DocumentCode :
120218
Title :
EMD Based Value at Risk Estimate Algorithm for Electricity Markets
Author :
Hongqian Wang ; Kaijian He ; Yingchao Zou
Author_Institution :
Sch. of Econ. & Manage., Beijing Univ. of Chem. Technol., Beijing, China
fYear :
2014
fDate :
4-6 July 2014
Firstpage :
445
Lastpage :
449
Abstract :
With the electricity market reform in recent decades, the electricity price volatility brings more uncertainty and greater risks. This paper proposes a novel risk measurement approach Based on the EMD algorithm to estimate Value at Risk (VaR) in the electricity market. The EMD algorithm is used to decompose the time series into several intrinsic mode functions (IMFs) and one residual component. Then the decomposed parts will be calculate with the Exponential Weighted Moving Average (EWMA) model. Empirical studies in the five Australian electricity markets suggest that the proposed algorithm outperforms the benchmark EWMA model, in terms of conventional performance evaluation criteria for the model reliability.
Keywords :
moving average processes; power markets; risk analysis; time series; EMD based value at risk estimate algorithm; EWMA model; VaR estimation; electricity markets; electricity price volatility; exponential weighted moving average model; intrinsic mode functions; risk measurement approach; time series decomposition; Accuracy; Biological system modeling; Electricity; Electricity supply industry; Predictive models; Reactive power; Time series analysis; Empirical Mode Decomposition (EMD) model; Exponential Weighted Moving Average (EWMA) model; Heterogeneous Market Hypothesis (HMH) model; Value at Risk;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Sciences and Optimization (CSO), 2014 Seventh International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-5371-4
Type :
conf
DOI :
10.1109/CSO.2014.91
Filename :
6923722
Link To Document :
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