Researchers and practitioners face many choices when estimating an asset's sensitivities toward risk factors, i.e., betas. We study the effect of different data sampling frequencies, forecast adjustments, and model combinations for beta estimation. Using the entire U.S. stock universe and a...

We comprehensively analyze the predictive power of several option implied variables for monthly S & P 500 excess returns and realized variance. The correlation risk premium (CRP) emerges as a strong predictor of both excess returns and realized variance. This is true both in- and out-of-sample....

We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is...

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...

Using more than 140 years of data, we comprehensively analyze the predictive power of a broad set of macroeconomic variables for risk and return in commodity spot markets. We find that industrial production growth and inflation are the strongest predictors for future commodity excess returns....

We examine the pricing of tail risk in international stock markets. We find that the tail risk of different countries is highly integrated. Introducing a new World Fear index, we find that local and global aggregate market returns are mainly driven by global tail risk rather than local tail...