跳至正文
返回文库全部文档

用最大权匹配选择协整配对

文章 arXiv papers · 作者: Khizar Qureshi et al.
阅读原文

总结

本文提出一种用于配对交易的投资组合构建方法，以限制所选配对之间的重叠。该方法将资产表示为图中的节点，将协整候选配对表示为带权边，边权反映协整强度。投资组合通过最大权匹配选出协整关系强的配对，同时确保每项资产只出现在一组配对中。这解决了仅依据协整选择配对的一项缺点：多个配对可能重复使用同一资产，从而集中风险。

作者报告了理论分析，以及使用 500 数据、样本期为 2017 至 2023 的实证研究。匹配投资组合的方差低于仅依据协整构建的基准组合；其总夏普比率为 1.23，基准组合为 0.48，市场为 0.59。据文中所述，该组合的换手相关交易成本也较低，单一资产风险较小。摘要未提供净绩效、交易成本假设，也未提供判断结果对样本和配对选择敏感度所需的细节。

核心观点

  • 该方法将资产建模为图节点，将协整配对建模为带权边。
  • 最大权匹配选出协整关系强的配对，并防止同一资产出现在多个配对中。
  • 作者报告称，与仅依据协整选择配对的基准相比，该方法构建的投资组合方差较低。
  • 在文中所述的标普 500研究中，匹配策略的总夏普比率为 1.23，换手相关成本较低。

标签

配对交易投资组合构建均值回归统计学美国市场
全文
# Pairs Trading Using a Novel Graphical Matching Approach


# Pairs Trading Using a Novel Graphical Matching Approach









Pairs trading, a strategy that capitalizes on price movements of asset pairs driven by similar factors, has gained significant popularity among traders. Common practice involves selecting highly cointegrated pairs to form a portfolio, which often leads to the inclusion of multiple pairs sharing common assets. This approach, while intuitive, inadvertently elevates portfolio variance and diminishes risk-adjusted returns by concentrating on a small number of highly cointegrated assets. Our study introduces an innovative pair selection method employing graphical matchings designed to tackle this challenge. We model all assets and their cointegration levels with a weighted graph, where edges signify pairs and their weights indicate the extent of cointegration. A portfolio of pairs is a subgraph of this graph. We construct a portfolio which is a maximum weighted matching of this graph to select pairs which have strong cointegration while simultaneously ensuring that there are no shared assets within any pair of pairs. This approach ensures each asset is included in just one pair, leading to a significantly lower variance in the matching-based portfolio compared to a baseline approach that selects pairs purely based on cointegration. Theoretical analysis and empirical testing using data from the S\&P 500 between 2017 and 2023, affirm the efficacy of our method. Notably, our matching-based strategy showcases a marked improvement in risk-adjusted performance, evidenced by a gross Sharpe ratio of 1.23, a significant enhancement over the baseline value of 0.48 and market value of 0.59. Additionally, our approach demonstrates reduced trading costs attributable to lower turnover, alongside minimized single asset risk due to a more diversified asset base.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。