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Realized volatility and price spikes in electricity markets: The importance of observation frequency

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  • Ullrich, Carl J.

Abstract

This paper uses high frequency spot price data from eight wholesale electricity markets in Australia, Canada, and the United States to estimate realized volatility and the frequency of price spikes. I find similar levels of realized volatility in Australia and North America, with estimates ranging from 1500% to 2700%, much greater than estimates reported previously in the literature. In hourly data, the frequency of price spikes ranges from approximately 35% to 40% in seven of eight markets. I present evidence that increasing the lag length in the calculation of bipower variation improves jump detection in electricity prices.

Suggested Citation

  • Ullrich, Carl J., 2012. "Realized volatility and price spikes in electricity markets: The importance of observation frequency," Energy Economics, Elsevier, vol. 34(6), pages 1809-1818.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:6:p:1809-1818 DOI: 10.1016/j.eneco.2012.07.003
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    References listed on IDEAS

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    Cited by:

    1. Ciarreta, Aitor & Zarraga, Ainhoa, 2016. "Modeling realized volatility on the Spanish intra-day electricity market," Energy Economics, Elsevier, pages 152-163.
    2. Werner, Dan, 2014. "Electricity Market Price Volatility: The Importance of Ramping Costs," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169619, Agricultural and Applied Economics Association.
    3. Kyritsis, Evangelos & Andersson, Jonas & Serletis, Apostolos, 2017. "Electricity prices, large-scale renewable integration, and policy implications," Energy Policy, Elsevier, vol. 101(C), pages 550-560.
    4. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, pages 492-502.
    5. Qu, Hui & Chen, Wei & Niu, Mengyi & Li, Xindan, 2016. "Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models," Energy Economics, Elsevier, pages 68-76.
    6. Mario Domingues de Paula Simões & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo Pinto & Leonardo Lima Gomes, 2016. "Electricity prices forecast analysis using the extreme value theory," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 5(1), pages 1-22.
    7. Huang, Daisy J. & Leung, Charles Ka Yui & Tse, Chung-Yi, 2017. "What account for the differences in rent-price ratio and turnover rate? A search-and-matching approach," MPRA Paper 76864, University Library of Munich, Germany.
    8. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    9. Haugom, Erik & Ullrich, Carl J., 2012. "Forecasting spot price volatility using the short-term forward curve," Energy Economics, Elsevier, pages 1826-1833.
    10. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.

    More about this item

    Keywords

    Realized volatility; Bipower variation; Observation frequency; Electricity markets; Price spikes;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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