• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

多协整股票的统计套利

Statistical Arbitrage for Multiple Co-integrated Stocks.

作者信息

Li Thomas Nanfeng, Papanicolaou Andrew

机构信息

Department of Mathematics, New York University, 251 Mercer Street, New York, 10012 NY USA.

Department of Mathematics, North Carolina State University, 2311 Stinson Drive, Raleigh, 27695 NC USA.

出版信息

Appl Math Optim. 2022;86(1):12. doi: 10.1007/s00245-022-09838-3. Epub 2022 Jun 7.

DOI:10.1007/s00245-022-09838-3
PMID:35694046
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9171503/
Abstract

UNLABELLED

In this article, we analyse optimal statistical arbitrage strategies from stochastic control and optimisation problems for multiple co-integrated stocks with eigenportfolios being factors. Optimal portfolio weights are found by solving a Hamilton-Jacobi-Bellman (HJB) partial differential equation, which we solve for both an unconstrained portfolio and a portfolio constrained to be market neutral. Our analyses demonstrate sufficient conditions on the model parameters to ensure long-term stability of the HJB solutions and stable growth rates for the optimal portfolios. To gauge how these optimal portfolios behave in practice, we perform backtests on historical stock prices of the S&P 500 constituents from year 2000 through year 2021. These backtests suggest three key conclusions: that the proposed co-integrated model with eigenportfolios being factors can generate a large number of co-integrated stocks over a long time horizon, that the optimal portfolios are sensitive to parameter estimation, and that the statistical arbitrage strategies are more profitable in periods when overall market volatilities are high.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00245-022-09838-3.

摘要

未标注

在本文中，我们从随机控制和优化问题的角度分析了针对多个具有特征投资组合作为因子的协整股票的最优统计套利策略。通过求解汉密尔顿 - 雅可比 - 贝尔曼（HJB）偏微分方程来找到最优投资组合权重，我们分别针对无约束投资组合和被约束为市场中性的投资组合求解该方程。我们的分析展示了模型参数的充分条件，以确保HJB解的长期稳定性以及最优投资组合的稳定增长率。为了衡量这些最优投资组合在实际中的表现，我们对2000年至2021年标准普尔500指数成分股的历史股价进行了回测。这些回测得出三个关键结论：即所提出的以特征投资组合为因子的协整模型在较长时间范围内可以产生大量协整股票，最优投资组合对参数估计敏感，并且统计套利策略在整体市场波动率较高的时期更有利可图。

补充信息

在线版本包含可在10.1007/s00245-022-09838-3获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/036f88f1aa19/245_2022_9838_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/72ee49c92611/245_2022_9838_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/84c59fd1ae02/245_2022_9838_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/9569c15bf374/245_2022_9838_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/11bc5ab703b7/245_2022_9838_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/76bc1c2a754e/245_2022_9838_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/036f88f1aa19/245_2022_9838_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/72ee49c92611/245_2022_9838_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/84c59fd1ae02/245_2022_9838_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/9569c15bf374/245_2022_9838_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/11bc5ab703b7/245_2022_9838_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/76bc1c2a754e/245_2022_9838_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f3/9171503/036f88f1aa19/245_2022_9838_Fig6_HTML.jpg

相似文献

1
Statistical Arbitrage for Multiple Co-integrated Stocks.多协整股票的统计套利
Appl Math Optim. 2022;86(1):12. doi: 10.1007/s00245-022-09838-3. Epub 2022 Jun 7.
2
Dynamic Portfolio Strategy Using Clustering Approach.基于聚类方法的动态投资组合策略
PLoS One. 2017 Jan 27;12(1):e0169299. doi: 10.1371/journal.pone.0169299. eCollection 2017.
3
Optimal statistical arbitrage trading of Berkshire Hathaway stock and its replicating portfolio.伯克希尔·哈撒韦股票的最优统计套利交易及其复制投资组合。
PLoS One. 2021 Jan 15;16(1):e0244541. doi: 10.1371/journal.pone.0244541. eCollection 2021.
4
New trading strategy in investment and a new anomaly: A study of the hedge funds from emerging and developed markets.投资中的新交易策略与新异象：对新兴市场和发达市场对冲基金的研究。
Heliyon. 2023 Nov 26;9(12):e22486. doi: 10.1016/j.heliyon.2023.e22486. eCollection 2023 Dec.
5
Neural Network-Based Solutions for Stochastic Optimal Control Using Path Integrals.基于神经网络的路径积分随机最优控制解决方案。
IEEE Trans Neural Netw Learn Syst. 2017 Mar;28(3):534-545. doi: 10.1109/TNNLS.2016.2544787.
6
Big Data Challenges of High-Dimensional Continuous-Time Mean-Variance Portfolio Selection and a Remedy.高维连续时间均值方差投资组合选择的大数据挑战及应对。
Risk Anal. 2017 Aug;37(8):1532-1549. doi: 10.1111/risa.12801. Epub 2017 Mar 30.
7
Is a correlation-based investment strategy beneficial for long-term international portfolio investors?基于相关性的投资策略对长期国际投资组合投资者有益吗？
Financ Innov. 2023;9(1):64. doi: 10.1186/s40854-023-00471-9. Epub 2023 Mar 9.
8
Design of nonlinear optimal control for chaotic synchronization of coupled stochastic neural networks via Hamilton-Jacobi-Bellman equation.基于 Hamilton-Jacobi-Bellman 方程的耦合随机神经网络混沌同步的非线性最优控制设计。
Neural Netw. 2018 Mar;99:166-177. doi: 10.1016/j.neunet.2018.01.003. Epub 2018 Feb 7.
9
Fuzzy Portfolio Selection Using Stochastic Correlation.基于随机相关性的模糊投资组合选择
Comput Econ. 2023 Mar 3:1-17. doi: 10.1007/s10614-023-10371-w.
10
Profitability of Contrarian Strategies in the Chinese Stock Market.中国股票市场中反向投资策略的盈利能力。
PLoS One. 2015 Sep 14;10(9):e0137892. doi: 10.1371/journal.pone.0137892. eCollection 2015.