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Abstract
The behavior of agricultural commodity markets can arguably result in markedly asymmetric
price cycles, that is, downward cycles of substantially different length and breadth than
upward cycles. This study assesses whether asymmetric-cycle models can enhance the understanding
of the dynamics and provide for a better forecasting of U.S. soybeans and
Brazilian coffee prices. The forecasts from asymmetric cycle models are found to be substantially
mode precise than those obtained from standard autoregressive models. The
asymmetric cycle models also provide useful insights on the markedly different dynamics of
the upward versus the downward cycles exhibited by the prices of these two commodities.