Commodity Futures Characteristics and Asset Pricing Models
基于工具变量主成分分析方法的潜在因子模型在解释商品期货收益的截面差异上优于现有模型，发现特征与收益的关系主要由风险补偿驱动，而非错误定价。
ABSTRACT A latent‐factor model based on the instrumented principal component analysis (IPCA) methodology of Kelly et al. outperforms existing factor models in explaining cross‐sectional variations in commodity futures returns. The model allows for observed commodity futures characteristics to work as instruments for unobservable dynamic factor loadings. We find that the relationship between characteristics and commodity futures returns is driven by compensation for exposure to latent risk factors (beta) rather than compensation for exposure to mispricing (alpha). Three latent factors deliver more powerful explanations than any number of observable factors. Among a collection of 20 characteristics, only three are significantly related to latent factor betas. These three characteristics are momentum, expected shortfall, and idiosyncratic volatility.