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Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology

Author

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  • Kaijian He

    (School of Business, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Hongqian Wang

    (Payment and Settlement Department, Software Center, Bank of China, Beijing 100094, China)

  • Jiangze Du

    (School of Finance, Jiangxi University of Finance and Economics, Nanchang 330013, China)

  • Yingchao Zou

    (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
    School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

Abstract

The electricity market has experienced an increasing level of deregulation and reform over the years. There is an increasing level of electricity price fluctuation, uncertainty, and risk exposure in the marketplace. Traditional risk measurement models based on the homogeneous and efficient market assumption no longer suffice, facing the increasing level of accuracy and reliability requirements. In this paper, we propose a new Empirical Mode Decomposition (EMD)-based Value at Risk (VaR) model to estimate the downside risk measure in the electricity market. The proposed model investigates and models the inherent multiscale market risk structure. The EMD model is introduced to decompose the electricity time series into several Intrinsic Mode Functions (IMF) with distinct multiscale characteristics. The Exponential Weighted Moving Average (EWMA) model is used to model the individual risk factors across different scales. Experimental results using different models in the Australian electricity markets show that EMD-EWMA models based on Student’s t distribution achieves the best performance, and outperforms the benchmark EWMA model significantly in terms of model reliability and predictive accuracy.

Suggested Citation

  • Kaijian He & Hongqian Wang & Jiangze Du & Yingchao Zou, 2016. "Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology," Energies, MDPI, vol. 9(11), pages 1-11, November.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:931-:d:82489
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    References listed on IDEAS

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    Cited by:

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    2. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2017. "Recent Advances in Energy Time Series Forecasting," Energies, MDPI, vol. 10(6), pages 1-3, June.
    3. Sachin Kahawala & Daswin De Silva & Seppo Sierla & Damminda Alahakoon & Rashmika Nawaratne & Evgeny Osipov & Andrew Jennings & Valeriy Vyatkin, 2021. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing," Energies, MDPI, vol. 14(14), pages 1-20, July.

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