This paper evaluates the predictive performance of machine learning techniques in estimating time-varying betas of US stocks. Compared to established estimators, tree-based models and neural networks outperform from both a statistical and an economic perspective. Random forests perform the best...

We comprehensively investigate the robustness of well-known factor models to altered factor-formation breakpoints. Deviating from the standard 30th and 70th percentile selection, we use an extensive set of anomaly test portfolios to uncover two main findings: First, there is a trade-off between...

We confront prominent asset pricing models with the classical out-of-sample cross-sectional test of Fama and MacBeth (1973). For all models, we uncover three main findings: (i) the intercept coefficients are economically large and highly statistically significant; (ii) the cross-sectional factor...

The objective of this study is to analyse the theoretical pricing of wind power derivatives, which is important for renewable energy risk management but has a problem in the pricing due to the illiquidity of the assets and to show the application of the theory to the practical implementation of...

In this study, we investigate the determinants of convenience yields across a broad range of commodities. We find that the convenience yields of commodities are exposed to both commodity-specific and systematic factors, but to a different extent. The difference in explanatory power of these...

The liberalization of electricity markets has forced the energy producing companies to react to the new situation. The abolishment of monopolies and the launch of open markets have increased the need of calculating costs closer to the profit frontier to be still competitive, not only against the...