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• W3133923887 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.
• W3133923887 created "2021-03-15" @default.
• W3133923887 creator A5032572394 @default.
• W3133923887 creator A5077547180 @default.
• W3133923887 date "2019-11-25" @default.
• W3133923887 modified "2023-09-25" @default.
• W3133923887 title "Predicting bubble bursts in oil prices during the COVID-19 pandemic with mixed causal-noncausal models" @default.
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• W3133923887 hasPublicationYear "2019" @default.
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