A Stochastic Latent Moment Model for Electricity Price Formation

• 成果类型: Article
• 署名作者: Gianfreda, Angelica; Bunn, Derek
• 署名单位: University of London; London Business School; Free University of Bozen-Bolzano; University of London; London Business School
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2018.1733
• 发表日期: 2018
• 页码: 1189-1203
• 关键词: regression quantiles; wind generation; t-distribution; spot-market; IMPACT; energy; POWER; options; distributions; valuation

摘要

The wide range of models needed to support the various short-term operations for electricity generation demonstrates the importance of accurate specifications for the uncertainty in market prices. This is becoming increasingly challenging, since hourly price densities for electricity exhibit a variety of shapes, with their characteristic features changing substantially within the day and evolving over time. Furthermore, the influx of renewable power, wind, and solar, in particular, has made these density shapes very weather dependent. We develop a general four-parameter stochastic model for hourly prices, in which the four moments of the density function are dynamically estimated as latent state variables and, furthermore, modelled as functions of several plausible exogenous drivers. This provides a transparent and credible model that is sufficiently flexible to capture the shape-shifting effects, particularly with respect to the wind and solar output variations causing dynamic switches in the upside and downside risks. Extensive testing on German wholesale price data, benchmarked against quantile regression and other models in out-of-sample backtesting, validated the approach and its analytical appeal.