CVaR-based risk parity model with machine learning

Jiliang Sheng, Lanxi Chen, Huan Chen, Yunbi An,
CVaR-based risk parity model with machine learning,
Pacific-Basin Finance Journal,
Volume 93,
2025,
102857,
ISSN 0927-538X,
https://doi.org/10.1016/j.pacfin.2025.102857.
(https://www.sciencedirect.com/science/article/pii/S0927538X25001945)
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.
Keywords: Portfolios; Machine learning; Risk parity strategy; Conditional value-at-risk