Detecting Common Bubbles in Multivariate Mixed Causal-noncausal Models
Cubadda GianlucaHecq AlainVoisin Elisa
CEIS Research Paper
This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal-noncausal vector autoregressive models, we suggest statistical tools to detect the common locally explosive dynamics in a Student-t distribution maximum likelihood framework. The performances of both likelihood ratio tests and information criteria are investigated in a Monte Carlo study. Finally, we evaluate the practical value of our approach by an empirical application on three commodity prices.
Keywords: Forward-looking models, bubbles, co-movements
JEL codes: C32
Date: Monday, February 27, 2023
Revision Date: Monday, February 27, 2023