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Energies 2016, 9(11), 931; doi:10.3390/en9110931

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

1
School of Business, Hunan University of Science and Technology, Xiangtan 411201, China
2
Payment and Settlement Department, Software Center, Bank of China, Beijing 100094, China
3
School of Finance, Jiangxi University of Finance and Economics, Nanchang 330013, China
4
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
5
School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
*
Author to whom correspondence should be addressed.
Academic Editor: José C. Riquelme
Received: 15 July 2016 / Revised: 7 October 2016 / Accepted: 25 October 2016 / Published: 9 November 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
View Full-Text   |   Download PDF [2761 KB, uploaded 9 November 2016]   |  

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. View Full-Text
Keywords: Empirical Mode Decomposition (EMD); electricity market risk; Value at Risk (VaR); Exponential Weighted Moving Average (EWMA) Empirical Mode Decomposition (EMD); electricity market risk; Value at Risk (VaR); Exponential Weighted Moving Average (EWMA)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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He, K.; Wang, H.; Du, J.; Zou, Y. Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology. Energies 2016, 9, 931.

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