In this study, we develop a framework, based on a global vector autoregression (GVAR) model, to unite two perspectives on commodity markets, the commodity-specific, single-market-centered approach, investigating the micro- and macroeconomic drivers of commodity prices, and the market perspective, which observes joint movements of commodity prices on exchanges. Thereby, the GVAR model disentangles single market from inter-market effects, while simultaneously accounting for the impact of macroeconomic factors. We apply the framework to the six industrial metals markets, reflecting their interdependencies via their co-production, co-consumption, or co-trading relation. In particular, the numerous significant spillover effects in the cross-commodity dimension underline the importance of jointly modeling commodity markets. While the strong co-movement between industrial metal prices is represented exceptionally well by our framework, the microeconomic supply and demand attributes of the commodities have significant impact, within and across markets, even on price variables, highlighting their relevance in modern commodity market models. Moreover, we detect global shocks, e.g., an increase in global demand, affect each commodity market to a similar extent.

A sound statistical model for recovery rates is required for various applications in quantitative risk management, with the computation of capital requirements for loan portfolios as one important example. We compare different models for predicting the recovery rate on borrower level including linear and quantile regressions, decision trees, neural networks, and mixture regression models. We fit and apply these models on the worldwide largest loss and recovery data set for commercial loans provided by GCD, where we focus on small- and medium-sized entities in the US. Additionally, we include macroeconomic information via a predictive Crisis Indicator or Crisis Probability indicating whether economic downturn scenarios are expected within the time of resolution. The horserace is won by the mixture regression model which regresses the densities as well as the probabilities that an observation belongs to a certain component.

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques.

In this study, we examine the evolution of commodity markets, focusing on the role of fundamentals and cross-market co-movement in shaping commodity price dynamics over time. To capture these time-varying spillover effects, we apply a Markov-switching global vector autoregressive (MS-GVAR) model to industrial metal markets in the period from January 1995 to December 2020. By distinguishing between calm and volatile regimes, our results reveal significant cross-commodity responses, emphasizing the importance of modeling commodity markets jointly. Spillover effects from supply and demand fundamentals within and across markets further highlight the critical role of fundamental drivers in commodity price formation. While the significance of spillover effects remains consistent across regimes, their magnitude intensifies during volatile periods, indicating heightened spillover risks. Additionally, we demonstrate that our time-varying model provides a superior representation of interdependencies between commodity prices compared to a time-invariant benchmark model, as validated through an out-of-sample forecasting analysis.

Wird die deutsche Energiewende und damit ein Teil der ökologischen Nachhaltigkeit Deutschlands auf Kosten der Bevölkerung in den Rohstoffabbauländern ausgetragen? Zur Klärung dieser Frage erstellt diese Studie ein Bewertungsmodell, mithilfe dessen ressourcenintensive Projekte anhand ihrer sozialen Bedingungen beim Rohstoffabbau klassifiziert und bewertet werden können. Die Anwendung des Modells auf die Ressourcenbedarfe von 8 potentiellen Entwicklungspfaden des deutschen Energiesystems verdeutlicht, dass Technologien wie Batteriespeicher, Solar- und Windkraftanlagen vermehrt die Rohstoffe Kobalt, Indium, Dysprosium und Neodym einsetzen, welche unter sozial schwierigen Bedingungen abgebaut werden.