量化资产配置系列之三：宏观因子组合及股债相关性再探索

• The report references the Fama-MacBeth method to simulate macro risk factors, transforming the logic of configuring macro risks through asset allocation into a logic of configuring macro risks by configuring assets[1][12][18] - Real macro factor data uses forecast values of relevant monthly macro indicators or asset monthly returns (interest rates/credit), performing univariate time series regression with each asset to obtain risk loadings, and applying a half-life weighting to historical loadings to smooth out instability caused by asset volatility[1][18][22] - The macro factor risk is decomposed into underlying asset portfolios to construct a macro factor risk parity portfolio[1][18][22] - The optimization results of risk parity for macro factors show certain economic growth elasticity, with both returns and volatility higher than those based on asset risk parity[2][39] - The report also discusses the factors influencing stock-bond correlation, referencing AQR's approach, which decomposes stock-bond correlation into economic growth volatility, inflation volatility, and the correlation between economic growth and inflation[3][42][43] - The study finds that economic growth volatility negatively contributes to stock-bond correlation, while interest rate volatility positively contributes, and the correlation between economic growth and interest rates is a positive contributing variable in domestic asset research[3][42][48] - Adding the inflation level factor further improves the explanatory power, with domestic data showing that the inflation level is a significant positive variable for stock-bond correlation[3][48][51] - Using a three-year historical window to calculate the coefficients of each variable, the study combines real values and consensus forecast data to calculate the change in stock-bond correlation for the next month, showing that the estimated and predicted values have the same trend and consistent signs with the real values[3][48][54] Quantitative Models and Construction Methods 1. Model Name: Macro-Factor Mimicking - Construction Idea: Transform the logic of configuring macro risks through asset allocation into configuring macro risks by configuring assets[1][12][18] - Construction Process: - Use forecast values of relevant monthly macro indicators or asset monthly returns (interest rates/credit) - Perform univariate time series regression with each asset to obtain risk loadings - Apply a half-life weighting to historical loadings to smooth out instability caused by asset volatility - Decompose macro factor risk into underlying asset portfolios to construct a macro factor risk parity portfolio[1][18][22] - Formula: $r_{t}=\alpha_{t}+B\cdot f_{t}+\varepsilon_{t}$ $\Sigma=B\cdot F\cdot B^{T}+E$ $\sigma_{P}{}^{2}\ =\ w^{T}\cdot\Sigma\ \cdot w=\ (w^{T}\cdot B)\cdot F\cdot(B^{T}\cdot w)+w^{T}\cdot E\cdot w$ $\%\text{RC}\ =(w^{T}\cdot B)_{i}\cdot\frac{\partial\sigma_{P}}{\partial(w^{T}\cdot B)_{i}}/\sigma_{P}=\frac{(w^{T}\cdot B)_{i}\cdot(F\cdot(B^{T}\cdot w))_{i}}{w^{T}\cdot\Sigma\ \cdot w}$ where B is the time-series calculated risk loadings, f is the factor returns, Σ is the asset risk matrix, and F is the macro factor return risk matrix[23][24] - Evaluation: The optimization results of risk parity for macro factors show certain economic growth elasticity, with both returns and volatility higher than those based on asset risk parity[2][39] Model Backtest Results 1. Macro-Factor Mimicking Model: - Annualized Return: 9.86% (12-month half-life), 9.46% (no half-life)[29] - Annualized Volatility: 9.55% (12-month half-life), 9.44% (no half-life)[29] - Maximum Drawdown: -14.30% (12-month half-life), -15.20% (no half-life)[29] - 2016 Return: 37.24% (12-month half-life), 18.65% (no half-life)[29] - 2017 Return: 2.17% (12-month half-life), 7.29% (no half-life)[29] - 2018 Return: -5.02% (12-month half-life), -7.45% (no half-life)[29] - 2019 Return: 14.61% (12-month half-life), 14.23% (no half-life)[29] - 2020 Return: 12.20% (12-month half-life), 7.57% (no half-life)[29] - 2021 Return: 14.63% (12-month half-life), 10.27% (no half-life)[29] - 2022 Return: 0.36% (12-month half-life), 8.15% (no half-life)[29] - 2023 Return: 5.41% (12-month half-life), 3.68% (no half-life)[29] - 2024 Return: 6.83% (12-month half-life), 15.40% (no half-life)[29] - 2025.07.31 Return: 7.44% (12-month half-life), 11.53% (no half-life)[29]