Analysis with high frequency returns has become a core part of modern financial econometrics. Particularly in the measurement and forecasting of variance, covariance, correlation and Capital Asset Pricing Model (CAPM) beta. This paper studies CAPM beta measurement and forecasting with high...

Financial uncertainty and macroeconomic uncertainty are commonly proxied separately by the volatility of stock returns or key macroeconomic variables, respectively. We propose a portfolio-based measure (PBMEU) that aims to capture aggregate uncertainty in both financial markets and the...

This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...

Advances in volatility and beta forecasting are extended to the setting of volatility timing of market neutral portfolios. Key features of the study include short horizon forecasting from models with higher accuracy levels than previously documented in the literature. A trade-off in the joint...