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

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.

Forecasting bubbles with mixed causal-noncausal autoregressive models

“…Although commodities are a competitive market with many buyers and sellers, there is evidence that their dynamics can be explained with noncausal autoregressive models. Hecq and Voisin (2021) found evidence of noncausality in monthly Nickel prices, Gourieroux, Jasiak, and Tong (2021); Hecq and Voisin (2019) in crude oil monthly prices, Karapanagiotidis (2014) in 25 commodity futures price, including soft, precious metals, energy, and livestock sectors and Lof and Nyberg (2017) in the exchange rates of commodity exporters. All series do not share the same trend, but they appear to be affected by similar shocks.…”

“…However, arbitrarily selecting the type of trend or the transformation of the data can affect the dynamics of the series, greatly influencing the noncausal component. Alternatively, Hecq and Voisin (2019) proposes to use the Hodrick-Prescott (HP hereafter) filter with a penalty parameter λ =129.600 for monthly data, indicating through a Monte Carlo study that the method keeps the data dynamics unaltered. Notwithstanding the above, there are criticisms about the dynamics induced by the HP filter and the selection of its penalty parameter; see Hamilton (2018) for details.…”

Detecting Common Bubbles in Multivariate Mixed Causal–Noncausal Models

“…Following the work of Hecq and Voisin (2022), we detrend all series using the Hodrick-Prescott filter (hereafter HP filter). Although this approach to obtain stationary time series has been strongly criticized, in particular for the investigation of business cycles, Hecq and Voisin (2022) show that it is a convenient strategy to preserve the bubble features. They also show in a Monte Carlo simulation that this is the filter that preserves the best identification of the MAR(r, s) model.…”

“…We rely on mixed causal-noncausal models (denoted as MAR(r, s) hereafter), namely autoregressive time series that depend on both r lags and s leads. Indeed, there has been recent interest in the properties of noncausal processes associated with a blooming of applications on commodity prices, inflation or cryptocurrency series, and the developments around the notion of nonfundamental shocks, see, i.a., Hecq and Voisin (2022) and the references therein. We choose to consider mixed causal and noncausal, models as they might also be used for forecasting.…”