Regime-Aware Risk Parity: Conditioning the Covariance Matrix on Macroeconomic and Stock Market Regimes
研究了一种制度感知的风险平价策略，通过整合宏观经济数据和股市风险指标动态调整协方差矩阵，实现对不同制度下投资组合风险的更精准控制，并提升样本外表现。
The authors investigate a regime-aware risk parity strategy designed to stabilize portfolio risk across various regimes. Their method dynamically adjusts an industry-standard multi-asset portfolio to the current macroeconomic and stock market environment. Specifically, they integrate macroeconomic data and stock market risk indicators, including the S&P 500 implied volatility term structure, into an estimation of the covariance matrix that drives the risk parity strategy. This integration aims to enhance the robustness of regime predictions. The empirical results demonstrate that the ability to differentiate risk and return profiles leads to more precise control of ex post portfolio risk across all regimes. It also improves out-of-sample portfolio performance when macroeconomic and market conditionalities are incorporated into the original risk parity portfolio. These enhancements are observed to be robust across various definitions of conditionality, time periods, and asset classes.