  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.
