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. This paper was accepted by Karl Diether, finance.

Estimating beta: Forecast adjustments and the impact of stock characteristics for a broad cross-section

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Salmon futures and the Fish Pool market in the context of the CAPM and a three-factor model

“…The CAPM is a relatively simple equilibrium model and has been around for a long time but is still considered as one of the benchmark valuation models. Hollstein et al (2020) show that it is in no way outdated. Application of the CAPM to commodity futures requires some modification.…”

Predicting Systematic Risk With Macroeconomic and Financial Variables

“…For example, studies such as Andersen et al (2006), Ghysels and Jacquier (2006), Hooper, Reeves, and Ng (2008), Papageorgiou, Reeves, and Xie (2016), Hollstein, Prokopczuk, and Simen (2019), and Cenesizoglu et al (2019) show that one can obtain better beta estimates and/or forecasts using daily data. Other studies such as Bollerslev, Patton, and Quaedvlieg (2016) and Hollstein, Prokopczuk, and Simen (2020) show that one can improve on these estimates/ forecasts using even higher frequency intraday data. Another strand of this large literature (e.g., Gonzalez, Nave, and Rubio 2012;Cenesizoglu, Liu, and Reeves 2016;Cenesizoglu and Reeves 2018) shows that one can use both high-and low-frequency data jointly to estimate and forecast betas.…”