Electricity Price Instability over Time: Time Series Analysis and Forecasting

Diankai Wang, Inna Gryshova, Mykola Kyzym, Tetiana Salashenko, Viktoriia Khaustova and Maryna Shcherbata
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Diankai Wang: School of International Business, Zhejiang International Studies University, 299 Liuhe Rd., Xihu District, Hangzhou 310030, China
Inna Gryshova: Sino-Russian Institute, Jiangsu Normal University, 101 Shanghai Rd., Tangshan District, Xuzhou 221116, China
Mykola Kyzym: Ministry of Education and Science of Ukraine, O. M. Beketov National University of Urban Economy in Kharkiv, 17 Marshal Bazhanov Str., 61002 Kharkiv, Ukraine
Tetiana Salashenko: Research Centre for Industrial Problems of Development, National Academy of Sciences of Ukraine, 1a Inzhenernyi Ln, 61166 Kharkiv, Ukraine
Viktoriia Khaustova: Research Centre for Industrial Problems of Development, National Academy of Sciences of Ukraine, 1a Inzhenernyi Ln, 61166 Kharkiv, Ukraine
Maryna Shcherbata: Sino-Russian Institute, Jiangsu Normal University, 101 Shanghai Rd., Tangshan District, Xuzhou 221116, China

Sustainability, 2022, vol. 14, issue 15, 1-24

Abstract: Competition in electricity markets leads to volatile conditions which cause persistent price fluctuations over time. This study explores the problem of electricity pricing fluctuations in the DE-LU bidding zone from October 2018 to March 2022 by applying time series analysis. The determinants of electricity price fluctuations are broken down into three groups: exogenous prices (gas, coal and CO 2 prices), internal (consumption and generation) and external (net import between neighboring bidding zones) electricity flows. Based on the SARIMAX model, we tried to combine all these factors to forecast electricity prices in the single bidding zone. It was found that the SARIMAX (1, 1, 2) × (3, 1, 0, 7) model with exogenous prices, internal and external electricity flows, which has the lowest AIC and MAPE values, is the best-fitted model for the DE-LU bidding zone. Anonymous trading and unpredictable individual bidding strategies lead to persistent price volatility, which causes electricity prices to deviate from fundamental trends. To reveal the risk factors, the SARIMAX model of electricity prices needs to be supplemented with a GARCH model of the residual returns. For forecasting electricity price residual volatility in the DE-LU bidding zone, the SARIMAX model with exogenous prices, internal and external electricity flows must be accompanied with the GARCH (7, 0) model.

Keywords: electricity market; electricity prices; instability; time series analysis; short-term forecasting; price volatility; SARIMAX model; GARCH model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:15:p:9081-:d:870788

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