Risk parity strategies are widely adopted in asset allocation for their risk-balanced advantage, yet they remain vulnerable to financial market uncertainty—a critical pain point for global investors amid volatile market conditions. Addressing dynamic market uncertainty remains a key challenge in optimizing risk parity performance. This study proposes a robust risk parity (RRP) asset allocation strategy. By integrating an adaptive perturbation mechanism and a robust covariance matrix estimation method within the traditional risk parity framework, the proposed strategy effectively addresses the impact of financial market uncertainty on investment portfolios. The RRP strategy dynamically adjusts the perturbation radius and employs the robustly estimated covariance matrix. This approach enhances portfolio resilience and stability while preserving the balanced allocation of risk across assets. Empirical analysis, based on data from the Chinese market spanning the period 2012 to 2024, compares the performance of the robust risk parity strategy against traditional risk parity (TRP), equal weight (EW), global minimum variance (GMV), global maximum return (MaxRet), and enhanced risk parity (ERP). Numerical results demonstrate that the RRP strategy outperforms the alternative models. Specifically, by incorporating GARCH volatility forecasting, market state identification, and factor-structured covariance estimation, the RRP strategy achieves higher returns and a superior Sharpe ratio, alongside lower volatility and maximum drawdown, resulting in an improved Calmar ratio. This strategy effectively enhances return robustness and reduces portfolio risk under uncertain market conditions, offering actionable insights for investors navigating global market volatility.
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