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Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration

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  • Parley R Yang
  • Alexander Y Shestopaloff

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.

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  • Parley R Yang & Alexander Y Shestopaloff, 2024. "Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration," Papers 2407.10175, arXiv.org.
    • Handle: RePEc:arx:papers:2407.10175
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    References listed on IDEAS

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    3. Liang, Chong & Schienle, Melanie, 2019. "Determination of vector error correction models in high dimensions," Journal of Econometrics, Elsevier, vol. 208(2), pages 418-441.
    4. Liu Ziyin & Kentaro Minami & Kentaro Imajo, 2021. "Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction," Papers 2106.04114, arXiv.org, revised Dec 2022.
    5. Gabriel Borrageiro, 2022. "Sequential asset ranking in nonstationary time series," Papers 2202.12186, arXiv.org, revised Oct 2022.
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