动量信息驱动的通胀风险
Momentum Informed Inflation-at-Risk
AI总结：
本文填补了通胀风险研究的空白，提出通胀的最佳特征由分位数变化和以通胀动量为条件的两类非线性组合来刻画，从而为政策制定者提供通胀风险估计。
摘要：
经济增长风险近期已成为衡量宏观经济尾部风险的关键指标，并被广泛研究。令人惊讶的是，通胀风险却并非如此，其双尾（通缩与高通胀）均为政策制定者所重点关注，但相关研究却相对较少。本文旨在填补这一空白，提供通胀风险的估计。本文的核心洞见是，通胀的最佳特征由两类非线性的组合来刻画：分位数变化，以及以通胀动量为条件。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
