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- W3121570442 abstract "This paper investigates oil price series using mixed causal-noncausal autoregressive (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 speculative bubbles. We estimate the probabilities of crash in oil prices. To do so we first evaluate how to adequately detrend nonstationary oil price series while preserving the bubble patterns observed in the raw data. The impact of different filters on the identification of MAR models as well as on forecasting bubble events is investigated using Monte Carlo simulations. We illustrate our findings on WTI and Brent monthly series before and during the outbreak of the Covid-19 pandemic." @default.
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- W3121570442 date "2019-01-01" @default.
- W3121570442 modified "2023-09-27" @default.
- W3121570442 title "Predicting bubble bursts in oil prices during the COVID-19 pandemic with mixed causal-noncausal models" @default.
- W3121570442 hasPublicationYear "2019" @default.
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