Analysis of Forecasting Models in Electricity Market Under Volatility: What We Learn from Sweden

Gazi Uddin, Ou Tang, Maziar Sahamkhadam, Farhad Taghizadeh-Hesary (), Muhammad Yahya, Pontus Cerin and Jakob Rehmea
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Ou Tang: Linköping University
Muhammad Yahya: University of Applied Sciences
Pontus Cerin: Linköping University
Jakob Rehmea: Linköping University

A chapter in Revisiting Electricity Market Reforms, 2022, pp 117-142 from Springer

Abstract: Abstract Understanding short-term electricity price forecasting has received considerable attention in recent years. Despite this increased interest, the literature lacks concrete consensus on the best-suited forecasting approach. This study conducts an extensive empirical analysis to evaluate the short-term price forecasting dynamics of different regions in the Swedish electricity market (SEM). We utilise several forecasting approaches ranging from standard conditional volatility models to wavelet-based forecasting. In addition, we perform out-of-sample forecasting and back-testing, and evaluate the performance of these models. Our empirical analysis indicates that the ARMA-GARCH model with the Student’s t-distribution significantly outperforms other frameworks. Wavelet-based forecasting is only performed based on the mean absolute percent error (MAPE). Our results of the robust forecasting methods can display the importance of proper forecasting process design, policy implications for market efficiency, and predictability in SEM.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-4266-2_5

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DOI: 10.1007/978-981-19-4266-2_5

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