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Markets Design, Bidding Rules, and Long Memory in Electricity Prices

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  • Sandro Sapio

Abstract

[fre] Pour les enchères sous plis scellés et prix uniformes sur les marchés day ahead de l'électricité, le prix de marché est déterminé à l'intersection des fonctions d'offre et de demande agrégées construites par un opérateur de marché. Chaque jour, un seul agent (le générateur marginal) détient l'usine qui permet de solder le marché. De plus, les enchères day ahead sont intégrées dans des systèmes multi segments, dans lesquels divers protocoles coexistent et changent dans le temps. Cet environnement complexe conduit à l'adoption de règles d'enchères simples et adaptatives. Plus précisément, une telle configuration de marché autorise l'émergence de deux types différents de routines, selon que l'agent est un générateur marginal ou infra marginal. Cependant, du fait du mécanisme de prix uniforme, seul le comportement d'enchère du premier peut être reflété dans les prix de marchés. En fonction de la manière spécifique dont les générateurs marginaux traitent l'information passée pour déterminer leurs enchères (« hyperbolique » ou « exponentielle ») les prix de l'électricité sont susceptibles d'exhiber des effets de mémoire longue ou courte. En utilisant une analogie avec l'actualisation hyperbolique (un biais de comportement humain robuste) une conception en mémoire longue du prix de l'électricité est proposée. Cette intuition est confirmée par une analyse spectrale des données journalières issues des marchés NordPool et CalPX, en opposition forte avec de nombreuses études empiriques précédentes. Cet article souligne l'importance des accords institutionnels pour déterminer les relations entre les comportements individuels et les résultats du marché. Il propose une cartographie intéressante des règles d'enchères et des modèles de traitement de l'information selon les propriétés des séries temporelles des prix de marché. [eng] In uniform price, sealed-bid, day-ahead electricity auctions, the market price is set at the intersection between aggregate demand and supply functions constructed by a market operator. Each day, just one agent - the marginal generator - owns the market-clearing plant. Moreover, day-ahead auctions are embedded in multi-segment systems, wherein diverse protocols coexist and change over time. This complex environment leads to adoption of simple, adaptive bidding rules. Specifically, such a market design enables the emergence of two different types of routines, depending on whether the agent is a likely marginal or inframarginal generator. However, because of the uniform price mechanism, only the bidding behavior of the former can be reflected into market prices. Depending on the specific way marginal generators process past information to set their bids - « hyperbolic » or « exponential » - electricity prices are likely to display long- or short-memory. Using an analogy with the hyperbolic discounting - a quite robust behavioral bias in humans - a long-memory view of electricity prices is proposed. This insight is confirmed by spectral analysis of daily data from NordPool and CaIPX markets, in sharp contrast with most previous empirical studies. This paper underlines the importance of institutional settings in determining the relationship between individual behavior and market outcomes, and proposes an interesting mapping of bidding rules and models of information processing into the time series properties of market prices.

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Bibliographic Info

Article provided by Programme National Persée in its journal Revue d'économie industrielle.

Volume (Year): 107 (2004)
Issue (Month): 1 ()
Pages: 151-170

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Handle: RePEc:prs:recind:rei_0154-3229_2004_num_107_1_3053

Note: DOI:10.3406/rei.2004.3053
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Web page: http://www.persee.fr/web/revues/home/prescript/revue/rei

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Cited by:
  1. Bottazzi, G. & Sapio, S. & Secchi, A., 2005. "Some statistical investigations on the nature and dynamics of electricity prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 54-61.
  2. Sandro Sapio, 2012. "Modeling the distribution of day-ahead electricity returns: a comparison," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1935-1949, December.

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