We employ a Bayesian modelling technique for high dimensional cointegration estimation to construct low volatility portfolios from a large number of stocks. The proposed Bayesian framework effectively identifies sparse and important cointegration relationships amongst large baskets of stocks across various asset spaces, resulting in portfolios with reduced volatility. Such cointegration relationships persist well over the out-of-sample testing time, providing practical benefits in portfolio construction and optimization. Further studies on drawdown and volatility minimization also highlight the benefits of including cointegrated portfolios as risk management instruments.

翻译：本文采用高维协整估计的贝叶斯建模技术，从大量股票中构建低波动率投资组合。所提出的贝叶斯框架能有效识别跨资产类别的大规模股票组合间稀疏且重要的协整关系，从而构建出波动率显著降低的投资组合。此类协整关系在样本外测试期间表现出良好的持续性，为投资组合构建与优化提供了实际价值。对回撤控制和波动率最小化的进一步研究也表明，将协整投资组合纳入风险管理工具具有显著优势。