A stock's exposure to systematic risk factors is surrounded by substantial uncertainty. This beta uncertainty is both economically and statistically significantly priced in the cross-section of stock returns. Stocks with high beta uncertainty substantially under-perform those with low beta...

This paper examines the estimation of global and local betas for a large set of Developed and Emerging international markets. Estimators based on daily data clearly outperform those based on monthly or quarterly data. For global and local market betas, the optimal window length is at roughly 24...

We examine the pricing of tail risk in international stock markets. Studying all MSCI Developed and Emerging Markets countries, we find that the tail risk of these countries is highly integrated. We find that both local and our newly computed global tail risk strongly predict global equity index...

We study the term structure of variance (total risk), systematic, and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little...

Researchers and practitioners face many choices when estimating an asset's sensitivities toward risk factors, i.e., betas. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting...

When using high-frequency data, the conditional 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 3 out of 6 of the anomaly component excess...

We conduct a comprehensive comparison of market beta estimation techniques. We study the performance of several historical, time-series model, and option implied estimators for estimating realized market beta. Thereby, we find the hybrid methodology of Buss and Vilkov (2012) to consistently...

This paper evaluates the predictive performance of machine learning techniques in estimating time-varying betas of US stocks. Compared to established estimators, tree-based models and neural networks outperform from both a statistical and an economic perspective. Random forests perform the best...

We confront prominent asset pricing models with the classical out-of-sample cross-sectional test of Fama and MacBeth (1973). For all models, we uncover three main findings: (i) the intercept coefficients are economically large and highly statistically significant; (ii) the cross-sectional factor...

Researchers and practitioners employ a variety of time-series processes to forecast betas, using either short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: beta factors show consistent long-memory properties. For the vast majority of stocks,...