Details
| Original language | English |
|---|---|
| Pages (from-to) | 2474-2494 |
| Number of pages | 21 |
| Journal | Management Science |
| Volume | 66 |
| Issue number | 6 |
| Publication status | Published - 22 Jan 2020 |
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.
Keywords
- Beta estimation, Conditional CAPM, High-frequency data
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Management Science and Operations Research
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Management Science, Vol. 66, No. 6, 22.01.2020, p. 2474-2494.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - The conditional capital asset pricing model revisited
T2 - evidence from high-frequency betas
AU - Hollstein, Fabian
AU - Prokopczuk, Marcel
AU - Wese Simen, Chardin
N1 - Funding information: The authors thank Karl Diether (the editor), an anonymous associate editor, two anonymous referees, Alexandros Kontonikas, Binh Nguyen, and Björn Tharann; as well as seminar participants at the Financial Econometrics and Empirical Asset Pricing Conference in Lancaster, the World Finance Conference, Macquarie University Sydney, and Leibniz University Hannover, for providing valuable comments.
PY - 2020/1/22
Y1 - 2020/1/22
N2 - 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.
AB - 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.
KW - Beta estimation
KW - Conditional CAPM
KW - High-frequency data
UR - http://www.scopus.com/inward/record.url?scp=85089256613&partnerID=8YFLogxK
U2 - 10.1287/mnsc.2019.3317
DO - 10.1287/mnsc.2019.3317
M3 - Article
VL - 66
SP - 2474
EP - 2494
JO - Management Science
JF - Management Science
SN - 0025-1909
IS - 6
ER -