Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration

TL;DR

This paper introduces a Bayesian high-dimensional cointegration method to construct low volatility stock portfolios, effectively identifying key relationships that persist over time and aid in risk management.

Contribution

It presents a novel Bayesian framework for high-dimensional cointegration estimation, improving portfolio volatility reduction and risk management strategies.

Findings

Cointegration relationships remain stable out-of-sample.

Portfolios with identified cointegration relationships exhibit lower volatility.

Including cointegrated portfolios enhances risk management.

Abstract

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.