国君金工|BL模型本月表现亮眼,2024年收益已达4%

Quantitative Models and Construction Methods 1. Model Name: Black-Litterman (BL) Model - Model Construction Idea: The BL model integrates market equilibrium returns with subjective views to generate optimal portfolio weights for asset allocation[1] - Model Construction Process: The BL model combines the equilibrium returns (π) derived from the CAPM with investor views (Q) using the following formula: $ E(R) = \pi + \tau \cdot \Sigma \cdot (\Sigma + P^T \cdot \Omega^{-1} \cdot P)^{-1} \cdot (Q - P \cdot \pi) $ - $ E(R) $: Expected returns after incorporating views - $ \pi $: Equilibrium returns - $ \tau $: Scalar representing the uncertainty in the prior estimate of returns - $ \Sigma $: Covariance matrix of asset returns - $ P $: Matrix linking views to assets - $ \Omega $: Covariance matrix of the views - $ Q $: Vector of views on asset returns[1] - Model Evaluation: The BL model is effective in combining subjective views with market data, making it suitable for dynamic asset allocation[1] 2. Model Name: Risk Parity Model - Model Construction Idea: The risk parity model aims to allocate portfolio weights such that each asset contributes equally to the portfolio's overall risk[1] - Model Construction Process: The portfolio weights are determined by solving the following optimization problem: $ \min \sum_{i=1}^n \left( \frac{w_i \cdot \sigma_i}{\sum_{j=1}^n w_j \cdot \sigma_j} - \frac{1}{n} \right)^2 $ - $ w_i $: Weight of asset $ i $ - $ \sigma_i $: Volatility of asset $ i $ - $ n $: Number of assets in the portfolio This ensures that the risk contribution of each asset is equalized across the portfolio[1] - Model Evaluation: The risk parity model is robust in diversifying risk across assets, making it particularly useful in volatile markets[1] 3. Model Name: Macro Factor-Based Asset Allocation Model - Model Construction Idea: This model uses macroeconomic factors to predict asset returns and allocate weights accordingly[1] - Model Construction Process: - Identify key macroeconomic factors (e.g., GDP growth, inflation, interest rates) - Use regression or machine learning techniques to estimate the relationship between these factors and asset returns - Construct a portfolio by optimizing weights based on the predicted returns and risk constraints[1] - Model Evaluation: The macro factor-based model is effective in capturing the impact of economic conditions on asset performance, providing a forward-looking approach to allocation[1] --- Model Backtesting Results 1. Black-Litterman (BL) Strategy 1 - Cumulative Return: 4.23% (YTD), 0.64% (June)[2] - Maximum Drawdown: 0.78%[2] - Volatility: 1.18%[2] 2. Black-Litterman (BL) Strategy 2 - Cumulative Return: 3.96% (YTD), 0.58% (June)[2] - Maximum Drawdown: 0.65%[2] - Volatility: 1.09%[2] 3. Risk Parity Strategy - Cumulative Return: 4.07% (YTD), 0.3% (June)[2] - Maximum Drawdown: 0.23%[2] - Volatility: 0.82%[2] 4. Macro Factor-Based Strategy - Cumulative Return: 3.3% (YTD), 0.22% (June)[2] - Maximum Drawdown: 0.27%[2] - Volatility: 0.82%[2]