arXiv subjects

Tomasz Serafin

Publications and source records attributed to Tomasz Serafin.

5 recordsLinked to original sources

arXiv2025

Data-driven Calibration Sample Selection and Forecast Combination in Electricity Price Forecasting: An Application of the ARHNN Method

Calibration sample selection and forecast combination are two simple yet powerful tools used in forecasting. They can be combined with a variety of models to significantly improve prediction accuracy, at the same time offering easy implementation and low computational complexity. While their effectiveness has been repeatedly confirmed in prior scientific literature, the topic is still underexplored in the field of electricity price forecasting. In this research article we apply the Autoregressive Hybrid Nearest Neighbors (ARHNN) method to three long-term time series describing the German, Spanish and New England electricity markets. We show that it outperforms popular literature benchmarks in terms of forecast accuracy by up to 10%. We also propose two simplified variants of the method, granting a vast decrease in computation time with only minor loss of prediction accuracy. Finally, we compare the forecasts' performance in a battery storage system trading case study. We find that using a forecast-driven strategy can achieve up to 80% of theoretical maximum profits while trading, demonstrating business value in practical applications.

stat.AP↗

arXiv2025

Probabilistic intraday electricity price forecasting using generative machine learning

The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generative neural network model to generate probabilistic path forecasts for intraday electricity prices and use them to construct effective trading strategies for Germany's continuous-time intraday market. Our method demonstrates competitive performance in terms of statistical evaluation metrics compared to two state-of-the-art statistical benchmark approaches. To further assess its economic value, we consider a realistic fixed-volume trading scenario and propose various strategies for placing market sell orders based on the path forecasts. Among the different trading strategies, the price paths generated by our generative model lead to higher profit gains than the benchmark methods. Our findings highlight the potential of generative machine learning tools in electricity price forecasting and underscore the importance of economic evaluation.

stat.AP↗

arXiv2024

Ranking probabilistic forecasting models with different loss functions

In this study, we introduced various statistical performance metrics, based on the pinball loss and the empirical coverage, for the ranking of probabilistic forecasting models. We tested the ability of the proposed metrics to determine the top performing forecasting model and investigated the use of which metric corresponds to the highest average per-trade profit in the out-of-sample period. Our findings show that for the considered trading strategy, ranking the forecasting models according to the coverage of quantile forecasts used in the trading hours exhibits a superior economic performance.

econ.EM↗

arXiv2024

Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading

This paper presents a novel hybrid approach for constricting probabilistic forecasts that combines both the Quantile Regression Averaging (QRA) method and the factor-based averaging scheme. The performance of the approach is evaluated on data sets from two European energy markets - the German EPEX SPOT and the Polish Power Exchange (TGE). The results show that the newly proposed method outperforms literature benchmarks in terms of statistical measures: the empirical coverage and the Christoffersen test for conditional coverage. Moreover, in line with recent literature trends, the economic value of forecasts is evaluated based on the trading strategy using probabilistic price predictions to optimize the operation of an energy storage system. The results suggest that apart from the use of statistical measures, there is a need for the economic evaluation of forecasts.

stat.AP↗

arXiv2020

Note on simulation pricing of $π$-options

In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $π$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces the lower and the upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $π$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.

q-fin.CP↗