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CVaR-based risk parity model with machine learning

Author

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  • Sheng, Jiliang
  • Chen, Lanxi
  • Chen, Huan
  • An, Yunbi

Abstract

This study proposes a risk parity model based on conditional value-at-risk (CVaR), enhanced by integrating machine learning techniques into dynamic portfolio optimization. The CVaR-based risk parity (CVaR-RP) model allocates portfolio tail risk among assets evenly to mitigate downside risk. To enhance the CVaR-RP's predicting accuracy and adaptability to changing market conditions, we use a two-stage training approach within machine learning algorithms to forecast asset price movements. Portfolios are dynamically rebalanced based on these predictions to optimize the trade-off between risk mitigation and return maximization. Numerical analysis shows that the CVaR-RP strategy outperforms volatility-based risk parity and equal-weight strategies. Specifically, with machine learning-driven predictions and dynamic weight adjustments, the CVaR-RP achieves a higher Sharpe ratio, reduced maximum drawdown, and improved Calmar ratio. This research highlights the effectiveness of integrating machine learning methods into CVaR-RP strategies in enhancing returns and mitigating downside risk.

Suggested Citation

  • Sheng, Jiliang & Chen, Lanxi & Chen, Huan & An, Yunbi, 2025. "CVaR-based risk parity model with machine learning," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:pacfin:v:93:y:2025:i:c:s0927538x25001945
    DOI: 10.1016/j.pacfin.2025.102857
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    References listed on IDEAS

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    2. Ming Che Lee, 2025. "A Hybrid EGARCH–Informer Model with Consistent Risk Calibration for Volatility and CVaR Forecasting," Mathematics, MDPI, vol. 13(19), pages 1-22, September.

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