• 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