The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas
研究发现，使用高频数据计算的条件贝塔能更好地解释规模、价值和动量等资产定价异常，且对未来贝塔的预测比基于日度数据更准确。
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. This paper was accepted by Karl Diether, finance.