Momentum Informed Inflation-at-Risk

TL;DR

This paper introduces a novel approach to estimating Inflation-at-Risk by combining nonlinear quantile variation with inflation momentum, addressing a significant research gap in macroeconomic tail-risk analysis.

Contribution

It provides the first comprehensive method to estimate Inflation-at-Risk using inflation momentum and nonlinear quantile analysis, filling a gap in macroeconomic risk measurement.

Findings

Inflation-at-Risk can be effectively estimated using the proposed momentum-informed nonlinear approach.

The method captures both deflation and high inflation tail risks.

Results demonstrate improved risk assessment accuracy over traditional models.

Abstract

Growth-at-Risk has recently become a key measure of macroeconomic tail-risk, which has seen it be researched extensively. Surprisingly, the same cannot be said for Inflation-at-Risk where both tails, deflation and high inflation, are of key concern to policymakers, which has seen comparatively much less research. This paper will tackle this gap and provide estimates for Inflation-at-Risk. The key insight of the paper is that inflation is best characterised by a combination of two types of nonlinearities: quantile variation, and conditioning on the momentum of inflation.