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Multivariate constrained robust M‐regression for shaping forward curves in electricity markets

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  • Peter Leoni
  • Pieter Segaert
  • Sven Serneels
  • Tim Verdonck

Abstract

In this paper, a multivariate constrained robust M‐regression method is developed to estimate shaping coefficients for electricity forward prices. An important benefit of the new method is that model arbitrage can be ruled out at an elementary level, as all shaping coefficients are treated simultaneously. Moreover, the new method is robust to outliers, such that the provided results are stable and not sensitive to isolated sparks or dips in the market. An efficient algorithm is presented to estimate all shaping coefficients at a low computational cost. To illustrate its good performance, the method is applied to German electricity prices.

Suggested Citation

  • Peter Leoni & Pieter Segaert & Sven Serneels & Tim Verdonck, 2018. "Multivariate constrained robust M‐regression for shaping forward curves in electricity markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(11), pages 1391-1406, November.
    • Handle: RePEc:wly:jfutmk:v:38:y:2018:i:11:p:1391-1406
    DOI: 10.1002/fut.21958
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    References listed on IDEAS

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    1. Paraschiv, Florentina & Bunn, Derek & Westgaard, Sjur, 2016. "Estimation and Application of Fully Parametric Multifactor Quantile Regression with Dynamic Coefficients," Working Papers on Finance 1607, University of St. Gallen, School of Finance.
    2. repec:dau:papers:123456789/607 is not listed on IDEAS
    3. Szymon Borak & Rafał Weron, 2008. "A semiparametric factor model for electricity forward curve dynamics," SFB 649 Discussion Papers SFB649DP2008-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Fleten, Stein-Erik & Lemming, Jacob, 2003. "Constructing forward price curves in electricity markets," Energy Economics, Elsevier, vol. 25(5), pages 409-424, September.
    5. Willems, Gert & Van Aelst, Stefan, 2005. "Fast and robust bootstrap for LTS," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 703-715, April.
    6. Koller, Manuel & Stahel, Werner A., 2011. "Sharpening Wald-type inference in robust regression for small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2504-2515, August.
    7. Steen Koekebakker * & Roar Os Ådland, 2004. "Modelling forward freight rate dynamics—empirical evidence from time charter rates," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(4), pages 319-335, October.
    8. Helyette Geman, 2005. "Commodities and Commodity Derivatives. Modeling and Pricing for Agriculturals, Metals and Energy," Post-Print halshs-00144182, HAL.
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    Cited by:

    1. Filzmoser, P. & Höppner, S. & Ortner, I. & Serneels, S. & Verdonck, T., 2020. "Cellwise robust M regression," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).

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