白银短期风险或依然处于高位

Quantitative Models and Construction Methods 1. Model Name: Commodity Term Structure Model - Model Construction Idea: This model captures the contango and backwardation states of commodities by utilizing the roll yield factor. It dynamically goes long on commodities with high roll yields and short on those with low roll yields[23] - Model Construction Process: The model is based on the roll yield factor, which is calculated as: $ Roll Yield = \frac{F_{t,T} - S_t}{S_t} $ where $ F_{t,T} $ is the futures price at time $ t $ for maturity $ T $, and $ S_t $ is the spot price at time $ t $[23] The portfolio dynamically adjusts positions to go long on commodities with higher roll yields and short on those with lower roll yields[23] - Model Evaluation: The model effectively captures the term structure dynamics of commodities, providing a systematic approach to exploit roll yield opportunities[23] 2. Model Name: Commodity Time-Series Momentum Model - Model Construction Idea: This model identifies medium- to long-term trends in domestic commodities using multiple technical indicators. It dynamically goes long on assets with upward trends and short on those with downward trends[23] - Model Construction Process: The model uses technical indicators such as moving averages and momentum signals to identify trends. Positions are adjusted dynamically based on the direction of these trends[23] - Model Evaluation: The model is effective in capturing momentum effects in commodity markets, particularly in trending environments[23] 3. Model Name: Commodity Cross-Sectional Inventory Model - Model Construction Idea: This model captures changes in the fundamentals of domestic commodities using inventory factors. It dynamically goes long on assets with declining inventories and short on those with increasing inventories[23] - Model Construction Process: The inventory factor is calculated as: $ Inventory Factor = \frac{\Delta Inventory}{Average Inventory} $ where $ \Delta Inventory $ is the change in inventory levels, and $ Average Inventory $ is the average inventory over a specific period[23] Positions are adjusted dynamically based on the direction of inventory changes[23] - Model Evaluation: The model provides a systematic approach to exploit inventory-driven price movements, particularly in supply-constrained markets[23] 4. Model Name: Commodity Fusion Strategy - Model Construction Idea: This strategy combines the three sub-strategies (term structure, time-series momentum, and cross-sectional inventory) using an equal-weighted approach to achieve diversification and enhance returns[19][23] - Model Construction Process: The net value of the fusion strategy is calculated as: $ Net Value = \frac{1}{3} \times (Term Structure + Time-Series Momentum + Cross-Sectional Inventory) $ Each sub-strategy contributes equally to the overall portfolio[19][23] - Model Evaluation: The fusion strategy benefits from diversification, reducing the risk of relying on a single factor while maintaining robust performance across different market conditions[19][23] --- Model Backtesting Results 1. Commodity Term Structure Model - Two-Week Return: -0.42%[22] - Year-to-Date Return: 0.04%[25] 2. Commodity Time-Series Momentum Model - Two-Week Return: 1.79%[22] - Year-to-Date Return: 2.17%[30] 3. Commodity Cross-Sectional Inventory Model - Two-Week Return: -1.11%[22] - Year-to-Date Return: -2.15%[35] 4. Commodity Fusion Strategy - Two-Week Return: 0.09%[22] - Year-to-Date Return: 0.02%[19] --- Quantitative Factors and Construction Methods 1. Factor Name: Roll Yield Factor - Factor Construction Idea: Measures the profitability of rolling futures contracts, capturing the contango or backwardation state of the market[23] - Factor Construction Process: $ Roll Yield = \frac{F_{t,T} - S_t}{S_t} $ where $ F_{t,T} $ is the futures price at time $ t $ for maturity $ T $, and $ S_t $ is the spot price at time $ t $[23] 2. Factor Name: Momentum Factor - Factor Construction Idea: Identifies trends in commodity prices using technical indicators such as moving averages and momentum signals[23] - Factor Construction Process: The factor is derived from the slope of the moving average or the momentum signal over a specific period[23] 3. Factor Name: Inventory Factor - Factor Construction Idea: Captures changes in commodity fundamentals by analyzing inventory levels[23] - Factor Construction Process: $ Inventory Factor = \frac{\Delta Inventory}{Average Inventory} $ where $ \Delta Inventory $ is the change in inventory levels, and $ Average Inventory $ is the average inventory over a specific period[23] --- Factor Backtesting Results 1. Roll Yield Factor - Two-Week Return Contribution: Top contributors include zinc (0.12%), rapeseed oil (0.10%), and soybean oil (0.09%)[27][29] 2. Momentum Factor - Two-Week Return Contribution: Top contributors include zinc (0.43%), LPG (0.29%), and palm oil (0.28%)[30][33] 3. Inventory Factor - Two-Week Return Contribution: Top contributors include crude oil (0.57%), rubber (0.27%), and rapeseed oil (0.26%)[37][39]