Multivariate probabilistic forecasting of electricity prices with trading applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10491160" target="_blank" >RIV/00216208:11320/25:10491160 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=AzDiI1-yf5" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=AzDiI1-yf5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eneco.2024.108008" target="_blank" >10.1016/j.eneco.2024.108008</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multivariate probabilistic forecasting of electricity prices with trading applications

Popis výsledku v původním jazyce

This study extends recently introduced neural networks approach, based on a regularized distributional multilayer perceptron (DMLP) technique fora multivariate case electricity price forecasting. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. Different from previous studies we incorporate dependence between multiple exchanges (EPEX and Nord Pool). The empirical data application analyzes two auctions in the day-ahead electricity market for the United Kingdom market. Along with statistical evaluation of probabilistic forecasts we develop a flexible bidding strategy based on risk-adjusted investor utility function. The trading application leverages the differences of the two exchanges by having long/short positions in both. Our findings demonstrate while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly. LASSO Quantile Regression is better in terms if statistical evaluation of distributional fit, while DMLP outperforms in terms of Sharpe ratio (by 18%) in the trading application.

Název v anglickém jazyce

Multivariate probabilistic forecasting of electricity prices with trading applications

Popis výsledku anglicky

This study extends recently introduced neural networks approach, based on a regularized distributional multilayer perceptron (DMLP) technique fora multivariate case electricity price forecasting. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. Different from previous studies we incorporate dependence between multiple exchanges (EPEX and Nord Pool). The empirical data application analyzes two auctions in the day-ahead electricity market for the United Kingdom market. Along with statistical evaluation of probabilistic forecasts we develop a flexible bidding strategy based on risk-adjusted investor utility function. The trading application leverages the differences of the two exchanges by having long/short positions in both. Our findings demonstrate while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly. LASSO Quantile Regression is better in terms if statistical evaluation of distributional fit, while DMLP outperforms in terms of Sharpe ratio (by 18%) in the trading application.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50201 - Economic Theory

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Economics

ISSN

0140-9883

e-ISSN

1873-6181

Svazek periodika

141

Číslo periodika v rámci svazku

Jan 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

24

Strana od-do

108008

Kód UT WoS článku

001360612500001

EID výsledku v databázi Scopus

2-s2.0-85209084463