This paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The dynamics of hourly electricity prices does not behave as a time series process. Instead, these prices should be treated as a panel in which the prices of 24 cross-sectional hours vary from day to day. This paper introduces a panel model for hourly electricity prices in day-ahead markets and examines their characteristics. The results show that hourly electricity prices exhibit hourly specific mean-reversion and that they oscillate around an hourly specific mean price level.Furthermore, a block structured cross-sectional correlation pattern between the hours is apparent. AbstractThis paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The dynamics of hourly electricity prices does not behave as a time series process. Instead, these prices should be treated as a panel in which the prices of 24 cross-sectional hours vary from day to day. This paper introduces a panel model for hourly electricity prices in day-ahead markets and examines their characteristics. The results show that hourly electricity prices exhibit hourly specific mean-reversion and that they oscillate around an hourly specific mean price level. Furthermore, a block structured cross-sectional correlation pattern between the hours is apparent.
This paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The dynamics of hourly electricity prices does not behave as a time series process. Instead, these prices should be treated as a panel in which the prices of 24 cross-sectional hours vary from day to day. This paper introduces a panel model for hourly electricity prices in day-ahead markets and examines their characteristics. The results show that hourly electricity prices exhibit hourly specific mean-reversion and that they oscillate around an hourly specific mean price level.Furthermore, a block structured cross-sectional correlation pattern between the hours is apparent. AbstractThis paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The dynamics of hourly electricity prices does not behave as a time series process. Instead, these prices should be treated as a panel in which the prices of 24 cross-sectional hours vary from day to day. This paper introduces a panel model for hourly electricity prices in day-ahead markets and examines their characteristics. The results show that hourly electricity prices exhibit hourly specific mean-reversion and that they oscillate around an hourly specific mean price level. Furthermore, a block structured cross-sectional correlation pattern between the hours is apparent.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.