Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration

ArXiv ID: 2407.10175 “View on arXiv”

Authors: Unknown

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.

Keywords: Bayesian Cointegration, Portfolio Construction, High-Dimensional Estimation, Sparse Modeling, Risk Management, Equities

Complexity vs Empirical Score

• Math Complexity: 9.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper uses advanced high-dimensional Bayesian statistics and complex matrix decompositions (Spike-and-Slab Lasso, VECM) but also demonstrates out-of-sample testing on large stock universes (400-1000 stocks) with specific performance metrics like volatility reduction and Sharpe ratio.

  flowchart TD
    A["Research Goal: Construct Low Volatility Portfolio<br>from High-Dimensional Stock Data"] --> B["Data Inputs: Time-series<br>of N Stocks (High-Dimensional)"]
    B --> C["Methodology: Bayesian Sparse<br>Cointegration Estimation"]
    C --> D["Computational Process:<br>Posterior Sampling & Model Fitting"]
    D --> E["Key Outcome 1: Identified<br>Persistent Cointegration Relationships"]
    E --> F["Key Outcome 2: Constructed<br>Low Volatility Portfolio"]
    F --> G["Outcome: Reduced Volatility &<br>Effective Risk Management (Drawdown)"]