方差分解与加密货币回报预测

IF 3.9 2区 经济学 Q1 Economics, Econometrics and Finance
Suzanne S. Lee, Minho Wang
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引用次数: 0

摘要

本文利用盘中数据研究了已实现方差如何预测加密货币的横截面回报。我们发现,方差越大的加密货币在随后几周的回报率越低。将总方差分解为带符号的跳跃方差和跳跃-稳健方差后发现,负预测性可归因于正跳跃方差和跳跃-稳健方差。对于价格较低、流动性较差、散户交易活动较多、情绪较为积极的小型加密货币来说,负面定价效应更为明显。我们的研究结果表明,加密货币市场是独特的,因为散户投资者和对类似彩票的回报的偏好在部分方差效应中发挥了重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variance Decomposition and Cryptocurrency Return Prediction

This article examines how realized variances predict cryptocurrency returns in the cross section using intraday data. We find that cryptocurrencies with higher variances exhibit lower returns in subsequent weeks. Decomposing total variances into signed jump and jump-robust variances reveals that the negative predictability is attributable to positive jump and jump-robust variances. The negative pricing effect is more pronounced for smaller cryptocurrencies with lower prices, less liquidity, more retail trading activities, and more positive sentiment. Our results suggest that cryptocurrency markets are unique because retail investors and preferences for lottery-like payoffs play important roles in the partial variance effects.

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来源期刊
CiteScore
6.60
自引率
5.10%
发文量
131
期刊介绍: The Journal of Financial and Quantitative Analysis (JFQA) publishes theoretical and empirical research in financial economics. Topics include corporate finance, investments, capital and security markets, and quantitative methods of particular relevance to financial researchers. With a circulation of 3000 libraries, firms, and individuals in 70 nations, the JFQA serves an international community of sophisticated finance scholars—academics and practitioners alike. The JFQA prints less than 10% of the more than 600 unsolicited manuscripts submitted annually. An intensive blind review process and exacting editorial standards contribute to the JFQA’s reputation as a top finance journal.
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