Economics at your fingertips  

Predicting bubble bursts in oil prices using mixed causal-noncausal models

Alain Hecq () and Elisa Voisin

Papers from

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 that bubbles in oil price series burst once the series enter an explosive phase. 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.

Date: 2019-11
New Economics Papers: this item is included in nep-ene, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Papers from
Bibliographic data for series maintained by arXiv administrators ().

Page updated 2021-01-17
Handle: RePEc:arx:papers:1911.10916