#1 Przewidywanie zmienności cen głównych surowców energetycznych: dynamiczny model persystencji 
[Predicting the volatility of major energy commodity prices: the dynamic persistence model]

Jozef Barunik (Institute of Economic Studies, Charles University, Prague, Czechia)


Time variation and persistence are crucial properties of volatility that are often studied 
separately in energy volatility forecasting models. Here, we propose a novel approach that 
allows shocks with heterogeneous persistence to vary smoothly over time, and thus model the 
two together. We argue that this is important because such dynamics arise naturally from the 
dynamic nature of shocks in energy commodities. We identify such dynamics from the data using 
localised regressions and build a model that significantly improves volatility forecasts. 
Such forecasting models, based on a rich persistence structure that varies smoothly over 
time, outperform state-of-the-art benchmark models and are particularly useful for 
forecasting over longer horizons.

Based on: https://ideas.repec.org/p/arx/papers/2402.01354.html


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#2 Uczenie się rozkładów prawdopodobieństwa cen energii elektrycznej na rynku dnia następnego
[Learning probability distributions of day-ahead electricity prices]

Luboš Hanus (Institute of Economic Studies, Charles University, Prague, Czechia)


We propose a novel machine learning approach to probabilistic forecasting of hourly day-ahead 
electricity prices. In contrast to recent advances in data-rich probabilistic forecasting 
that approximate the distributions with some features such as moments, our method is 
non-parametric and selects the best distribution from all possible empirical distributions 
learned from the data. The model we propose is a multiple output neural network with a 
monotonicity adjusting penalty. Such a distributional neural network can learn complex 
patterns in electricity prices from data-rich environments and it outperforms 
state-of-the-art benchmarks.

Based on: https://ideas.repec.org/p/arx/papers/2310.02867.html