research articles, shorter ''Perspectives'' and survey articles on current topics of interest to the financial community, as well as book reviews. All article submissions are subject to a double-blind peer review. http://www.fmpm.org Officially cited as: Financ Mark Portf Manag
期刊最新文献
Can machine learning make technical analysis work? Politically connected outside directors and market reaction: evidence from Korea Herding the crowds: how sentiment affects crowdsourced earnings estimates Report of the editor 2023 A simple test of misspecification for linear asset pricing models
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1