两次收紧的故事

IF 1.9 3区 经济学 Q2 ECONOMICS
Yundi Lu, Victor J. Valcarcel
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引用次数: 0

摘要

资产负债表政策现已成为货币政策的一个突出方面。根据美国在 2017 年至 2019 年间的经验,显示第一阶段的量化紧缩(QT1)与之前的资产负债表扩张明显不同。本文提供了美联储于2022年1月开始的第二次资产负债表放松(QT2)与QT1显著不同的证据。我们发现,在 QT2 期间,各种国债收益率和利差都出现了实质性的公告效应,而 QT1 期间则基本没有这种效应。在本文撰写之时,即 2023 年 2 月,两种情况下储备余额减少的百分比相似。然而,QT2 实施后的市场反应更为强烈。不仅两个时期的基本金融条件不同,2022 年的货币政策行为似乎也不同。与 QT1 期间相比,QT2 期间货币传导的预期渠道出现了更清晰的信号机制。在 QT1 期间似乎非常重要的流动性效应,在第二轮资产负债表紧缩期间已基本减弱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A tale of two tightenings

Balance sheet policy is now a prominent facet of monetary policy. Based on the U.S. experience between 2017 and 2019, Smith and Valcarcel (2023) show the first period of quantitative tightening (QT1) was markedly different from earlier balance sheet expansions. This paper provides evidence the Federal Reserve's second balance sheet unwind effort that began in January 2022 (QT2) is strikingly different from QT1. We find substantial announcement effects during QT2 for various treasury yields and interest rate spreads, which are largely absent from QT1. At the time of this writing—by February 2023—both episodes have experienced a similar percent reduction in reserve balances. Yet, QT2 shows a stronger market response upon implementation. Not only are the underlying financial conditions different across the two periods, but the conduct of monetary policy in 2022 seems to be different as well. A clearer signaling mechanism for the expectations channel of monetary transmission takes place during QT2 than was apparent during QT1. The liquidity effects that seemed to be so important during QT1 have been largely attenuated during the second episode of balance sheet tightening.

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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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