Paper Info
Title
The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas
Abstract
When using high-frequency data, the conditional capital asset pricing model (CAPM) can explain asset-pricing anomalies. Using conditional betas based on daily data, the model works reasonably well for a recent sample period. However, it fails to explain the size anomaly as well as three out of six of the anomaly component excess returns. Using high-frequency betas, the conditional CAPM is able to explain the size, value, and momentum anomalies. We further show that high-frequency betas provide more accurate predictions of future betas than those based on daily data. This result holds for both the time-series and the cross-sectional dimensions.
Year
DOI
Venue
2020
10.1287/mnsc.2019.3317
MANAGEMENT SCIENCE
Keywords
DocType
Volume
beta estimation,conditional CAPM,high-frequency data
Journal
66
Issue
ISSN
Citations 
6
0025-1909
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Fabian Hollstein100.34
Marcel Prokopczuk200.34
Chardin Wese Simen300.34