We apply the term-structure-of-risk-premia framework of Bryzgalova, Huang, and Julliard (2024) to a cross section of FX forwards and government bonds. Using a Bayesian model that extracts latent priced shocks from returns and traces their propagation into macroeconomic variables, we estimate horizon-specific risk premia for seven US macro and financial factors. Four of them bear statistically significant premia: CPI inflation, the Fed Funds Rate, the yield curve slope, and the US–Europe yield differential. In each case, premia are small at short horizons and grow with the investment horizon, consistent with priced shocks that markets absorb immediately but that map slowly into macro series. We derive a closed-form decomposition showing that the model-implied mimicking portfolio preserves the same asset-space direction across all horizons, changing only its leverage. The Bayesian implementation delivers coherent inference over the entire term structure and over portfolio weights in a setting where non-parametric alternatives would be underpowered.
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