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Efficiency of the California electricity reserves market

Author

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  • Konstantinos Metaxoglou

    (Department of Economics, University of California Davis, California, USA)

  • Aaron Smith

    (Department of Agricultural and Resource Economics, University of California, Davis, California, USA)

Abstract

We test the efficiency of the California electricity reserves market by examining systematic differences between its day- and hour-ahead prices. We uncover significant day-ahead premia, which we attribute to market design characteristics. On the demand side, the market design established a principal-agent relationship between the markets' buyers (principal) and their supervisory authority (agent). The agent had very limited incentives to shift reserve purchases to the lower priced hour-ahead markets. On the supply side, the market design raised substantial entry barriers by precluding purely speculative trading and by introducing a complicated code of conduct that induced uncertainty about which actions were subject to regulatory scrutiny. We use a high-dimensional vector moving average model to estimate the premia and conduct correct inferences. To obtain exact maximum likelihood estimates of the model, we develop a new EM algorithm that seamlessly incorporates missing data and applies directly to general moving average time series models. Our algorithm uses only analytical expressions: the Kalman filter and a fixed interval smoother in the E step and least squares-type regressions in the M step. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Konstantinos Metaxoglou & Aaron Smith, 2007. "Efficiency of the California electricity reserves market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1127-1144.
    • Handle: RePEc:jae:japmet:v:22:y:2007:i:6:p:1127-1144
    DOI: 10.1002/jae.982
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    References listed on IDEAS

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    3. Chao, Hung-Po & Wilson, Robert, 2002. "Multi-dimensional Procurement Auctions for Power Reserves: Robust Incentive-Compatible Scoring and Settlement Rules," Journal of Regulatory Economics, Springer, vol. 22(2), pages 161-183, September.
    4. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
    5. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    6. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
    7. Peiris, M. Shelton, 1988. "On the study of some functions of multivariate ARMA processes," Journal of Multivariate Analysis, Elsevier, vol. 25(1), pages 146-151, April.
    8. Bushnell, James & Wolfram, Catherine, 2008. "Electricity Markets," Staff General Research Papers Archive 31547, Iowa State University, Department of Economics.
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    Cited by:

    1. 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.

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