We find out-of-sample predictability of commodity futures excess returns using forecast combinations of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean-variance investor....

We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty...

We document a substantial increase in downside risk to US economic growth over the last 30 years. By modelling secular trends and cyclical changes of the predictive density of GDP growth, we find an accelerating decline in the skewness of the conditional distributions, with significant,...

In this paper, I investigate which commodity disturbances are significant in regards to a country's business cycle. I develop a Small Open New Keynesian model that is estimated for twenty-three countries using nineteen primary commodities within the energy, metal, timber, rubber, cotton and food...

This paper examines the performance of a naïve equally weighted buy-and-hold portfolio and optimization-based commodity futures portfolios for various lookback and holding periods using data from January 1986 to December 2018. The application of Monte Carlo simulation-based mean-variance and...

Many electricity markets exhibit an oligopolistic structure with market participants whose individual trading activities may shift prices essentially. In this context, the question of how to optimally liquidate an existing electricity futures portfolio over a fixed time horizon under the...

We examine whether sensitivities to cash flow (CF) and discount rate (DR) risk in down markets provide an explanation for the investment effect, where low-investment stocks earn higher expected returns than high-investment stocks. We show how productivity and financing constraints asymmetrically...