Forecasting bubbles with mixed causal-noncausal autoregressive models

Voisin, Elisa and Hecq, Alain (2019): Forecasting bubbles with mixed causal-noncausal autoregressive models.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Noncausal models, forecasting, predictive densities, bubbles, simulations-based forecasts
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics

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