Modeling meaningful volatility events to classify monetary policy announcements

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Research Pub Date : 2025-02-26 DOI:10.1016/j.bdr.2025.100517

Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto

引用次数: 0

Abstract

Central Bank monetary policy interventions frequently have direct implications for financial market volatility. In this paper, we introduce an intradaily Asymmetric Multiplicative Error Model with Meaningful Volatility (MV) events (AMEM-MV), which decomposes realized variance into a base component and an MV component. A novel model-based classification of monetary announcements is developed based on their impact on the MV component of the variance. By focusing on the 30-minute window following each Federal Reserve communication, we isolate the specific impact of monetary announcements on the volatility of seven US tickers.

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建立有意义的波动事件模型，对货币政策公告进行分类

中央银行货币政策干预经常对金融市场波动产生直接影响。本文提出了一种包含有意义波动率（MV）事件的日内非对称乘法误差模型（AMEM-MV），该模型将实际方差分解为基分量和MV分量。基于货币公告对方差的MV分量的影响，开发了一种新的基于模型的货币公告分类。通过关注美联储每次信息发布后的30分钟窗口，我们分离出货币政策公告对7个美国股票市场波动性的具体影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Research Computer Science-Computer Science Applications

CiteScore

8.40

自引率

3.00%

发文量

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