In this paper, we examine the temporal stability of the evidence for two commodity futures pricing theories. We investigate whether the forecast power of commodity futures can be attributed to the extent to which they exhibit seasonality and we also consider whether there are time varying...

We contribute to the literature by analyzing forecast combination methods in the context of machine learning to predict equity returns. Whilst individual models lack robustness, forecast combinations display stability and are able to produce improved results with Sharpe ratios up to 3.16. We use...

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...

This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong...

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....