We investigate the importance of time-varying discount rates for commodity prices using an index based on twenty-three commodities for the period 1959–2024. We show that in commodities markets, unlike other financial markets, time variation in discount rates plays a much smaller role. Instead, prices forecast cash flows as well as discount rates. A high price for a commodity today, measured as a low percentage net convenience yield, forecasts both a high future convenience yield and a low expected return. For longer horizons, variation in percentage net convenience yields seems mainly driven by net convenience yield growth, making commodities much closer to the classical textbook view of price changes representing news about cash flows.

This paper shows that belief differences have strong effects on asset prices in consumption-based asset-pricing models with long-run risks. Belief heterogeneity leads to time-varying consumption and wealth shares of the agents. This time variation can resolve several asset-pricing puzzles, including the large countercyclical variation of expected risk premia, the volatility of the price-dividend ratio, the predictability of cash flows and returns, and the large predictability of returns in recessions. These findings show that belief differences, a widely observed attribute of investors, significantly improve the explanatory power of long-run risk asset-pricing models.

Modern asset pricing models combine recursive preferences with complex dynamics for the underlying consumption process. The existence of solutions is for many of these models an unsettled question. This paper introduces a novel technique to prove existence and nonexistence, as well as uniqueness for models with recursive preferences. The approach applies to many models of interest, including those with long-run consumption risks, with stochastic volatility and jumps, with time-varying consumption disasters, and with smooth ambiguity aversion and learning. Collectively, the proven results settle the existence question for many of today’s leading asset pricing models.

ABSTRACT This paper shows that the latest generation of asset pricing models with long‐run risk exhibit economically significant nonlinearities, and thus the ubiquitous Campbell‐Shiller log‐linearization can generate large numerical errors. These errors translate in turn to considerable errors in the model predictions, for example, for the magnitude of the equity premium or return predictability. We demonstrate that these nonlinearities arise from the presence of multiple highly persistent processes, which cause the exogenous states to attain values far away from their long‐run means with nonnegligible probability. These extreme values have a significant impact on asset price dynamics.