- The BL model is an improvement of the traditional mean-variance optimization (MVO) model, developed by Fisher Black and Robert Litterman in 1990. It integrates Bayesian theory to combine subjective views with quantitative asset allocation models, optimizing asset weights based on investor forecasts of market returns. This model addresses MVO's sensitivity to expected returns and offers higher tolerance compared to purely subjective investment approaches, providing efficient asset allocation solutions[12][13] - The BL model was implemented for both global and domestic assets. For global assets, it utilized indices such as S&P 500, Hang Seng Index, and Nanhua Commodity Index. For domestic assets, it included indices like CSI 300, CSI 1000, and SHFE Gold. Two versions of BL models were developed for each market, focusing on equities, bonds, commodities, and gold[13][14] - The Risk Parity model, introduced by Bridgewater in 2005, aims to equalize risk contributions across asset classes in a portfolio. It calculates initial asset weights based on expected volatility and correlation, then optimizes deviations between actual and expected risk contributions to determine final weights[17][18] - The Risk Parity model was constructed in three steps: selecting appropriate underlying assets, calculating risk contributions of each asset to the portfolio, and solving optimization problems to determine asset weights. It was applied to both global and domestic assets, using indices like CSI 300, CSI 1000, and COMEX Gold for domestic assets, and S&P 500, Hang Seng Index, and Nanhua Commodity Index for global assets[19][21] - The macro factor-based asset allocation model incorporates six macro risks: growth, inflation, interest rates, credit, exchange rates, and liquidity. Using Factor Mimicking Portfolio methodology, high-frequency macro factors were constructed. The strategy involves calculating asset factor exposures, determining benchmark exposures, setting subjective factor deviations based on macro forecasts, and solving for asset weights to reflect macro risk judgments[23][26] - The macro factor-based model was applied to domestic assets, including indices like CSI 300, CSI 1000, and SHFE Gold. For example, in September 2025, subjective factor deviations were set as 0 for growth, inflation, interest rates, and credit, 1 for exchange rates, and 0 for liquidity, reflecting macroeconomic conditions at the time[25][27] - Domestic BL Model 1 achieved weekly returns of 0.1%, monthly returns of 0.38%, and annual returns of 3.97%, with annualized volatility of 2.23% and maximum drawdown of 1.31%[14][17] - Domestic BL Model 2 recorded weekly returns of -0.01%, monthly returns of 0.48%, and annual returns of 3.68%, with annualized volatility of 2.02% and maximum drawdown of 1.06%[14][17] - Global BL Model 1 delivered weekly returns of 0.54%, monthly returns of 0.03%, and annual returns of 1.02%, with annualized volatility of 2.04% and maximum drawdown of 1.64%[14][17] - Global BL Model 2 achieved weekly returns of 0.37%, monthly returns of 0.35%, and annual returns of 2.43%, with annualized volatility of 1.65% and maximum drawdown of 1.28%[14][17] - Domestic Risk Parity Model recorded weekly returns of 0.14%, monthly returns of 0.34%, and annual returns of 3.47%, with annualized volatility of 1.34% and maximum drawdown of 0.76%[21][22] - Global Risk Parity Model achieved weekly returns of 0.22%, monthly returns of 0.39%, and annual returns of 2.99%, with annualized volatility of 1.46% and maximum drawdown of 1.2%[21][22] - Macro Factor-Based Model delivered weekly returns of -0.25%, monthly returns of 0.73%, and annual returns of 4.29%, with annualized volatility of 1.54% and maximum drawdown of 0.64%[27][28]