  This paper aims at shedding light upon how transforming or detrending a
series can substantially impact predictions of mixed causal-noncausal (MAR)
models, namely dynamic processes that depend not only on their lags but also on
their leads. MAR models have been successfully implemented on commodity prices
as they allow to generate nonlinear features such as locally explosive episodes
(denoted here as bubbles) in a strictly stationary setting. We consider
multiple detrending methods and investigate, using Monte Carlo simulations, to
what extent they preserve the bubble patterns observed in the raw data. MAR
models relies on the dynamics observed in the series alone and does not require
economical background to construct a structural model, which can sometimes be
intricate to specify or which may lack parsimony. We investigate oil prices and
estimate probabilities of crashes before and during the first 2020 wave of the
COVID-19 pandemic. We consider three different mechanical detrending methods
and compare them to a detrending performed using the level of strategic
petroleum reserves.
