Modelling price formation in electricity markets is a notoriously difficult process, due to physical constraints on electricity generation and flow. This difficulty has inspired the recent development of bottom-up agent-based models of electricity markets. While these have proven quite...

This study extends previous work applying unsupervised machine learning to commodity markets. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity...

The Great Recession has been characterised by the two stylized facts: the buildup of leverage in the household sector in the period preceding the recession and a protracted economic recovery that followed. We attempt to explain these two facts as an information friction, whereby agents are...

With the increasing integration of wind and photovoltaic power in the whole European power system, there is a longing for detecting how to trade energy in the ever-changing intraday market from electric power industries. The intraday trading becomes even more relevant in the wake of the European...

After analyzing the characteristics and pricing models on the Russian wholesale electricity market, some important features for econometric modeling are introduced. This paper suggests econometric forecasting models developed to predict daily and hourly electricity prices on the day-ahead market...