Predicting crashes in oil prices during the COVID-19 pandemic with mixed causal-noncausal models

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titlepage\affil[ ]{Maastricht University} \begin{abstract} 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.\\ Keywords: { noncausal models, detrending, forecasting, predictive densities, bubbles, crashes, simulations-based forecasts, Hodrick-Prescott filter, COVID-19 pandemic\\ JEL: C22 , C53} \end{abstract}

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\import{./}{1-Introduction.tex} \import{./}{2-Discussion.tex} \import{./}{3-Montecarlo_MAR.tex} \import{./}{4-Empirics.tex}

Conclusion

This paper aims at shedding light upon how transforming or detrending a series can substantially impact predictions of mixed causal-noncausal models. Assuming a polynomial trend of order 4 for WTI and Brent series probably alters the dynamics in the remaining cycle. The HP filter (with penalizing parameter $\lambda=129\,600$) does not require any further assumptions with respect to the trend and can therefore be an adequate filter in cases where the trend is unknown. Knowing the actual trend or using exogenous variables for it is also not straightforward. We use US crude oil strategic petroleum reserves (SPR) to detrend oil price series to illustrate this option. We show that by detrending with SPR we obtain similar results to the $HP$ and polynomial trend of order 6 detrending. However, detrending with a variable that has seasonality or dynamics will alter the dynamics left in the cycle. Overall, caution is needed when detrending a series, and some filtering such as polynomial trends may require additional understanding regarding the deviations of the series from its fundamental trend. Nonetheless, once the series is detrended, resulting in a stationary series, using MAR models is a straightforward approach to model nonlinear time series. They capture the locally explosive episodes observed in oil prices in a strictly stationary setting. While the bi-modality of the predictive density would not be detected with standard Gaussian ARMA models, it could be detected with complex nonlinear models, but such model lacks the parsimonious characteristic of MAR models. The data-driven prediction methods may lack theoretical grounds but provide valuable information based on the estimated model and on past behaviors of the series in a parsimonious way. This paper focuses on one-step ahead predictions of decrease in crude oil prices during the first wave of the COVID-19 pandemic. \\

Acknowledgments

The authors would like to thank Francesco Giancaterini, an anonymous referee and the editors for valuable comments and suggestions. All remaining errors are ours. \import{./}{Appendix.tex}