Bubble Detection with Application to Green Bubbles: A Noncausal Approach

TL;DR

This paper proposes a novel stationary-process-based method for bubble detection, utilizing mixed causal and noncausal autoregressive models, applied to identify green bubbles in renewable energy markets.

Contribution

It introduces a noncausal autoregressive approach that models bubbles as intrinsic nonlinear components within stationary price processes.

Findings

Developed a bubble indicator based on tail process representation.

Applied the method to detect green bubbles in renewable energy investments.

Provided a new perspective on bubble dynamics as stationary phenomena.

Abstract

This paper introduces a new approach for bubble detection based on mixed causal and noncausal autoregressive processes and their tail process representation during an explosive episode. Departing from traditional definitions of bubbles as nonstationary and temporarily explosive processes, we adopt a perspective in which prices are assumed to follow a strictly stationary process, with the bubble considered an intrinsic component of its nonlinear dynamics. The proposed approach provides a bubble indicator for detecting bubbles and measuring their duration. We implement our strategy to investigate the phenomenon called the "green bubble" in the field of renewable energy investment.