We integrate a high-frequency monetary event study into a mixed-frequency macro-finance model and structural estimation. The model and estimation allow for jumps at Fed announcements in investor beliefs, providing granular detail on why markets react to central bank communications. We find that...

Systematic risk factors can be used to create portfolios free of idiosyncratic risks but are not usually orthogonal to each other. Are correlations between pairs of factors predictable? Does accounting for the dependence structure among systematic factors improve traditional portfolio...

It is common to estimate equity betas for private firms or non-traded assets through a comparable company analysis, we test if the Random Forest algorithm can provide superior forecasts. In out-of-sample tests from 1992 to 2018, we find that Random Forest forecasts produce substantially lower...

This paper investigates the predictability of market betas for crypto assets. The market beta is the optimal weight of a short position in a simple two-asset portfolio hedging the market risk. Investors are therefore keen to forecast the market beta accurately. Estimating the market beta is a...

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,...

In this paper, we provide techniques for combining different experts' opinions of the forward looking equity premium to resolve questions about the future value of an equity index tracker fund. By exploiting the fact that the survey data is approximately gamma distributed, we either use...