Forecasting bubbles with mixed causal-noncausal autoregressive models

Elisa Voisin and Alain Hecq

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates one-step ahead density forecasts of mixed causal-noncausal models. We compare the sample-based and the simulations-based approaches respectively developed by Gouriéroux and Jasiak (2016) and Lanne, Luoto, and Saikkonen (2012). We focus on explosive episodes and therefore on predicting turning points of bubbles bursts. We suggest the use of both methods to construct investment strategies based on how much probabilities are induced by the assumed model and by past behaviours. We illustrate our analysis on Nickel prices series.

Keywords: Noncausal models; forecasting; predictive densities; bubbles; simulations-based forecasts (search for similar items in EconPapers)
JEL-codes: C22 C53 C58 (search for similar items in EconPapers)
Date: 2019-03-13
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://mpra.ub.uni-muenchen.de/96350/1/MPRA_paper_92734.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/96350/5/MPRA_paper_96350.pdf revised version (application/pdf)

Related works:
Journal Article: Forecasting bubbles with mixed causal-noncausal autoregressive models (2021)
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