量化配置基础模型周报第17期：恒生指数领涨，BL策略1本月收益达到1%

Quantitative Models and Construction Methods 1. Model Name: Black-Litterman (BL) Model - Model Construction Idea: The BL model is an improvement over the traditional Mean-Variance Optimization (MVO) model. It integrates subjective views with quantitative models using Bayesian theory to optimize asset allocation weights. This approach addresses MVO's sensitivity to expected returns and provides a more robust solution for asset allocation[12]. - Model Construction Process: 1. The model starts with a prior distribution of expected returns based on market equilibrium. 2. Subjective views on asset returns are incorporated using Bayesian updating. 3. The posterior distribution is calculated, combining the prior and subjective views. 4. The final asset weights are derived by solving the optimization problem based on the posterior distribution[12][13]. - Model Evaluation: The BL model effectively reduces sensitivity to expected returns and offers higher fault tolerance compared to purely subjective investment strategies, making it a widely used and efficient asset allocation tool[12]. 2. Model Name: Risk Parity Model - Model Construction Idea: The Risk Parity model aims to equalize the risk contribution of each asset (or factor) in the portfolio. It is an improvement over the traditional MVO model and focuses on diversifying risk rather than capital allocation[17]. - Model Construction Process: 1. Select appropriate underlying assets. 2. Calculate each asset's risk contribution to the portfolio based on expected volatility and correlations. 3. Optimize the portfolio by minimizing the deviation between actual and target risk contributions, resulting in final asset weights[18]. - Model Evaluation: The Risk Parity model provides a balanced risk allocation across assets, making it suitable for achieving stable returns across economic cycles[17]. 3. Model Name: Macro Factor-Based Asset Allocation Model - Model Construction Idea: This model bridges macroeconomic research and asset allocation by constructing a framework based on six macroeconomic risks: growth, inflation, interest rates, credit, exchange rates, and liquidity. It translates macroeconomic views into actionable asset allocation strategies[21]. - Model Construction Process: 1. Construct high-frequency macro factors using the Factor Mimicking Portfolio method. 2. Calculate factor exposures for each asset at the end of each month. 3. Determine baseline factor exposures using a risk parity portfolio as the benchmark. 4. Adjust factor exposures based on subjective macroeconomic views and solve for the next month's asset weights[22][23]. - Model Evaluation: This model allows for the integration of subjective macroeconomic views into asset allocation, providing a flexible and systematic approach to reflect macroeconomic expectations[21]. --- Model Backtesting Results 1. Black-Litterman (BL) Model - Domestic Asset BL Model 1: Weekly return 0.47%, September return 1.1%, YTD return 6.48%, annualized volatility 1.7%, maximum drawdown 0.78%[14][16]. - Domestic Asset BL Model 2: Weekly return 0.28%, September return 0.79%, YTD return 5.7%, annualized volatility 1.47%, maximum drawdown 0.65%[14][16]. - Global Asset BL Model 1: Weekly return 0.29%, September return 1.05%, YTD return 6.93%, annualized volatility 1.96%, maximum drawdown 0.95%[14][16]. - Global Asset BL Model 2: Weekly return 0.32%, September return 0.84%, YTD return 5.82%, annualized volatility 1.49%, maximum drawdown 0.64%[14][16]. 2. Risk Parity Model - Domestic Asset Risk Parity Model: Weekly return 0.24%, September return 0.32%, YTD return 4.71%, annualized volatility 1.15%, maximum drawdown 0.37%[20][26]. - Global Asset Risk Parity Model: Weekly return 0.2%, September return 0.13%, YTD return 5.11%, annualized volatility 1.03%, maximum drawdown 0.31%[20][26]. 3. Macro Factor-Based Asset Allocation Model - Macro Factor-Based Model: Weekly return 0.25%, September return 0.32%, YTD return 4.14%, annualized volatility 1.24%, maximum drawdown 0.45%[25][26].